The 10 Most Experts Leaders in Data & AI Creating Global Impact, 2024

1. Issue: 27 | 2024 THE 10 MOST EXPERTS LEADERS IN CREATING GLOBAL IMPACT, 2024 IN FOCUS Data and AI in the Boardroom: How CEOs are Embracing Digital Transformation IN FOCUS Optimizing AI for health Enquiry: The 5 Key Ingredients IN FOCUS The Next Generation of Data and AI Leaders: Emerging Innovators Head of Responsible AI & AI Strategy NatWest Banking Group Dr.Paul Dongha www.ciobusinessworld.com

4. Editor in chief Robert Patrick Contents Managing Editor : Smith Collins Design Visualizer : Jack Thomas Arts & Design Director : Adam Jones Associate Designer : Erick Williams Sales Senior Sales Manager : Scott M Marketing Manager : Andrew T Sales Executive : Mark Davis Technical Victor Anderson SME- SMO Research Analyst : Henry Martinez SEO Executive : Daniel Lee Circulation Manager Alexander Nelson Follow us : www.ciobusinessworld.com

6. Editorial Note Leading the Future of Data & AI A s we look toward the future, the role of experts and leaders in data and artificial intelligence (AI) becomes even more critical in shaping the trajectory of modern industries. These individuals are more than technologists—they are the visionaries transforming how businesses operate and how society interacts with the vast, complex digital landscape. Their leadership is essential in navigating an ever-evolving technological environment, where data is being produced at unprecedented rates, and AI continues to advance at a breakneck speed. One of the key contributions of these data and AI leaders is their ability to harness the potential of big data. While the volume of data continues to grow exponentially, the real challenge lies in the extraction of meaningful insights from this data. AI has proven indispensable in helping organizations process vast amounts of information

7. efficiently, but it is the human expertise behind these technologies that ensures successful implementation and innovation. Data scientists, AI engineers, and technology leaders are the ones turning theoretical potential into real-world applications, directly impacting everything from business strategies to daily operations. Industries such as healthcare, finance, and logistics have already experienced significant benefits from the integration of AI. For example, in healthcare, AI-driven diagnostic tools and personalized treatment plans have revolutionized patient care. Financial institutions are leveraging AI to enhance fraud detection and improve risk management, while in logistics, AI is optimizing supply chains and forecasting demand more accurately than ever before. The application of AI across such diverse sectors demonstrates the breadth of its potential, and it is the thought leaders and innovators in this space who are driving these breakthroughs. However, as powerful as AI is, there are still challenges and responsibilities that leaders must face. The ethical use of AI has become a focal point of discussion. Issues such as data privacy, algorithmic bias, and the lack of transparency in AI decision-making models continue to raise concerns. Leading figures in data and AI are spearheading efforts to address these challenges, advocating for responsible AI practices that prioritize fairness, accountability, and transparency. They are working not only to build powerful technologies but also to ensure that these innovations are developed and deployed in ways that benefit society as a whole. Collaboration is another critical factor in the ongoing success of AI initiatives. The complexity of AI technology requires interdisciplinary teams, combining expertise from fields like computer science, data analytics, ethics, and domain-specific industries. Leaders in this space foster environments of innovation, encouraging experimentation and creative problem-solving across their teams. Their leadership is central to ensuring that AI solutions are scalable, sustainable, and adaptable to future challenges. Despite the progress made, AI and data-driven technologies are still in their relative infancy. As these technologies evolve, the role of AI experts and leaders will only grow in importance. They are at the forefront of not just technological innovation but also the ethical and strategic considerations that will shape the future of global economies and societies. In conclusion, leaders in data and AI are the driving forces behind today's most transformative technological advancements. Their expertise and vision are redefining the future of business, healthcare, and countless other sectors, while their commitment to responsible innovation ensures that AI can positively impact the world. As featured by CIO Business World, these pioneers continue to push the boundaries of what is possible, laying the groundwork for a future where AI plays an even more integral role in improving lives, driving business growth, and solving global challenges.

10. Contents COVER STORY 14 Dr. Paul Dongha

11. ARTICLE 28 36 42 Data and AI in the Boardroom: How CEOs are Embracing Digital Transformation The Next Generation of Data and AI Leaders: Emerging Innovators Optimizing AI for health Enquiry: The 5 Key Ingredients CXO 24 AI: How engaging with schools is key to the future talent pipeline 32 Artificial Intelligence: Balancing Cybersecurity Risks and Defenses 38 Artificial Intelligence or Human Intelligence? 46 Building Awareness on the Implications of AI Usage in Organizations

14. Head of Responsible AI & AI Strategy NatWest Banking Group Dr.Paul Dongha 02 www.ciobusinessworld.com Building a Responsible AI Assurance Framework Paul argues for a generative AI assurance framework that covers people, process, and technology across an enterprise. On the people side, an ethics board or committee should be established. This committee, composed of senior leaders, would be responsible for making decisions on model deployment considering ethical risks such as bias, discrimination, reputational harm, etc. Training programs on AI ethics and responsible AI should also be implemented for various personas across the organization, including data scientists, non-technical users, legal, risk management, data privacy teams, and executives. I wisdom and technological prowess. His journey began in the 1990s, a time when AI was a fledgling concept, and Paul was among the pioneers, diving deep into the realms of artificial intelligence with a passion that would define his life's work. He spent years teaching and researching AI, motivated by the possibilities it held for the future. navigate the murky waters of ethical dilemmas; human judgment is essential. Key to achieving this is through the creation of an AI ethics board. Paul has co-chaired an organization-wide ethics board, a forum of senior leaders tasked with making critical decisions about the deployment of AI models. This board ensures that every model is scrutinized not just for its technical merits, but for its potential impact on society and individual lives. Paul’s vision and experience is perfectly aligned with the culture and ambitions of NatWest Banking group, who are striving to deliver a more personalised and safer banking experience for its customers and colleagues n the bustling heart of the financial world, a man named Dr Paul Dongha stood as a beacon of ethical On the process side, for organizations with complex business processes, such as banks, ensuring model validation and risk management teams have the right support, technology, and skills is crucial. Additionally, existing data science or DevOps lifecycles should be augmented to include ethical checks, focusing on explainability, bias removal, and fairness during model creation. Paul also understands that knowledge is power. He champions rigorous training, communication, and awareness programs across the organization, ensuring that everyone, from data scientists, risk management and compliance teams to executives, are well-versed in the ethical risks and responsibilities associated with AI. His commitment to fostering a culture of ethical awareness is unwavering, recognizing that a well-informed team is the foundation of responsible AI deployment. Fast forward to the present, and Paul has become a guardian of integrity in the ever- evolving landscape of data and AI ethics. Currently, as Head of Responsible AI and AI Strategy at Natwest Banking Group, and before that as Group Head of Data & AI Ethics at Lloyds Banking group, his mission is clear: to ensure that the powerful tools of AI are used responsibly and ethically, safeguarding both the organizations, customers and society. Finally, on the technology side, building guardrails into the models themselves is necessary. Techniques include debiasing training data and using libraries like Interpret ML, Fairlearn, and AIF360 to detect and address bias throughout the data science lifecycle. Model cards documenting the model's characteristics, assumptions, training data, testing results, and ethical impact assessments are also crucial for responsible AI practices. Lastly, maintaining a model inventory that tracks all models in production, In his heart, Paul knows that AI holds immense potential for good. He envisions a world where AI can bring tailored learning to underserved communities, revolutionize healthcare with groundbreaking discoveries like Alphafold 3, and drive sustainability initiatives that protect our planet. His dedication to these ideals is a testament to his belief in AI's power to transform society for the better. Through his tireless efforts, Dr Paul Dongha has carved a path where technology and ethics walk hand in hand, ensuring that the future of AI is bright, responsible, and profoundly human. Paul's vision for an ethical AI framework is comprehensive and deeply human. He believes that technology alone cannot 03 04 www.ciobusinessworld.com In light of these risks, Paul emphasizes the importance of a robust assurance framework for all organizations using AI. This framework, driven by senior leadership, should establish safeguards to mitigate ethical risks across various industries. He concludes that there's no avoiding these challenges, and responsible AI development necessitates addressing these issues head-on. communication with industry regulators are also valuable resources. Staying ahead of the curve, however, proves more challenging. Rather than attempting to predict future regulations, Paul suggests a proactive approach. Organizations should identify potential risks within their specific AI use cases and proactively mitigate them. This focus on responsible AI practices aligns with ethical obligations and positions them to contribute meaningfully to shaping future regulations based on real-world experience. Building a Responsible AI Future Paul stresses the importance of robust program management when implementing a generative AI assurance framework. Success hinges on several key principles. First, a clearly defined vision and measurable goals ensure everyone is working towards the same objective. Second, strong leadership commitment is crucial to drive collaboration between the various teams involved. Third, AI ethics specialists play a critical role in bridging the gap between technical teams and fostering an understanding of the ethical considerations behind responsible AI. Measuring the Effectiveness of a Generative AI Assurance Framework Paul emphasizes the importance of measurable metrics to assess the effectiveness of a generative AI assurance framework. He proposes a set of key metrics that track various stages of the model development process. Ethical Impact Assessment Completion: This metric monitors the number of models that have successfully completed a mandatory ethical impact assessment. Furthermore, a successful program requires an organizational culture that acknowledges the importance of ethical AI and fosters a sense of shared responsibility. The evolving nature of the AI field necessitates an adaptable framework that can accommodate ongoing learning and experimentation. This dynamic environment, though challenging, also presents exciting opportunities for innovation. Finally, Paul highlights that the problem- solving nature and novelty of building an AI assurance framework can be motivating for engineers, fostering a positive program environment. • Ethical Impact Assessment Success Rate: This metric goes beyond completion rates and focuses on the percentage of models that pass the ethical impact assessment, indicating a successful mitigation of potential ethical risks. • 14 Leading the Way in Ethical AI including risk levels, deployment duration, and retraining needs, is essential for effective oversight. Paul reports encountering a positive reception for his work on ethical AI. The companies he's worked with have expressed genuine interest and curiosity in understanding this concept, despite its perceived mysteriousness. He attributes this openness to the growing awareness of ethical risks in AI and the potential for reputational damage if these risks are not addressed. Consequently, he's been welcomed by development teams, data scientists, and machine learning engineers who are eager to collaborate and ensure responsible AI practices. Ethics Board Review: these metric tracks the number of models presented to the ethics board for additional scrutiny and approval, ensuring a higher level of oversight for potentially sensitive models. • Rise of AI and the Urgency of Ethics Staying Informed and Proactive Paul Dongha describes his personal journey in the field of AI. Paul Dongha addresses the challenge of navigating the ever-evolving regulatory landscape surrounding AI. The global nature of AI development presents a complex situation with various organizations formulating regulations. These include the OECD's principles, the EU's legislation, and individual US states enacting their own laws. Similar trends are observed in Canada and other countries, with industry-specific regulations emerging as well. Model Validation Success Rate: This metric focuses on the technical aspects, tracking the percentage of models that pass the model validation team's assessments, ensuring the models function as intended without technical flaws. • After earning a PhD and working in AI research and education during the 1990s, the lack of career opportunities in AI led him to pursue a successful career in finance. Paul has lead manay enterprise-wide transformation programs, building and designing complex quantitative systems involving Big Data feeds. Urgency of Ethical AI Frameworks Model Inventory Completion: This metric tracks the number of models that have successfully reached the final stage by being added to the organization's risk model inventory. A complete inventory provides a clear picture of all deployed models for ongoing monitoring and risk management. By monitoring these metrics, organizations can gain valuable insights into the effectiveness of their AI assurance framework in identifying and mitigating risks throughout • However, around 2018, he recognized the significant advancements in machine learning and AI being utilized by major companies. This resurgence of AI, particularly the complex and probabilistic nature of generative models, sparked a renewed concern for the ethical risks involved. By 2020, the potential for rapid AI growth solidified his belief in the critical need to address these complex ethical issues. This realization reignited his passion for AI, leading him back to the field in 2019. 05 Paul argues that ethical considerations surrounding AI are universal across industries. While inherent risks like explainability, bias, and robustness apply to all AI models, the severity of consequences varies by sector. High-stakes sectors like healthcare and finance face particularly critical ramifications for model errors. Public services are also concerning due to the potential impact on people's lives. To stay informed, Paul recommends establishing a dedicated horizon scanning team within an organization. This team, consisting of just a few people, would be responsible for monitoring publications from relevant organizations like the OECD and EU. Legal firms specializing in tracking these regulations and 06 www.ciobusinessworld.com www.ciobusinessworld.com the entire AI development lifecycle. These metrics not only assess the health of the framework but also demonstrate a commitment to responsible AI practices. Beyond technical knowledge, Paul emphasizes the importance of empathy for the impact of AI models on users. Mitigating the Unforeseen Most importantly, Paul highlights passion as the most critical quality. A genuine enthusiasm for the field and a desire to leverage AI for positive outcomes are essential for success in this role. Paul recognizes the inherent difficulty of anticipating unforeseen consequences with AI. However, he proposes two key strategies to mitigate these risks. AI for Social Good Firstly, Paul emphasizes the importance of fostering diversity within data science teams. This includes diversity in terms of gender, background, culture, and age. A broader range of perspectives during model development can help identify potential biases or unintended consequences that a homogenous team might overlook. Paul expresses optimism about the positive societal impacts of AI. He highlights several examples: Personalized Customer Interactions: AI enables highly personalized interactions with customers across various sectors, including banking, eLearning, and more. This allows for tailored experiences and targeted information delivery, exceeding previous capabilities. • Secondly, Paul describes a structured process called "consequence scanning." This proactive approach involves a series of steps for teams to identify potential negative behaviors a model might exhibit. By considering "what-if" scenarios and corresponding mitigation strategies, teams can address potential issues before they arise. For example, consequence scanning might involve examining how a model could unintentionally discriminate against a certain group, even if such discrimination wasn't the intended purpose. Additionally, this process includes analyzing the training data for potential biases or a lack of diversity that could lead to skewed outcomes when deployed in different regions. AI on Mobile Devices: AI systems can be delivered on mobile devices, making them accessible even in regions with limited traditional IT infrastructure. This opens doors to applications like personalized learning opportunities in regions with underdeveloped educational systems. • Medical Advancements: AI is playing a significant role in medical breakthroughs. For instance, DeepMind's AlphaFold 3 has the potential to revolutionize disease identification, treatment, and vaccine development, leading to a major boost in drug discovery. Personalized medicine also holds immense potential. • By implementing these two strategies, organizations can take a proactive approach to mitigating unforeseen consequences and fostering more responsible AI development. Environmental Sustainability: AI can be a powerful tool in achieving the UN's Sustainable Development Goals. It can be used to predict pathways to net-zero emissions and optimize energy efficiency. For example, AI models are being used to optimize workload distribution in data centers, leading to significant efficiency improvements. • Beyond Code Paul dispels the myth that data and AI ethics specialists require extensive technical expertise. While a foundational understanding of probabilistic models, common algorithms like gradient descent and XGBoost, and transformer models is beneficial, the core competency lies in ethical considerations. Paul emphasizes that AI ethics plays a crucial role in maximizing these benefits by mitigating potential harms and limitations associated with AI development. He expresses his enthusiasm for the future of AI, particularly its potential to solve problems and contribute meaningfully to society. A strong understanding of how and where bias can creep into AI models is crucial. This includes recognizing bias in raw data, training data labeling, discrepancies between training and testing data sets, and potential mismatches between training data origin and deployment location. 07 08 www.ciobusinessworld.com www.ciobusinessworld.com

15. Building a Responsible AI Assurance Framework Paul argues for a generative AI assurance framework that covers people, process, and technology across an enterprise. On the people side, an ethics board or committee should be established. This committee, composed of senior leaders, would be responsible for making decisions on model deployment considering ethical risks such as bias, discrimination, reputational harm, etc. Training programs on AI ethics and responsible AI should also be implemented for various personas across the organization, including data scientists, non-technical users, legal, risk management, data privacy teams, and executives. I wisdom and technological prowess. His journey began in the 1990s, a time when AI was a fledgling concept, and Paul was among the pioneers, diving deep into the realms of artificial intelligence with a passion that would define his life's work. He spent years teaching and researching AI, motivated by the possibilities it held for the future. navigate the murky waters of ethical dilemmas; human judgment is essential. Key to achieving this is through the creation of an AI ethics board. Paul has co-chaired an organization-wide ethics board, a forum of senior leaders tasked with making critical decisions about the deployment of AI models. This board ensures that every model is scrutinized not just for its technical merits, but for its potential impact on society and individual lives. Paul’s vision and experience is perfectly aligned with the culture and ambitions of NatWest Banking group, who are striving to deliver a more personalised and safer banking experience for its customers and colleagues n the bustling heart of the financial world, a man named Dr Paul Dongha stood as a beacon of ethical On the process side, for organizations with complex business processes, such as banks, ensuring model validation and risk management teams have the right support, technology, and skills is crucial. Additionally, existing data science or DevOps lifecycles should be augmented to include ethical checks, focusing on explainability, bias removal, and fairness during model creation. Paul also understands that knowledge is power. He champions rigorous training, communication, and awareness programs across the organization, ensuring that everyone, from data scientists, risk management and compliance teams to executives, are well-versed in the ethical risks and responsibilities associated with AI. His commitment to fostering a culture of ethical awareness is unwavering, recognizing that a well-informed team is the foundation of responsible AI deployment. Fast forward to the present, and Paul has become a guardian of integrity in the ever- evolving landscape of data and AI ethics. Currently, as Head of Responsible AI and AI Strategy at Natwest Banking Group, and before that as Group Head of Data & AI Ethics at Lloyds Banking group, his mission is clear: to ensure that the powerful tools of AI are used responsibly and ethically, safeguarding both the organizations, customers and society. Finally, on the technology side, building guardrails into the models themselves is necessary. Techniques include debiasing training data and using libraries like Interpret ML, Fairlearn, and AIF360 to detect and address bias throughout the data science lifecycle. Model cards documenting the model's characteristics, assumptions, training data, testing results, and ethical impact assessments are also crucial for responsible AI practices. Lastly, maintaining a model inventory that tracks all models in production, In his heart, Paul knows that AI holds immense potential for good. He envisions a world where AI can bring tailored learning to underserved communities, revolutionize healthcare with groundbreaking discoveries like Alphafold 3, and drive sustainability initiatives that protect our planet. His dedication to these ideals is a testament to his belief in AI's power to transform society for the better. Through his tireless efforts, Dr Paul Dongha has carved a path where technology and ethics walk hand in hand, ensuring that the future of AI is bright, responsible, and profoundly human. Paul's vision for an ethical AI framework is comprehensive and deeply human. He believes that technology alone cannot 03 04 www.ciobusinessworld.com Head of Responsible AI & AI Strategy NatWest Banking Group Dr.Paul Dongha In light of these risks, Paul emphasizes the importance of a robust assurance framework for all organizations using AI. This framework, driven by senior leadership, should establish safeguards to mitigate ethical risks across various industries. He concludes that there's no avoiding these challenges, and responsible AI development necessitates addressing these issues head-on. communication with industry regulators are also valuable resources. Staying ahead of the curve, however, proves more challenging. Rather than attempting to predict future regulations, Paul suggests a proactive approach. Organizations should identify potential risks within their specific AI use cases and proactively mitigate them. This focus on responsible AI practices aligns with ethical obligations and positions them to contribute meaningfully to shaping future regulations based on real-world experience. Building a Responsible AI Future Paul stresses the importance of robust program management when implementing a generative AI assurance framework. Success hinges on several key principles. First, a clearly defined vision and measurable goals ensure everyone is working towards the same objective. Second, strong leadership commitment is crucial to drive collaboration between the various teams involved. Third, AI ethics specialists play a critical role in bridging the gap between technical teams and fostering an understanding of the ethical considerations behind responsible AI. Measuring the Effectiveness of a Generative AI Assurance Framework Paul emphasizes the importance of measurable metrics to assess the effectiveness of a generative AI assurance framework. He proposes a set of key metrics that track various stages of the model development process. Ethical Impact Assessment Completion: This metric monitors the number of models that have successfully completed a mandatory ethical impact assessment. Furthermore, a successful program requires an organizational culture that acknowledges the importance of ethical AI and fosters a sense of shared responsibility. The evolving nature of the AI field necessitates an adaptable framework that can accommodate ongoing learning and experimentation. This dynamic environment, though challenging, also presents exciting opportunities for innovation. Finally, Paul highlights that the problem- solving nature and novelty of building an AI assurance framework can be motivating for engineers, fostering a positive program environment. • Ethical Impact Assessment Success Rate: This metric goes beyond completion rates and focuses on the percentage of models that pass the ethical impact assessment, indicating a successful mitigation of potential ethical risks. • 15 www.ciobusinessworld.com Leading the Way in Ethical AI including risk levels, deployment duration, and retraining needs, is essential for effective oversight. Paul reports encountering a positive reception for his work on ethical AI. The companies he's worked with have expressed genuine interest and curiosity in understanding this concept, despite its perceived mysteriousness. He attributes this openness to the growing awareness of ethical risks in AI and the potential for reputational damage if these risks are not addressed. Consequently, he's been welcomed by development teams, data scientists, and machine learning engineers who are eager to collaborate and ensure responsible AI practices. Ethics Board Review: these metric tracks the number of models presented to the ethics board for additional scrutiny and approval, ensuring a higher level of oversight for potentially sensitive models. • Rise of AI and the Urgency of Ethics Staying Informed and Proactive Paul Dongha describes his personal journey in the field of AI. Paul Dongha addresses the challenge of navigating the ever-evolving regulatory landscape surrounding AI. The global nature of AI development presents a complex situation with various organizations formulating regulations. These include the OECD's principles, the EU's legislation, and individual US states enacting their own laws. Similar trends are observed in Canada and other countries, with industry-specific regulations emerging as well. Model Validation Success Rate: This metric focuses on the technical aspects, tracking the percentage of models that pass the model validation team's assessments, ensuring the models function as intended without technical flaws. • After earning a PhD and working in AI research and education during the 1990s, the lack of career opportunities in AI led him to pursue a successful career in finance. Paul has lead manay enterprise-wide transformation programs, building and designing complex quantitative systems involving Big Data feeds. Urgency of Ethical AI Frameworks Model Inventory Completion: This metric tracks the number of models that have successfully reached the final stage by being added to the organization's risk model inventory. A complete inventory provides a clear picture of all deployed models for ongoing monitoring and risk management. By monitoring these metrics, organizations can gain valuable insights into the effectiveness of their AI assurance framework in identifying and mitigating risks throughout • However, around 2018, he recognized the significant advancements in machine learning and AI being utilized by major companies. This resurgence of AI, particularly the complex and probabilistic nature of generative models, sparked a renewed concern for the ethical risks involved. By 2020, the potential for rapid AI growth solidified his belief in the critical need to address these complex ethical issues. This realization reignited his passion for AI, leading him back to the field in 2019. 05 Paul argues that ethical considerations surrounding AI are universal across industries. While inherent risks like explainability, bias, and robustness apply to all AI models, the severity of consequences varies by sector. High-stakes sectors like healthcare and finance face particularly critical ramifications for model errors. Public services are also concerning due to the potential impact on people's lives. To stay informed, Paul recommends establishing a dedicated horizon scanning team within an organization. This team, consisting of just a few people, would be responsible for monitoring publications from relevant organizations like the OECD and EU. Legal firms specializing in tracking these regulations and 06 www.ciobusinessworld.com www.ciobusinessworld.com the entire AI development lifecycle. These metrics not only assess the health of the framework but also demonstrate a commitment to responsible AI practices. Beyond technical knowledge, Paul emphasizes the importance of empathy for the impact of AI models on users. Mitigating the Unforeseen Most importantly, Paul highlights passion as the most critical quality. A genuine enthusiasm for the field and a desire to leverage AI for positive outcomes are essential for success in this role. Paul recognizes the inherent difficulty of anticipating unforeseen consequences with AI. However, he proposes two key strategies to mitigate these risks. AI for Social Good Firstly, Paul emphasizes the importance of fostering diversity within data science teams. This includes diversity in terms of gender, background, culture, and age. A broader range of perspectives during model development can help identify potential biases or unintended consequences that a homogenous team might overlook. Paul expresses optimism about the positive societal impacts of AI. He highlights several examples: Personalized Customer Interactions: AI enables highly personalized interactions with customers across various sectors, including banking, eLearning, and more. This allows for tailored experiences and targeted information delivery, exceeding previous capabilities. • Secondly, Paul describes a structured process called "consequence scanning." This proactive approach involves a series of steps for teams to identify potential negative behaviors a model might exhibit. By considering "what-if" scenarios and corresponding mitigation strategies, teams can address potential issues before they arise. For example, consequence scanning might involve examining how a model could unintentionally discriminate against a certain group, even if such discrimination wasn't the intended purpose. Additionally, this process includes analyzing the training data for potential biases or a lack of diversity that could lead to skewed outcomes when deployed in different regions. AI on Mobile Devices: AI systems can be delivered on mobile devices, making them accessible even in regions with limited traditional IT infrastructure. This opens doors to applications like personalized learning opportunities in regions with underdeveloped educational systems. • Medical Advancements: AI is playing a significant role in medical breakthroughs. For instance, DeepMind's AlphaFold 3 has the potential to revolutionize disease identification, treatment, and vaccine development, leading to a major boost in drug discovery. Personalized medicine also holds immense potential. • By implementing these two strategies, organizations can take a proactive approach to mitigating unforeseen consequences and fostering more responsible AI development. Environmental Sustainability: AI can be a powerful tool in achieving the UN's Sustainable Development Goals. It can be used to predict pathways to net-zero emissions and optimize energy efficiency. For example, AI models are being used to optimize workload distribution in data centers, leading to significant efficiency improvements. • Beyond Code Paul dispels the myth that data and AI ethics specialists require extensive technical expertise. While a foundational understanding of probabilistic models, common algorithms like gradient descent and XGBoost, and transformer models is beneficial, the core competency lies in ethical considerations. Paul emphasizes that AI ethics plays a crucial role in maximizing these benefits by mitigating potential harms and limitations associated with AI development. He expresses his enthusiasm for the future of AI, particularly its potential to solve problems and contribute meaningfully to society. A strong understanding of how and where bias can creep into AI models is crucial. This includes recognizing bias in raw data, training data labeling, discrepancies between training and testing data sets, and potential mismatches between training data origin and deployment location. 07 08 www.ciobusinessworld.com www.ciobusinessworld.com

16. Building a Responsible AI Assurance Framework Paul argues for a generative AI assurance framework that covers people, process, and technology across an enterprise. On the people side, an ethics board or committee should be established. This committee, composed of senior leaders, would be responsible for making decisions on model deployment considering ethical risks such as bias, discrimination, reputational harm, etc. Training programs on AI ethics and responsible AI should also be implemented for various personas across the organization, including data scientists, non-technical users, legal, risk management, data privacy teams, and executives. navigate the murky waters of ethical dilemmas; human judgment is essential. Key to achieving this is through the creation of an AI ethics board. Paul has co-chaired an organization-wide ethics board, a forum of senior leaders tasked with making critical decisions about the deployment of AI models. This board ensures that every model is scrutinized not just for its technical merits, but for its potential impact on society and individual lives. Paul’s vision and experience is perfectly aligned with the culture and ambitions of NatWest Banking group, who are striving to deliver a more personalised and safer banking experience for its customers and colleagues On the process side, for organizations with complex business processes, such as banks, ensuring model validation and risk management teams have the right support, technology, and skills is crucial. Additionally, existing data science or DevOps lifecycles should be augmented to include ethical checks, focusing on explainability, bias removal, and fairness during model creation. Paul also understands that knowledge is power. He champions rigorous training, communication, and awareness programs across the organization, ensuring that everyone, from data scientists, risk management and compliance teams to executives, are well-versed in the ethical risks and responsibilities associated with AI. His commitment to fostering a culture of ethical awareness is unwavering, recognizing that a well-informed team is the foundation of responsible AI deployment. Finally, on the technology side, building guardrails into the models themselves is necessary. Techniques include debiasing training data and using libraries like Interpret ML, Fairlearn, and AIF360 to detect and address bias throughout the data science lifecycle. Model cards documenting the model's characteristics, assumptions, training data, testing results, and ethical impact assessments are also crucial for responsible AI practices. Lastly, maintaining a model inventory that tracks all models in production, In his heart, Paul knows that AI holds immense potential for good. He envisions a world where AI can bring tailored learning to underserved communities, revolutionize healthcare with groundbreaking discoveries like Alphafold 3, and drive sustainability initiatives that protect our planet. His dedication to these ideals is a testament to his belief in AI's power to transform society for the better. Through his tireless efforts, Dr Paul Dongha has carved a path where technology and ethics walk hand in hand, ensuring that the future of AI is bright, responsible, and profoundly human. 04 www.ciobusinessworld.com I wisdom and technological prowess. His journey began in the 1990s, a time when AI was a fledgling concept, and Paul was among the pioneers, diving deep into the realms of artificial intelligence with a passion that would define his life's work. He spent years teaching and researching AI, motivated by the possibilities it held for the future. n the bustling heart of the financial world, a man named Dr Paul Dongha stood as a beacon of ethical In light of these risks, Paul emphasizes the importance of a robust assurance framework for all organizations using AI. This framework, driven by senior leadership, should establish safeguards to mitigate ethical risks across various industries. He concludes that there's no avoiding these challenges, and responsible AI development necessitates addressing these issues head-on. communication with industry regulators are also valuable resources. Staying ahead of the curve, however, proves more challenging. Rather than attempting to predict future regulations, Paul suggests a proactive approach. Organizations should identify potential risks within their specific AI use cases and proactively mitigate them. This focus on responsible AI practices aligns with ethical obligations and positions them to contribute meaningfully to shaping future regulations based on real-world experience. Building a Responsible AI Future Paul stresses the importance of robust program management when implementing a generative AI assurance framework. Success hinges on several key principles. First, a clearly defined vision and measurable goals ensure everyone is working towards the same objective. Second, strong leadership commitment is crucial to drive collaboration between the various teams involved. Third, AI ethics specialists play a critical role in bridging the gap between technical teams and fostering an understanding of the ethical considerations behind responsible AI. Fast forward to the present, and Paul has become a guardian of integrity in the ever- evolving landscape of data and AI ethics. Currently, as Head of Responsible AI and AI Strategy at Natwest Banking Group, and before that as Group Head of Data & AI Ethics at Lloyds Banking group, his mission is clear: to ensure that the powerful tools of AI are used responsibly and ethically, safeguarding both the organizations, customers and society. Measuring the Effectiveness of a Generative AI Assurance Framework Paul emphasizes the importance of measurable metrics to assess the effectiveness of a generative AI assurance framework. He proposes a set of key metrics that track various stages of the model development process. Ethical Impact Assessment Completion: This metric monitors the number of models that have successfully completed a mandatory ethical impact assessment. Furthermore, a successful program requires an organizational culture that acknowledges the importance of ethical AI and fosters a sense of shared responsibility. The evolving nature of the AI field necessitates an adaptable framework that can accommodate ongoing learning and experimentation. This dynamic environment, though challenging, also presents exciting opportunities for innovation. Finally, Paul highlights that the problem- solving nature and novelty of building an AI assurance framework can be motivating for engineers, fostering a positive program environment. • Paul's vision for an ethical AI framework is comprehensive and deeply human. He believes that technology alone cannot Ethical Impact Assessment Success Rate: This metric goes beyond completion rates and focuses on the percentage of models that pass the ethical impact assessment, indicating a successful mitigation of potential ethical risks. • 16 Leading the Way in Ethical AI including risk levels, deployment duration, and retraining needs, is essential for effective oversight. Paul reports encountering a positive reception for his work on ethical AI. The companies he's worked with have expressed genuine interest and curiosity in understanding this concept, despite its perceived mysteriousness. He attributes this openness to the growing awareness of ethical risks in AI and the potential for reputational damage if these risks are not addressed. Consequently, he's been welcomed by development teams, data scientists, and machine learning engineers who are eager to collaborate and ensure responsible AI practices. Ethics Board Review: these metric tracks the number of models presented to the ethics board for additional scrutiny and approval, ensuring a higher level of oversight for potentially sensitive models. • Rise of AI and the Urgency of Ethics Staying Informed and Proactive Paul Dongha describes his personal journey in the field of AI. Paul Dongha addresses the challenge of navigating the ever-evolving regulatory landscape surrounding AI. The global nature of AI development presents a complex situation with various organizations formulating regulations. These include the OECD's principles, the EU's legislation, and individual US states enacting their own laws. Similar trends are observed in Canada and other countries, with industry-specific regulations emerging as well. Model Validation Success Rate: This metric focuses on the technical aspects, tracking the percentage of models that pass the model validation team's assessments, ensuring the models function as intended without technical flaws. • After earning a PhD and working in AI research and education during the 1990s, the lack of career opportunities in AI led him to pursue a successful career in finance. Paul has lead manay enterprise-wide transformation programs, building and designing complex quantitative systems involving Big Data feeds. Urgency of Ethical AI Frameworks Model Inventory Completion: This metric tracks the number of models that have successfully reached the final stage by being added to the organization's risk model inventory. A complete inventory provides a clear picture of all deployed models for ongoing monitoring and risk management. By monitoring these metrics, organizations can gain valuable insights into the effectiveness of their AI assurance framework in identifying and mitigating risks throughout • However, around 2018, he recognized the significant advancements in machine learning and AI being utilized by major companies. This resurgence of AI, particularly the complex and probabilistic nature of generative models, sparked a renewed concern for the ethical risks involved. By 2020, the potential for rapid AI growth solidified his belief in the critical need to address these complex ethical issues. This realization reignited his passion for AI, leading him back to the field in 2019. 05 Paul argues that ethical considerations surrounding AI are universal across industries. While inherent risks like explainability, bias, and robustness apply to all AI models, the severity of consequences varies by sector. High-stakes sectors like healthcare and finance face particularly critical ramifications for model errors. Public services are also concerning due to the potential impact on people's lives. To stay informed, Paul recommends establishing a dedicated horizon scanning team within an organization. This team, consisting of just a few people, would be responsible for monitoring publications from relevant organizations like the OECD and EU. Legal firms specializing in tracking these regulations and 06 www.ciobusinessworld.com www.ciobusinessworld.com the entire AI development lifecycle. These metrics not only assess the health of the framework but also demonstrate a commitment to responsible AI practices. Beyond technical knowledge, Paul emphasizes the importance of empathy for the impact of AI models on users. Mitigating the Unforeseen Most importantly, Paul highlights passion as the most critical quality. A genuine enthusiasm for the field and a desire to leverage AI for positive outcomes are essential for success in this role. Paul recognizes the inherent difficulty of anticipating unforeseen consequences with AI. However, he proposes two key strategies to mitigate these risks. AI for Social Good Firstly, Paul emphasizes the importance of fostering diversity within data science teams. This includes diversity in terms of gender, background, culture, and age. A broader range of perspectives during model development can help identify potential biases or unintended consequences that a homogenous team might overlook. Paul expresses optimism about the positive societal impacts of AI. He highlights several examples: Personalized Customer Interactions: AI enables highly personalized interactions with customers across various sectors, including banking, eLearning, and more. This allows for tailored experiences and targeted information delivery, exceeding previous capabilities. • Secondly, Paul describes a structured process called "consequence scanning." This proactive approach involves a series of steps for teams to identify potential negative behaviors a model might exhibit. By considering "what-if" scenarios and corresponding mitigation strategies, teams can address potential issues before they arise. For example, consequence scanning might involve examining how a model could unintentionally discriminate against a certain group, even if such discrimination wasn't the intended purpose. Additionally, this process includes analyzing the training data for potential biases or a lack of diversity that could lead to skewed outcomes when deployed in different regions. AI on Mobile Devices: AI systems can be delivered on mobile devices, making them accessible even in regions with limited traditional IT infrastructure. This opens doors to applications like personalized learning opportunities in regions with underdeveloped educational systems. • Medical Advancements: AI is playing a significant role in medical breakthroughs. For instance, DeepMind's AlphaFold 3 has the potential to revolutionize disease identification, treatment, and vaccine development, leading to a major boost in drug discovery. Personalized medicine also holds immense potential. • By implementing these two strategies, organizations can take a proactive approach to mitigating unforeseen consequences and fostering more responsible AI development. Environmental Sustainability: AI can be a powerful tool in achieving the UN's Sustainable Development Goals. It can be used to predict pathways to net-zero emissions and optimize energy efficiency. For example, AI models are being used to optimize workload distribution in data centers, leading to significant efficiency improvements. • Beyond Code Paul dispels the myth that data and AI ethics specialists require extensive technical expertise. While a foundational understanding of probabilistic models, common algorithms like gradient descent and XGBoost, and transformer models is beneficial, the core competency lies in ethical considerations. Paul emphasizes that AI ethics plays a crucial role in maximizing these benefits by mitigating potential harms and limitations associated with AI development. He expresses his enthusiasm for the future of AI, particularly its potential to solve problems and contribute meaningfully to society. A strong understanding of how and where bias can creep into AI models is crucial. This includes recognizing bias in raw data, training data labeling, discrepancies between training and testing data sets, and potential mismatches between training data origin and deployment location. 07 08 www.ciobusinessworld.com www.ciobusinessworld.com

17. Building a Responsible AI Assurance Framework Paul argues for a generative AI assurance framework that covers people, process, and technology across an enterprise. On the people side, an ethics board or committee should be established. This committee, composed of senior leaders, would be responsible for making decisions on model deployment considering ethical risks such as bias, discrimination, reputational harm, etc. Training programs on AI ethics and responsible AI should also be implemented for various personas across the organization, including data scientists, non-technical users, legal, risk management, data privacy teams, and executives. navigate the murky waters of ethical dilemmas; human judgment is essential. Key to achieving this is through the creation of an AI ethics board. Paul has co-chaired an organization-wide ethics board, a forum of senior leaders tasked with making critical decisions about the deployment of AI models. This board ensures that every model is scrutinized not just for its technical merits, but for its potential impact on society and individual lives. Paul’s vision and experience is perfectly aligned with the culture and ambitions of NatWest Banking group, who are striving to deliver a more personalised and safer banking experience for its customers and colleagues On the process side, for organizations with complex business processes, such as banks, ensuring model validation and risk management teams have the right support, technology, and skills is crucial. Additionally, existing data science or DevOps lifecycles should be augmented to include ethical checks, focusing on explainability, bias removal, and fairness during model creation. In light of these risks, Paul emphasizes the importance of a robust assurance framework for all organizations using AI. This framework, driven by senior leadership, should establish safeguards to mitigate ethical risks across various industries. He concludes that there's no avoiding these challenges, and responsible AI development necessitates addressing these issues head-on. communication with industry regulators are also valuable resources. Staying ahead of the curve, however, proves more challenging. Rather than attempting to predict future regulations, Paul suggests a proactive approach. Organizations should identify potential risks within their specific AI use cases and proactively mitigate them. This focus on responsible AI practices aligns with ethical obligations and positions them to contribute meaningfully to shaping future regulations based on real-world experience. Building a Responsible AI Future Paul also understands that knowledge is power. He champions rigorous training, communication, and awareness programs across the organization, ensuring that everyone, from data scientists, risk management and compliance teams to executives, are well-versed in the ethical risks and responsibilities associated with AI. His commitment to fostering a culture of ethical awareness is unwavering, recognizing that a well-informed team is the foundation of responsible AI deployment. Paul stresses the importance of robust program management when implementing a generative AI assurance framework. Success hinges on several key principles. First, a clearly defined vision and measurable goals ensure everyone is working towards the same objective. Second, strong leadership commitment is crucial to drive collaboration between the various teams involved. Third, AI ethics specialists play a critical role in bridging the gap between technical teams and fostering an understanding of the ethical considerations behind responsible AI. Finally, on the technology side, building guardrails into the models themselves is necessary. Techniques include debiasing training data and using libraries like Interpret ML, Fairlearn, and AIF360 to detect and address bias throughout the data science lifecycle. Model cards documenting the model's characteristics, assumptions, training data, testing results, and ethical impact assessments are also crucial for responsible AI practices. Lastly, maintaining a model inventory that tracks all models in production, Measuring the Effectiveness of a Generative AI Assurance Framework Paul emphasizes the importance of measurable metrics to assess the effectiveness of a generative AI assurance framework. He proposes a set of key metrics that track various stages of the model development process. In his heart, Paul knows that AI holds immense potential for good. He envisions a world where AI can bring tailored learning to underserved communities, revolutionize healthcare with groundbreaking discoveries like Alphafold 3, and drive sustainability initiatives that protect our planet. His dedication to these ideals is a testament to his belief in AI's power to transform society for the better. Ethical Impact Assessment Completion: This metric monitors the number of models that have successfully completed a mandatory ethical impact assessment. Furthermore, a successful program requires an organizational culture that acknowledges the importance of ethical AI and fosters a sense of shared responsibility. The evolving nature of the AI field necessitates an adaptable framework that can accommodate ongoing learning and experimentation. This dynamic environment, though challenging, also presents exciting opportunities for innovation. Finally, Paul highlights that the problem- solving nature and novelty of building an AI assurance framework can be motivating for engineers, fostering a positive program environment. • Through his tireless efforts, Dr Paul Dongha has carved a path where technology and ethics walk hand in hand, ensuring that the future of AI is bright, responsible, and profoundly human. Ethical Impact Assessment Success Rate: This metric goes beyond completion rates and focuses on the percentage of models that pass the ethical impact assessment, indicating a successful mitigation of potential ethical risks. • 17 www.ciobusinessworld.com Leading the Way in Ethical AI including risk levels, deployment duration, and retraining needs, is essential for effective oversight. Paul reports encountering a positive reception for his work on ethical AI. The companies he's worked with have expressed genuine interest and curiosity in understanding this concept, despite its perceived mysteriousness. He attributes this openness to the growing awareness of ethical risks in AI and the potential for reputational damage if these risks are not addressed. Consequently, he's been welcomed by development teams, data scientists, and machine learning engineers who are eager to collaborate and ensure responsible AI practices. Ethics Board Review: these metric tracks the number of models presented to the ethics board for additional scrutiny and approval, ensuring a higher level of oversight for potentially sensitive models. • Rise of AI and the Urgency of Ethics Staying Informed and Proactive Paul Dongha describes his personal journey in the field of AI. Paul Dongha addresses the challenge of navigating the ever-evolving regulatory landscape surrounding AI. The global nature of AI development presents a complex situation with various organizations formulating regulations. These include the OECD's principles, the EU's legislation, and individual US states enacting their own laws. Similar trends are observed in Canada and other countries, with industry-specific regulations emerging as well. Model Validation Success Rate: This metric focuses on the technical aspects, tracking the percentage of models that pass the model validation team's assessments, ensuring the models function as intended without technical flaws. • After earning a PhD and working in AI research and education during the 1990s, the lack of career opportunities in AI led him to pursue a successful career in finance. Paul has lead manay enterprise-wide transformation programs, building and designing complex quantitative systems involving Big Data feeds. Urgency of Ethical AI Frameworks Model Inventory Completion: This metric tracks the number of models that have successfully reached the final stage by being added to the organization's risk model inventory. A complete inventory provides a clear picture of all deployed models for ongoing monitoring and risk management. By monitoring these metrics, organizations can gain valuable insights into the effectiveness of their AI assurance framework in identifying and mitigating risks throughout • However, around 2018, he recognized the significant advancements in machine learning and AI being utilized by major companies. This resurgence of AI, particularly the complex and probabilistic nature of generative models, sparked a renewed concern for the ethical risks involved. By 2020, the potential for rapid AI growth solidified his belief in the critical need to address these complex ethical issues. This realization reignited his passion for AI, leading him back to the field in 2019. 05 Paul argues that ethical considerations surrounding AI are universal across industries. While inherent risks like explainability, bias, and robustness apply to all AI models, the severity of consequences varies by sector. High-stakes sectors like healthcare and finance face particularly critical ramifications for model errors. Public services are also concerning due to the potential impact on people's lives. To stay informed, Paul recommends establishing a dedicated horizon scanning team within an organization. This team, consisting of just a few people, would be responsible for monitoring publications from relevant organizations like the OECD and EU. Legal firms specializing in tracking these regulations and 06 www.ciobusinessworld.com www.ciobusinessworld.com the entire AI development lifecycle. These metrics not only assess the health of the framework but also demonstrate a commitment to responsible AI practices. Beyond technical knowledge, Paul emphasizes the importance of empathy for the impact of AI models on users. Mitigating the Unforeseen Most importantly, Paul highlights passion as the most critical quality. A genuine enthusiasm for the field and a desire to leverage AI for positive outcomes are essential for success in this role. Paul recognizes the inherent difficulty of anticipating unforeseen consequences with AI. However, he proposes two key strategies to mitigate these risks. AI for Social Good Firstly, Paul emphasizes the importance of fostering diversity within data science teams. This includes diversity in terms of gender, background, culture, and age. A broader range of perspectives during model development can help identify potential biases or unintended consequences that a homogenous team might overlook. Paul expresses optimism about the positive societal impacts of AI. He highlights several examples: Personalized Customer Interactions: AI enables highly personalized interactions with customers across various sectors, including banking, eLearning, and more. This allows for tailored experiences and targeted information delivery, exceeding previous capabilities. • Secondly, Paul describes a structured process called "consequence scanning." This proactive approach involves a series of steps for teams to identify potential negative behaviors a model might exhibit. By considering "what-if" scenarios and corresponding mitigation strategies, teams can address potential issues before they arise. For example, consequence scanning might involve examining how a model could unintentionally discriminate against a certain group, even if such discrimination wasn't the intended purpose. Additionally, this process includes analyzing the training data for potential biases or a lack of diversity that could lead to skewed outcomes when deployed in different regions. AI on Mobile Devices: AI systems can be delivered on mobile devices, making them accessible even in regions with limited traditional IT infrastructure. This opens doors to applications like personalized learning opportunities in regions with underdeveloped educational systems. • Medical Advancements: AI is playing a significant role in medical breakthroughs. For instance, DeepMind's AlphaFold 3 has the potential to revolutionize disease identification, treatment, and vaccine development, leading to a major boost in drug discovery. Personalized medicine also holds immense potential. • By implementing these two strategies, organizations can take a proactive approach to mitigating unforeseen consequences and fostering more responsible AI development. Environmental Sustainability: AI can be a powerful tool in achieving the UN's Sustainable Development Goals. It can be used to predict pathways to net-zero emissions and optimize energy efficiency. For example, AI models are being used to optimize workload distribution in data centers, leading to significant efficiency improvements. • Beyond Code Paul dispels the myth that data and AI ethics specialists require extensive technical expertise. While a foundational understanding of probabilistic models, common algorithms like gradient descent and XGBoost, and transformer models is beneficial, the core competency lies in ethical considerations. Paul emphasizes that AI ethics plays a crucial role in maximizing these benefits by mitigating potential harms and limitations associated with AI development. He expresses his enthusiasm for the future of AI, particularly its potential to solve problems and contribute meaningfully to society. A strong understanding of how and where bias can creep into AI models is crucial. This includes recognizing bias in raw data, training data labeling, discrepancies between training and testing data sets, and potential mismatches between training data origin and deployment location. 07 08 www.ciobusinessworld.com www.ciobusinessworld.com

18. In light of these risks, Paul emphasizes the importance of a robust assurance framework for all organizations using AI. This framework, driven by senior leadership, should establish safeguards to mitigate ethical risks across various industries. He concludes that there's no avoiding these challenges, and responsible AI development necessitates addressing these issues head-on. communication with industry regulators are also valuable resources. Staying ahead of the curve, however, proves more challenging. Rather than attempting to predict future regulations, Paul suggests a proactive approach. Organizations should identify potential risks within their specific AI use cases and proactively mitigate them. This focus on responsible AI practices aligns with ethical obligations and positions them to contribute meaningfully to shaping future regulations based on real-world experience. Leading the Way in Ethical AI including risk levels, deployment duration, and retraining needs, is essential for effective oversight. Paul reports encountering a positive reception for his work on ethical AI. The companies he's worked with have expressed genuine interest and curiosity in understanding this concept, despite its perceived mysteriousness. He attributes this openness to the growing awareness of ethical risks in AI and the potential for reputational damage if these risks are not addressed. Consequently, he's been welcomed by development teams, data scientists, and machine learning engineers who are eager to collaborate and ensure responsible AI practices. Rise of AI and the Urgency of Ethics Building a Responsible AI Future Paul Dongha describes his personal journey in the field of AI. Paul stresses the importance of robust program management when implementing a generative AI assurance framework. Success hinges on several key principles. First, a clearly defined vision and measurable goals ensure everyone is working towards the same objective. Second, strong leadership commitment is crucial to drive collaboration between the various teams involved. Third, AI ethics specialists play a critical role in bridging the gap between technical teams and fostering an understanding of the ethical considerations behind responsible AI. After earning a PhD and working in AI research and education during the 1990s, the lack of career opportunities in AI led him to pursue a successful career in finance. Paul has lead manay enterprise-wide transformation programs, building and designing complex quantitative systems involving Big Data feeds. Measuring the Effectiveness of a Generative AI Assurance Framework Paul emphasizes the importance of measurable metrics to assess the effectiveness of a generative AI assurance framework. He proposes a set of key metrics that track various stages of the model development process. Urgency of Ethical AI Frameworks However, around 2018, he recognized the significant advancements in machine learning and AI being utilized by major companies. This resurgence of AI, particularly the complex and probabilistic nature of generative models, sparked a renewed concern for the ethical risks involved. By 2020, the potential for rapid AI growth solidified his belief in the critical need to address these complex ethical issues. This realization reignited his passion for AI, leading him back to the field in 2019. 18 Paul argues that ethical considerations surrounding AI are universal across industries. While inherent risks like explainability, bias, and robustness apply to all AI models, the severity of consequences varies by sector. High-stakes sectors like healthcare and finance face particularly critical ramifications for model errors. Public services are also concerning due to the potential impact on people's lives. Ethical Impact Assessment Completion: This metric monitors the number of models that have successfully completed a mandatory ethical impact assessment. Furthermore, a successful program requires an organizational culture that acknowledges the importance of ethical AI and fosters a sense of shared responsibility. The evolving nature of the AI field necessitates an adaptable framework that can accommodate ongoing learning and experimentation. This dynamic environment, though challenging, also presents exciting opportunities for innovation. Finally, Paul highlights that the problem- solving nature and novelty of building an AI assurance framework can be motivating for engineers, fostering a positive program environment. • Ethical Impact Assessment Success Rate: This metric goes beyond completion rates and focuses on the percentage of models that pass the ethical impact assessment, indicating a successful mitigation of potential ethical risks. • Ethics Board Review: these metric tracks the number of models presented to the ethics board for additional scrutiny and approval, ensuring a higher level of oversight for potentially sensitive models. • Staying Informed and Proactive Paul Dongha addresses the challenge of navigating the ever-evolving regulatory landscape surrounding AI. The global nature of AI development presents a complex situation with various organizations formulating regulations. These include the OECD's principles, the EU's legislation, and individual US states enacting their own laws. Similar trends are observed in Canada and other countries, with industry-specific regulations emerging as well. Model Validation Success Rate: This metric focuses on the technical aspects, tracking the percentage of models that pass the model validation team's assessments, ensuring the models function as intended without technical flaws. • Model Inventory Completion: This metric tracks the number of models that have successfully reached the final stage by being added to the organization's risk model inventory. A complete inventory provides a clear picture of all deployed models for ongoing monitoring and risk management. By monitoring these metrics, organizations can gain valuable insights into the effectiveness of their AI assurance framework in identifying and mitigating risks throughout • To stay informed, Paul recommends establishing a dedicated horizon scanning team within an organization. This team, consisting of just a few people, would be responsible for monitoring publications from relevant organizations like the OECD and EU. Legal firms specializing in tracking these regulations and 06 www.ciobusinessworld.com www.ciobusinessworld.com the entire AI development lifecycle. These metrics not only assess the health of the framework but also demonstrate a commitment to responsible AI practices. Beyond technical knowledge, Paul emphasizes the importance of empathy for the impact of AI models on users. Mitigating the Unforeseen Most importantly, Paul highlights passion as the most critical quality. A genuine enthusiasm for the field and a desire to leverage AI for positive outcomes are essential for success in this role. Paul recognizes the inherent difficulty of anticipating unforeseen consequences with AI. However, he proposes two key strategies to mitigate these risks. AI for Social Good Firstly, Paul emphasizes the importance of fostering diversity within data science teams. This includes diversity in terms of gender, background, culture, and age. A broader range of perspectives during model development can help identify potential biases or unintended consequences that a homogenous team might overlook. Paul expresses optimism about the positive societal impacts of AI. He highlights several examples: Personalized Customer Interactions: AI enables highly personalized interactions with customers across various sectors, including banking, eLearning, and more. This allows for tailored experiences and targeted information delivery, exceeding previous capabilities. • Secondly, Paul describes a structured process called "consequence scanning." This proactive approach involves a series of steps for teams to identify potential negative behaviors a model might exhibit. By considering "what-if" scenarios and corresponding mitigation strategies, teams can address potential issues before they arise. For example, consequence scanning might involve examining how a model could unintentionally discriminate against a certain group, even if such discrimination wasn't the intended purpose. Additionally, this process includes analyzing the training data for potential biases or a lack of diversity that could lead to skewed outcomes when deployed in different regions. AI on Mobile Devices: AI systems can be delivered on mobile devices, making them accessible even in regions with limited traditional IT infrastructure. This opens doors to applications like personalized learning opportunities in regions with underdeveloped educational systems. • Medical Advancements: AI is playing a significant role in medical breakthroughs. For instance, DeepMind's AlphaFold 3 has the potential to revolutionize disease identification, treatment, and vaccine development, leading to a major boost in drug discovery. Personalized medicine also holds immense potential. • By implementing these two strategies, organizations can take a proactive approach to mitigating unforeseen consequences and fostering more responsible AI development. Environmental Sustainability: AI can be a powerful tool in achieving the UN's Sustainable Development Goals. It can be used to predict pathways to net-zero emissions and optimize energy efficiency. For example, AI models are being used to optimize workload distribution in data centers, leading to significant efficiency improvements. • Beyond Code Paul dispels the myth that data and AI ethics specialists require extensive technical expertise. While a foundational understanding of probabilistic models, common algorithms like gradient descent and XGBoost, and transformer models is beneficial, the core competency lies in ethical considerations. Paul emphasizes that AI ethics plays a crucial role in maximizing these benefits by mitigating potential harms and limitations associated with AI development. He expresses his enthusiasm for the future of AI, particularly its potential to solve problems and contribute meaningfully to society. A strong understanding of how and where bias can creep into AI models is crucial. This includes recognizing bias in raw data, training data labeling, discrepancies between training and testing data sets, and potential mismatches between training data origin and deployment location. 07 08 www.ciobusinessworld.com www.ciobusinessworld.com

19. In light of these risks, Paul emphasizes the importance of a robust assurance framework for all organizations using AI. This framework, driven by senior leadership, should establish safeguards to mitigate ethical risks across various industries. He concludes that there's no avoiding these challenges, and responsible AI development necessitates addressing these issues head-on. communication with industry regulators are also valuable resources. Staying ahead of the curve, however, proves more challenging. Rather than attempting to predict future regulations, Paul suggests a proactive approach. Organizations should identify potential risks within their specific AI use cases and proactively mitigate them. This focus on responsible AI practices aligns with ethical obligations and positions them to contribute meaningfully to shaping future regulations based on real-world experience. Building a Responsible AI Future Paul stresses the importance of robust program management when implementing a generative AI assurance framework. Success hinges on several key principles. First, a clearly defined vision and measurable goals ensure everyone is working towards the same objective. Second, strong leadership commitment is crucial to drive collaboration between the various teams involved. Third, AI ethics specialists play a critical role in bridging the gap between technical teams and fostering an understanding of the ethical considerations behind responsible AI. Measuring the Effectiveness of a Generative AI Assurance Framework Paul emphasizes the importance of measurable metrics to assess the effectiveness of a generative AI assurance framework. He proposes a set of key metrics that track various stages of the model development process. Ethical Impact Assessment Completion: This metric monitors the number of models that have successfully completed a mandatory ethical impact assessment. Furthermore, a successful program requires an organizational culture that acknowledges the importance of ethical AI and fosters a sense of shared responsibility. The evolving nature of the AI field necessitates an adaptable framework that can accommodate ongoing learning and experimentation. This dynamic environment, though challenging, also presents exciting opportunities for innovation. Finally, Paul highlights that the problem- solving nature and novelty of building an AI assurance framework can be motivating for engineers, fostering a positive program environment. • Ethical Impact Assessment Success Rate: This metric goes beyond completion rates and focuses on the percentage of models that pass the ethical impact assessment, indicating a successful mitigation of potential ethical risks. • Ethics Board Review: these metric tracks the number of models presented to the ethics board for additional scrutiny and approval, ensuring a higher level of oversight for potentially sensitive models. • Staying Informed and Proactive Paul Dongha addresses the challenge of navigating the ever-evolving regulatory landscape surrounding AI. The global nature of AI development presents a complex situation with various organizations formulating regulations. These include the OECD's principles, the EU's legislation, and individual US states enacting their own laws. Similar trends are observed in Canada and other countries, with industry-specific regulations emerging as well. Model Validation Success Rate: This metric focuses on the technical aspects, tracking the percentage of models that pass the model validation team's assessments, ensuring the models function as intended without technical flaws. • Model Inventory Completion: This metric tracks the number of models that have successfully reached the final stage by being added to the organization's risk model inventory. A complete inventory provides a clear picture of all deployed models for ongoing monitoring and risk management. By monitoring these metrics, organizations can gain valuable insights into the effectiveness of their AI assurance framework in identifying and mitigating risks throughout • To stay informed, Paul recommends establishing a dedicated horizon scanning team within an organization. This team, consisting of just a few people, would be responsible for monitoring publications from relevant organizations like the OECD and EU. Legal firms specializing in tracking these regulations and 19 www.ciobusinessworld.com www.ciobusinessworld.com the entire AI development lifecycle. These metrics not only assess the health of the framework but also demonstrate a commitment to responsible AI practices. Beyond technical knowledge, Paul emphasizes the importance of empathy for the impact of AI models on users. Mitigating the Unforeseen Most importantly, Paul highlights passion as the most critical quality. A genuine enthusiasm for the field and a desire to leverage AI for positive outcomes are essential for success in this role. Paul recognizes the inherent difficulty of anticipating unforeseen consequences with AI. However, he proposes two key strategies to mitigate these risks. AI for Social Good Firstly, Paul emphasizes the importance of fostering diversity within data science teams. This includes diversity in terms of gender, background, culture, and age. A broader range of perspectives during model development can help identify potential biases or unintended consequences that a homogenous team might overlook. Paul expresses optimism about the positive societal impacts of AI. He highlights several examples: Personalized Customer Interactions: AI enables highly personalized interactions with customers across various sectors, including banking, eLearning, and more. This allows for tailored experiences and targeted information delivery, exceeding previous capabilities. • Secondly, Paul describes a structured process called "consequence scanning." This proactive approach involves a series of steps for teams to identify potential negative behaviors a model might exhibit. By considering "what-if" scenarios and corresponding mitigation strategies, teams can address potential issues before they arise. For example, consequence scanning might involve examining how a model could unintentionally discriminate against a certain group, even if such discrimination wasn't the intended purpose. Additionally, this process includes analyzing the training data for potential biases or a lack of diversity that could lead to skewed outcomes when deployed in different regions. AI on Mobile Devices: AI systems can be delivered on mobile devices, making them accessible even in regions with limited traditional IT infrastructure. This opens doors to applications like personalized learning opportunities in regions with underdeveloped educational systems. • Medical Advancements: AI is playing a significant role in medical breakthroughs. For instance, DeepMind's AlphaFold 3 has the potential to revolutionize disease identification, treatment, and vaccine development, leading to a major boost in drug discovery. Personalized medicine also holds immense potential. • By implementing these two strategies, organizations can take a proactive approach to mitigating unforeseen consequences and fostering more responsible AI development. Environmental Sustainability: AI can be a powerful tool in achieving the UN's Sustainable Development Goals. It can be used to predict pathways to net-zero emissions and optimize energy efficiency. For example, AI models are being used to optimize workload distribution in data centers, leading to significant efficiency improvements. • Beyond Code Paul dispels the myth that data and AI ethics specialists require extensive technical expertise. While a foundational understanding of probabilistic models, common algorithms like gradient descent and XGBoost, and transformer models is beneficial, the core competency lies in ethical considerations. Paul emphasizes that AI ethics plays a crucial role in maximizing these benefits by mitigating potential harms and limitations associated with AI development. He expresses his enthusiasm for the future of AI, particularly its potential to solve problems and contribute meaningfully to society. A strong understanding of how and where bias can creep into AI models is crucial. This includes recognizing bias in raw data, training data labeling, discrepancies between training and testing data sets, and potential mismatches between training data origin and deployment location. 07 08 www.ciobusinessworld.com www.ciobusinessworld.com

20. the entire AI development lifecycle. These metrics not only assess the health of the framework but also demonstrate a commitment to responsible AI practices. Beyond technical knowledge, Paul emphasizes the importance of empathy for the impact of AI models on users. Mitigating the Unforeseen Most importantly, Paul highlights passion as the most critical quality. A genuine enthusiasm for the field and a desire to leverage AI for positive outcomes are essential for success in this role. Paul recognizes the inherent difficulty of anticipating unforeseen consequences with AI. However, he proposes two key strategies to mitigate these risks. AI for Social Good Firstly, Paul emphasizes the importance of fostering diversity within data science teams. This includes diversity in terms of gender, background, culture, and age. A broader range of perspectives during model development can help identify potential biases or unintended consequences that a homogenous team might overlook. Paul expresses optimism about the positive societal impacts of AI. He highlights several examples: Personalized Customer Interactions: AI enables highly personalized interactions with customers across various sectors, including banking, eLearning, and more. This allows for tailored experiences and targeted information delivery, exceeding previous capabilities. • Secondly, Paul describes a structured process called "consequence scanning." This proactive approach involves a series of steps for teams to identify potential negative behaviors a model might exhibit. By considering "what-if" scenarios and corresponding mitigation strategies, teams can address potential issues before they arise. For example, consequence scanning might involve examining how a model could unintentionally discriminate against a certain group, even if such discrimination wasn't the intended purpose. Additionally, this process includes analyzing the training data for potential biases or a lack of diversity that could lead to skewed outcomes when deployed in different regions. AI on Mobile Devices: AI systems can be delivered on mobile devices, making them accessible even in regions with limited traditional IT infrastructure. This opens doors to applications like personalized learning opportunities in regions with underdeveloped educational systems. • Medical Advancements: AI is playing a significant role in medical breakthroughs. For instance, DeepMind's AlphaFold 3 has the potential to revolutionize disease identification, treatment, and vaccine development, leading to a major boost in drug discovery. Personalized medicine also holds immense potential. • By implementing these two strategies, organizations can take a proactive approach to mitigating unforeseen consequences and fostering more responsible AI development. Environmental Sustainability: AI can be a powerful tool in achieving the UN's Sustainable Development Goals. It can be used to predict pathways to net-zero emissions and optimize energy efficiency. For example, AI models are being used to optimize workload distribution in data centers, leading to significant efficiency improvements. • Beyond Code Paul dispels the myth that data and AI ethics specialists require extensive technical expertise. While a foundational understanding of probabilistic models, common algorithms like gradient descent and XGBoost, and transformer models is beneficial, the core competency lies in ethical considerations. Paul emphasizes that AI ethics plays a crucial role in maximizing these benefits by mitigating potential harms and limitations associated with AI development. He expresses his enthusiasm for the future of AI, particularly its potential to solve problems and contribute meaningfully to society. A strong understanding of how and where bias can creep into AI models is crucial. This includes recognizing bias in raw data, training data labeling, discrepancies between training and testing data sets, and potential mismatches between training data origin and deployment location. 20 08 www.ciobusinessworld.com www.ciobusinessworld.com

21. 21 www.ciobusinessworld.com www.ciobusinessworld.com

24. If more than 25 of your employees register and log at least one activity every 12 months, STEM Learning can create a data sharing agreement which will give you access to a consolidated social impact report for all the volunteering work reported by your employees on the platform. It really is that simple to generate social value impact, build a diverse future talent pipeline, help your early career employees develop key business skills - and ultimately increase both employee retention and productivity. T will be needed in the future, and how do we prepare our businesses for that future? Starting young he letters on all our minds are of course AI and what it means for our businesses and our technology teams. What skills Did you know that children can make career level type decisions as young as five years old? Also, between leaving primary and secondary school children's views on which careers are 'for them' continue to narrow as they are influenced by their families, communities, teachers, and peers. They then narrow again when they pick GCSEs, usually to try and match a very small number of career options. To better represent the world we live in, we need to continue to challenge and diversify the industry. This starts in schools. Join us! employers@stem.org.uk While AI might be busy revolutionising how we work, how we interact, and how we run our lives, there is one critical thing it's not going to change unless we act quickly. Right now, we are experiencing a crisis around skills gaps in digital, tech and tech adjacent roles. All research companies, recruiters, and management consultants tell us that the technology skills gap is only going to widen. Simply, more people need better digital skills and the ability to work confidently with AI as it augments and enhances their roles. Bio: Dr Nicki Clegg has over 30 years of experience as a technologist and 15 years as strategist and senior leader. She was a Chief Technology Officer for almost five years before deciding to focus fully on driving social mobility, diversity and inclusion in technology. She is now Industry Stakeholder Relationship Manager for STEM Learning helping deliver the National Centre for Computing Education (NCCE). Her role raises awareness of the NCCE and increases business engagement with schools and young people to help inspire a new and more diverse generation into careers in technology. How to make schools engagement part of your talent management programme A crucial but often under-developed component of strategic talent management programmes is the implementation of a volunteering scheme that works with schools to inspire new generations to join the tech workforce in the future. Not only can this create a bigger pool to recruit from, but a well-designed scheme focusing on young people from disadvantaged or under-represented groups can also create a large and diverse pipeline of previously untapped talent. It can also create a double-digit social value return on investment, not to mention engaged, confident and motivated employees. talent pipeline, as well as deliver measurable social value at national and local levels in terms of addressing systemic issues such as social mobility. Your employees can: Ÿ Free access to a digital community of other volunteers supporting each other There's also a compelling need for technology and data teams to be far more diverse than they currently are. The most productive and performant businesses need to have teams that match the demographic of their customers and citizens - not just to be competitive, but also to avoid unintended negative consequences in the AI driven services being created. Despite more than 20 years of targeted interventions by progressive businesses to recruit inclusively, to create supportive environments, encourage early careers, continuous learning and 'squiggly' careers, diversity levels haven't really changed. Ÿ Quickly register and undertake the required 45 minutes of training which includes safeguarding training Ÿ Free access to templated, tried and tested, impactful school activities Sustainable volunteering programmes in schools are easy to implement and manage if an educational partner like STEM Learning is used. You don't need to start from scratch. The free to use STEM Ambassadors platform has a Computing Ambassador scheme which guides and supports individuals and organisations through the volunteering process. Ÿ Get a free DBS check or share their DBS certificate if they already have one Ÿ Free access to a 'marketplace' where teachers ask for volunteers to support them with specific asks (such as careers events or to support a competition), and where volunteers can share their offers to teachers Ÿ Free access to a range of self-paced and remotely delivered training such as planning and delivery an activity, how to talk inclusively, how to engage virtually in an impactful way and many more Partnering with organisations who are trusted advisers to teachers and schools can accelerate impact both on the immediate and long-term Ÿ Free personal social impact score based on the activities logged on the platform 24

25. If more than 25 of your employees register and log at least one activity every 12 months, STEM Learning can create a data sharing agreement which will give you access to a consolidated social impact report for all the volunteering work reported by your employees on the platform. It really is that simple to generate social value impact, build a diverse future talent pipeline, help your early career employees develop key business skills - and ultimately increase both employee retention and productivity. To better represent the world we live in, we need to continue to challenge and diversify the industry. This starts in schools. Join us! employers@stem.org.uk Bio: Dr Nicki Clegg has over 30 years of experience as a technologist and 15 years as strategist and senior leader. She was a Chief Technology Officer for almost five years before deciding to focus fully on driving social mobility, diversity and inclusion in technology. She is now Industry Stakeholder Relationship Manager for STEM Learning helping deliver the National Centre for Computing Education (NCCE). Her role raises awareness of the NCCE and increases business engagement with schools and young people to help inspire a new and more diverse generation into careers in technology. talent pipeline, as well as deliver measurable social value at national and local levels in terms of addressing systemic issues such as social mobility. Your employees can: Ÿ Free access to a digital community of other volunteers supporting each other Ÿ Quickly register and undertake the required 45 minutes of training which includes safeguarding training Ÿ Free access to templated, tried and tested, impactful school activities Sustainable volunteering programmes in schools are easy to implement and manage if an educational partner like STEM Learning is used. You don't need to start from scratch. The free to use STEM Ambassadors platform has a Computing Ambassador scheme which guides and supports individuals and organisations through the volunteering process. Ÿ Get a free DBS check or share their DBS certificate if they already have one Ÿ Free access to a 'marketplace' where teachers ask for volunteers to support them with specific asks (such as careers events or to support a competition), and where volunteers can share their offers to teachers Ÿ Free access to a range of self-paced and remotely delivered training such as planning and delivery an activity, how to talk inclusively, how to engage virtually in an impactful way and many more Ÿ Free personal social impact score based on the activities logged on the platform 25 www.ciobusinessworld.com

26. If more than 25 of your employees register and log at least one activity every 12 months, STEM Learning can create a data sharing agreement which will give you access to a consolidated social impact report for all the volunteering work reported by your employees on the platform. It really is that simple to generate social value impact, build a diverse future talent pipeline, help your early career employees develop key business skills - and ultimately increase both employee retention and productivity. To better represent the world we live in, we need to continue to challenge and diversify the industry. This starts in schools. Join us! employers@stem.org.uk Bio: Dr Nicki Clegg has over 30 years of experience as a technologist and 15 years as strategist and senior leader. She was a Chief Technology Officer for almost five years before deciding to focus fully on driving social mobility, diversity and inclusion in technology. She is now Industry Stakeholder Relationship Manager for STEM Learning helping deliver the National Centre for Computing Education (NCCE). Her role raises awareness of the NCCE and increases business engagement with schools and young people to help inspire a new and more diverse generation into careers in technology. talent pipeline, as well as deliver measurable social value at national and local levels in terms of addressing systemic issues such as social mobility. Your employees can: Ÿ Free access to a digital community of other volunteers supporting each other Ÿ Quickly register and undertake the required 45 minutes of training which includes safeguarding training Ÿ Free access to templated, tried and tested, impactful school activities Sustainable volunteering programmes in schools are easy to implement and manage if an educational partner like STEM Learning is used. You don't need to start from scratch. The free to use STEM Ambassadors platform has a Computing Ambassador scheme which guides and supports individuals and organisations through the volunteering process. Ÿ Get a free DBS check or share their DBS certificate if they already have one Ÿ Free access to a 'marketplace' where teachers ask for volunteers to support them with specific asks (such as careers events or to support a competition), and where volunteers can share their offers to teachers Ÿ Free access to a range of self-paced and remotely delivered training such as planning and delivery an activity, how to talk inclusively, how to engage virtually in an impactful way and many more Ÿ Free personal social impact score based on the activities logged on the platform 26

27. If more than 25 of your employees register and log at least one activity every 12 months, STEM Learning can create a data sharing agreement which will give you access to a consolidated social impact report for all the volunteering work reported by your employees on the platform. It really is that simple to generate social value impact, build a diverse future talent pipeline, help your early career employees develop key business skills - and ultimately increase both employee retention and productivity. To better represent the world we live in, we need to continue to challenge and diversify the industry. This starts in schools. Join us! employers@stem.org.uk Bio: Dr Nicki Clegg has over 30 years of experience as a technologist and 15 years as strategist and senior leader. She was a Chief Technology Officer for almost five years before deciding to focus fully on driving social mobility, diversity and inclusion in technology. She is now Industry Stakeholder Relationship Manager for STEM Learning helping deliver the National Centre for Computing Education (NCCE). Her role raises awareness of the NCCE and increases business engagement with schools and young people to help inspire a new and more diverse generation into careers in technology. Ÿ Free access to a digital community of other volunteers supporting each other Ÿ Free access to templated, tried and tested, impactful school activities Ÿ Free access to a 'marketplace' where teachers ask for volunteers to support them with specific asks (such as careers events or to support a competition), and where volunteers can share their offers to teachers Ÿ Free personal social impact score based on the activities logged on the platform 27 www.ciobusinessworld.com

28. I digital transformation is key to remaining competitive, driving innovation, and improving decision-making. By leveraging data and AI, companies can optimize operations, predict market trends, and deliver highly personalized customer experiences. This shift is reshaping industries, and CEOs are leading the way toward a data-centric future. AI in the Boardroom n today's competitive business environment, data and artificial intelligence (AI) are no longer optional but essential tools for success. CEOs are realizing that AI is not just transforming business operations—it's reshaping how CEOs and boards make strategic decisions. AI-powered tools provide real-time insights that enable better decision-making. Predictive analytics allow CEOs to forecast market trends and adjust strategies proactively, while AI-driven financial models help executives monitor their companies' health and performance. The Power of Data and AI In the boardroom, AI also aids in governance and oversight. By analyzing large datasets, AI can detect inefficiencies, compliance issues, or financial risks, giving board members actionable insights to improve corporate performance. Additionally, AI supports risk management by identifying potential threats such as cybersecurity vulnerabilities or supply chain disruptions. This allows CEOs to make data- driven decisions to protect their companies from emerging risks. Data has become one of the most valuable resources for businesses. With AI's ability to process massive amounts of information in real-time, companies can make faster, more accurate decisions. AI can detect patterns and insights that help organizations stay ahead of their competitors. For CEOs, effectively utilizing data is now crucial for driving growth and maintaining a competitive edge. Challenges of Digital Transformation AI technologies like machine learning and natural language processing (NLP) have also matured, offering companies the ability to automate tasks, improve customer service, and create innovative products. Whether it's optimizing supply chains, enhancing customer experiences, or driving operational efficiency, AI is transforming every business function. While the benefits of data and AI are clear, CEOs face significant challenges in driving digital transformation. The rapid pace of technological change is one of the biggest hurdles. CEOs must continually educate themselves and their teams to stay ahead of AI advancements and determine which technologies will be most valuable for their businesses. The CEO's Role in Digital Transformation CEOs play a vital role in driving digital transformation. More than just adopting technology, successful digital change requires embedding a culture where data is central to decision-making. Visionary CEOs understand that companies must be agile and innovative to remain relevant in an ever-evolving digital landscape. This means fostering an environment where employees are encouraged to embrace data and AI as tools to improve their work. Data privacy and security are also major concerns. As companies collect and analyze increasing amounts of data, they must ensure that sensitive information is protected. Compliance with regulations like the General Data Protection Regulation (GDPR) is critical, and a data breach could damage a company's reputation and financial standing. CEOs must prioritize cybersecurity and data protection to maintain customer trust. Managing this change is one of the biggest challenges. Digital transformation often requires restructuring teams, reskilling employees, and rethinking traditional business processes. CEOs must guide their organizations through this transition, ensuring the workforce is engaged and aligned with the new digital direction. Organizational resistance to change is another challenge. Digital transformation often requires a shift in company culture, and employees may be hesitant to adopt new technologies or workflows. CEOs must break down silos and encourage cross-functional collaboration to drive innovation. CEOs also need to ensure that digital transformation aligns with the company's long-term strategy. It's not just about implementing AI and data tools, but about making sure they enhance overall business goals. Finally, finding the right talent is a critical issue. AI and data science expertise are in high demand, and CEOs must ensure their companies can attract and retain skilled professionals. Investing in training programs and building Data and AI in the Boardroom: How CEOs are Embracing Digital Transformation partnerships with academic institutions are essential strategies to close the talent gap. To succeed, CEOs must take a proactive approach to digital transformation. This means not only investing in AI and data tools but also fostering a culture of innovation, agility, and continuous learning. Those who lead with a clear digital vision will position their organizations to thrive in the data-driven future. The Future of AI in the Boardroom The role of AI and data in the boardroom will continue to grow. CEOs who embrace these technologies will be better positioned to lead their companies into a future of innovation, agility, and competitiveness. Real-time data and predictive analytics will become even more integral to decision-making, allowing business leaders to navigate complex markets with greater confidence. Conclusion Data and AI are transforming businesses, and CEOs are at the forefront of this revolution. By embracing digital transformation, they can improve decision-making, optimize operations, and stay competitive in an increasingly complex world. While challenges like rapid technological advancements, data privacy, and talent shortages remain, the benefits of adopting AI far outweigh the risks. CEOs who successfully lead their organizations through this transformation will unlock new opportunities for growth and innovation, ensuring their businesses remain competitive and relevant in the digital age. In the years ahead, AI will play a larger role in shaping corporate strategy. As the technology evolves, AI will provide even deeper insights, helping CEOs and boards make smarter, faster decisions. Companies that fully integrate AI into their operations will have a significant advantage over those that hesitate, gaining agility in adapting to new market conditions. 28

29. I digital transformation is key to remaining competitive, driving innovation, and improving decision-making. By leveraging data and AI, companies can optimize operations, predict market trends, and deliver highly personalized customer experiences. This shift is reshaping industries, and CEOs are leading the way toward a data-centric future. AI in the Boardroom n today's competitive business environment, data and artificial intelligence (AI) are no longer optional but essential tools for success. CEOs are realizing that partnerships with academic institutions are essential strategies to close the talent gap. To succeed, CEOs must take a proactive approach to digital transformation. This means not only investing in AI and data tools but also fostering a culture of innovation, agility, and continuous learning. Those who lead with a clear digital vision will position their organizations to thrive in the data-driven future. AI is not just transforming business operations—it's reshaping how CEOs and boards make strategic decisions. AI-powered tools provide real-time insights that enable better decision-making. Predictive analytics allow CEOs to forecast market trends and adjust strategies proactively, while AI-driven financial models help executives monitor their companies' health and performance. The Future of AI in the Boardroom The role of AI and data in the boardroom will continue to grow. CEOs who embrace these technologies will be better positioned to lead their companies into a future of innovation, agility, and competitiveness. Real-time data and predictive analytics will become even more integral to decision-making, allowing business leaders to navigate complex markets with greater confidence. Conclusion Data and AI are transforming businesses, and CEOs are at the forefront of this revolution. By embracing digital transformation, they can improve decision-making, optimize operations, and stay competitive in an increasingly complex world. While challenges like rapid technological advancements, data privacy, and talent shortages remain, the benefits of adopting AI far outweigh the risks. CEOs who successfully lead their organizations through this transformation will unlock new opportunities for growth and innovation, ensuring their businesses remain competitive and relevant in the digital age. The Power of Data and AI In the boardroom, AI also aids in governance and oversight. By analyzing large datasets, AI can detect inefficiencies, compliance issues, or financial risks, giving board members actionable insights to improve corporate performance. Additionally, AI supports risk management by identifying potential threats such as cybersecurity vulnerabilities or supply chain disruptions. This allows CEOs to make data- driven decisions to protect their companies from emerging risks. Data has become one of the most valuable resources for businesses. With AI's ability to process massive amounts of information in real-time, companies can make faster, more accurate decisions. AI can detect patterns and insights that help organizations stay ahead of their competitors. For CEOs, effectively utilizing data is now crucial for driving growth and maintaining a competitive edge. In the years ahead, AI will play a larger role in shaping corporate strategy. As the technology evolves, AI will provide even deeper insights, helping CEOs and boards make smarter, faster decisions. Companies that fully integrate AI into their operations will have a significant advantage over those that hesitate, gaining agility in adapting to new market conditions. Challenges of Digital Transformation AI technologies like machine learning and natural language processing (NLP) have also matured, offering companies the ability to automate tasks, improve customer service, and create innovative products. Whether it's optimizing supply chains, enhancing customer experiences, or driving operational efficiency, AI is transforming every business function. While the benefits of data and AI are clear, CEOs face significant challenges in driving digital transformation. The rapid pace of technological change is one of the biggest hurdles. CEOs must continually educate themselves and their teams to stay ahead of AI advancements and determine which technologies will be most valuable for their businesses. The CEO's Role in Digital Transformation CEOs play a vital role in driving digital transformation. More than just adopting technology, successful digital change requires embedding a culture where data is central to decision-making. Visionary CEOs understand that companies must be agile and innovative to remain relevant in an ever-evolving digital landscape. This means fostering an environment where employees are encouraged to embrace data and AI as tools to improve their work. Data privacy and security are also major concerns. As companies collect and analyze increasing amounts of data, they must ensure that sensitive information is protected. Compliance with regulations like the General Data Protection Regulation (GDPR) is critical, and a data breach could damage a company's reputation and financial standing. CEOs must prioritize cybersecurity and data protection to maintain customer trust. Managing this change is one of the biggest challenges. Digital transformation often requires restructuring teams, reskilling employees, and rethinking traditional business processes. CEOs must guide their organizations through this transition, ensuring the workforce is engaged and aligned with the new digital direction. Organizational resistance to change is another challenge. Digital transformation often requires a shift in company culture, and employees may be hesitant to adopt new technologies or workflows. CEOs must break down silos and encourage cross-functional collaboration to drive innovation. CEOs also need to ensure that digital transformation aligns with the company's long-term strategy. It's not just about implementing AI and data tools, but about making sure they enhance overall business goals. Finally, finding the right talent is a critical issue. AI and data science expertise are in high demand, and CEOs must ensure their companies can attract and retain skilled professionals. Investing in training programs and building 29 www.ciobusinessworld.com

30. partnerships with academic institutions are essential strategies to close the talent gap. To succeed, CEOs must take a proactive approach to digital transformation. This means not only investing in AI and data tools but also fostering a culture of innovation, agility, and continuous learning. Those who lead with a clear digital vision will position their organizations to thrive in the data-driven future. The Future of AI in the Boardroom The role of AI and data in the boardroom will continue to grow. CEOs who embrace these technologies will be better positioned to lead their companies into a future of innovation, agility, and competitiveness. Real-time data and predictive analytics will become even more integral to decision-making, allowing business leaders to navigate complex markets with greater confidence. Conclusion Data and AI are transforming businesses, and CEOs are at the forefront of this revolution. By embracing digital transformation, they can improve decision-making, optimize operations, and stay competitive in an increasingly complex world. While challenges like rapid technological advancements, data privacy, and talent shortages remain, the benefits of adopting AI far outweigh the risks. CEOs who successfully lead their organizations through this transformation will unlock new opportunities for growth and innovation, ensuring their businesses remain competitive and relevant in the digital age. In the years ahead, AI will play a larger role in shaping corporate strategy. As the technology evolves, AI will provide even deeper insights, helping CEOs and boards make smarter, faster decisions. Companies that fully integrate AI into their operations will have a significant advantage over those that hesitate, gaining agility in adapting to new market conditions. 30

32. Articial Intelligence: Balancing Cybersecurity Risks and Defenses A the modern digital landscape. This article delves into how AI is contributing to increased cybersecurity risks while simultaneously bolstering defense mechanisms, highlighting the complex interplay between innovation and vulnerability in today's cyber realm. rtificial Intelligence (AI) stands at the forefront of both cybersecurity risks and defenses, embodying a dual role that shapes improperly transferring $25M to an account controlled by the attacker. Adversarial AI: Researchers have demonstrated the potential for AI algorithms to be manipulated or deceived, leading to adversarial attacks. These attacks exploit vulnerabilities in AI systems, causing them to misclassify data or make incorrect decisions, undermining the reliability of AI-based cybersecurity defenses. • Increasing Cyber Risks Privacy Concerns: AI-powered surveillance and data analysis tools raise concerns about privacy infringement. The collection and analysis of vast amounts of personal data can lead to unauthorized access, data breaches, and regulatory non- compliance, posing significant risks to individuals and organizations alike. AI's proliferation in cyber introduces novel risks and challenges that organizations must navigate. Examples include: • Sophisticated Cyberattacks: AI-driven tools can enhance the sophistication and efficiency of cyberattacks. Malicious actors utilize AI to automate tasks like reconnaissance, phishing, and malware deployment, making attacks and malware more targeted and difficult to detect. • AI's Role in Enhancing Cybersecurity Defenses Conversely, AI-driven technologies are instrumental in strengthening cybersecurity defenses, offering proactive measures to mitigate evolving threats: Social Engineering: AI can also make social engineering harder to detect. Phishing emails can be more tailored and contain fewer errors and “tells.” Even video and audio can be faked with AI. In one incident, an attacker used AI to make live deep fakes to impersonate top executives on video calls, thereby tricking an employee into • Threat Detection and Analysis: AI excels in detecting patterns and anomalies within vast datasets, enabling quicker identification of potential threats. Machine Learning algorithms • • can analyze network traffic, user behavior, and system logs in real-time, alerting security teams to suspicious activities promptly. addressed by courts and it may be a while before we have reliably answers. Skill Gap: Effective implementation of AI- powered cybersecurity requires skilled professionals capable of managing, interpreting, and refining AI systems. Bridging the skill gap through training and education is essential to maximizing the potential of AI in cybersecurity defenses. • Automated Response and Mitigation: AI automates incident response processes, allowing for rapid containment and mitigation of cyber threats. Automated systems can isolate compromised systems, update security configurations, and deploy patches to vulnerable software, reducing the window of opportunity for attackers. • Future Outlook Predictive Capabilities: AI's predictive analytics forecast potential cyber threats based on historical data and current trends. This proactive approach enables organizations to preemptively strengthen defenses, allocate resources effectively, and prioritize security measures based on identified risks. • Looking ahead, the evolution of AI in cybersecurity will continue to shape the landscape of digital resilience and vulnerability. Innovations in AI-driven threat detection, behavioral analytics, and automated response systems will redefine cybersecurity strategies, empowering organizations to combat emerging threats effectively. Challenges and Ethical Considerations Striking a balance between leveraging AI's capabilities to fortify defenses while mitigating inherent risks remains paramount. Embracing collaborative efforts among cybersecurity professionals, researchers, and policymakers will drive advancements in AI technologies that safeguard digital assets and uphold cybersecurity resilience. While AI presents significant opportunities for cybersecurity, several challenges and ethical considerations must be addressed: Bias and Fairness: AI algorithms can inadvertently perpetuate biases present in training data, leading to discriminatory outcomes in cybersecurity decisions. Ensuring fairness and transparency in AI models is crucial to mitigating these risks. • Conclusion In conclusion, Artificial Intelligence represents a pivotal force in the dual narrative of cybersecurity, both augmenting risks and fortifying defenses in today's interconnected digital ecosystem. Organizations must navigate this complex landscape with a nuanced understanding of AI's potential vulnerabilities and transformative capabilities. By harnessing AI-driven technologies responsibly, organizations can proactively defend against evolving cyber threats, uphold data integrity, and foster a resilient cybersecurity posture. Embracing ethical considerations, regulatory compliance, and continuous innovation will enable AI to fulfill its promise as a cornerstone of modern cybersecurity defenses, safeguarding businesses and individuals against the ever-evolving threat landscape. Regulatory Compliance: The deployment of AI in cybersecurity must adhere to regulatory frameworks governing data privacy, security standards, and ethical guidelines. Compliance ensures that AI technologies operate within legal boundaries and uphold user trust. • Intellectual Property: Use of AI raises difficult intellectual property problems. For example, if AI generates cybersecurity code, procedures, policies, or other documents in part, on another person's copyrighted works, does it violate their copyright? These questions have yet to be fully • 32

33. • can analyze network traffic, user behavior, and system logs in real-time, alerting security teams to suspicious activities promptly. addressed by courts and it may be a while before we have reliably answers. Articial Intelligence: Balancing Cybersecurity Risks and Defenses A the modern digital landscape. This article delves into how AI is contributing to increased cybersecurity risks while simultaneously bolstering defense mechanisms, highlighting the complex interplay between innovation and vulnerability in today's cyber realm. Skill Gap: Effective implementation of AI- powered cybersecurity requires skilled professionals capable of managing, interpreting, and refining AI systems. Bridging the skill gap through training and education is essential to maximizing the potential of AI in cybersecurity defenses. • Automated Response and Mitigation: AI automates incident response processes, allowing for rapid containment and mitigation of cyber threats. Automated systems can isolate compromised systems, update security configurations, and deploy patches to vulnerable software, reducing the window of opportunity for attackers. • Future Outlook Predictive Capabilities: AI's predictive analytics forecast potential cyber threats based on historical data and current trends. This proactive approach enables organizations to preemptively strengthen defenses, allocate resources effectively, and prioritize security measures based on identified risks. • Looking ahead, the evolution of AI in cybersecurity will continue to shape the landscape of digital resilience and vulnerability. Innovations in AI-driven threat detection, behavioral analytics, and automated response systems will redefine cybersecurity strategies, empowering organizations to combat emerging threats effectively. rtificial Intelligence (AI) stands at the forefront of both cybersecurity risks and defenses, embodying a dual role that shapes improperly transferring $25M to an account controlled by the attacker. Challenges and Ethical Considerations Striking a balance between leveraging AI's capabilities to fortify defenses while mitigating inherent risks remains paramount. Embracing collaborative efforts among cybersecurity professionals, researchers, and policymakers will drive advancements in AI technologies that safeguard digital assets and uphold cybersecurity resilience. Adversarial AI: Researchers have demonstrated the potential for AI algorithms to be manipulated or deceived, leading to adversarial attacks. These attacks exploit vulnerabilities in AI systems, causing them to misclassify data or make incorrect decisions, undermining the reliability of AI-based cybersecurity defenses. • While AI presents significant opportunities for cybersecurity, several challenges and ethical considerations must be addressed: Bias and Fairness: AI algorithms can inadvertently perpetuate biases present in training data, leading to discriminatory outcomes in cybersecurity decisions. Ensuring fairness and transparency in AI models is crucial to mitigating these risks. • Increasing Cyber Risks Conclusion Privacy Concerns: AI-powered surveillance and data analysis tools raise concerns about privacy infringement. The collection and analysis of vast amounts of personal data can lead to unauthorized access, data breaches, and regulatory non- compliance, posing significant risks to individuals and organizations alike. AI's proliferation in cyber introduces novel risks and challenges that organizations must navigate. Examples include: • In conclusion, Artificial Intelligence represents a pivotal force in the dual narrative of cybersecurity, both augmenting risks and fortifying defenses in today's interconnected digital ecosystem. Organizations must navigate this complex landscape with a nuanced understanding of AI's potential vulnerabilities and transformative capabilities. By harnessing AI-driven technologies responsibly, organizations can proactively defend against evolving cyber threats, uphold data integrity, and foster a resilient cybersecurity posture. Embracing ethical considerations, regulatory compliance, and continuous innovation will enable AI to fulfill its promise as a cornerstone of modern cybersecurity defenses, safeguarding businesses and individuals against the ever-evolving threat landscape. Sophisticated Cyberattacks: AI-driven tools can enhance the sophistication and efficiency of cyberattacks. Malicious actors utilize AI to automate tasks like reconnaissance, phishing, and malware deployment, making attacks and malware more targeted and difficult to detect. • Regulatory Compliance: The deployment of AI in cybersecurity must adhere to regulatory frameworks governing data privacy, security standards, and ethical guidelines. Compliance ensures that AI technologies operate within legal boundaries and uphold user trust. • AI's Role in Enhancing Cybersecurity Defenses Conversely, AI-driven technologies are instrumental in strengthening cybersecurity defenses, offering proactive measures to mitigate evolving threats: Social Engineering: AI can also make social engineering harder to detect. Phishing emails can be more tailored and contain fewer errors and “tells.” Even video and audio can be faked with AI. In one incident, an attacker used AI to make live deep fakes to impersonate top executives on video calls, thereby tricking an employee into • Intellectual Property: Use of AI raises difficult intellectual property problems. For example, if AI generates cybersecurity code, procedures, policies, or other documents in part, on another person's copyrighted works, does it violate their copyright? These questions have yet to be fully • Threat Detection and Analysis: AI excels in detecting patterns and anomalies within vast datasets, enabling quicker identification of potential threats. Machine Learning algorithms • 33 www.ciobusinessworld.com

34. • can analyze network traffic, user behavior, and system logs in real-time, alerting security teams to suspicious activities promptly. addressed by courts and it may be a while before we have reliably answers. Skill Gap: Effective implementation of AI- powered cybersecurity requires skilled professionals capable of managing, interpreting, and refining AI systems. Bridging the skill gap through training and education is essential to maximizing the potential of AI in cybersecurity defenses. • Automated Response and Mitigation: AI automates incident response processes, allowing for rapid containment and mitigation of cyber threats. Automated systems can isolate compromised systems, update security configurations, and deploy patches to vulnerable software, reducing the window of opportunity for attackers. • Future Outlook Predictive Capabilities: AI's predictive analytics forecast potential cyber threats based on historical data and current trends. This proactive approach enables organizations to preemptively strengthen defenses, allocate resources effectively, and prioritize security measures based on identified risks. • Looking ahead, the evolution of AI in cybersecurity will continue to shape the landscape of digital resilience and vulnerability. Innovations in AI-driven threat detection, behavioral analytics, and automated response systems will redefine cybersecurity strategies, empowering organizations to combat emerging threats effectively. Challenges and Ethical Considerations Striking a balance between leveraging AI's capabilities to fortify defenses while mitigating inherent risks remains paramount. Embracing collaborative efforts among cybersecurity professionals, researchers, and policymakers will drive advancements in AI technologies that safeguard digital assets and uphold cybersecurity resilience. While AI presents significant opportunities for cybersecurity, several challenges and ethical considerations must be addressed: Bias and Fairness: AI algorithms can inadvertently perpetuate biases present in training data, leading to discriminatory outcomes in cybersecurity decisions. Ensuring fairness and transparency in AI models is crucial to mitigating these risks. • Conclusion In conclusion, Artificial Intelligence represents a pivotal force in the dual narrative of cybersecurity, both augmenting risks and fortifying defenses in today's interconnected digital ecosystem. Organizations must navigate this complex landscape with a nuanced understanding of AI's potential vulnerabilities and transformative capabilities. By harnessing AI-driven technologies responsibly, organizations can proactively defend against evolving cyber threats, uphold data integrity, and foster a resilient cybersecurity posture. Embracing ethical considerations, regulatory compliance, and continuous innovation will enable AI to fulfill its promise as a cornerstone of modern cybersecurity defenses, safeguarding businesses and individuals against the ever-evolving threat landscape. Regulatory Compliance: The deployment of AI in cybersecurity must adhere to regulatory frameworks governing data privacy, security standards, and ethical guidelines. Compliance ensures that AI technologies operate within legal boundaries and uphold user trust. • Intellectual Property: Use of AI raises difficult intellectual property problems. For example, if AI generates cybersecurity code, procedures, policies, or other documents in part, on another person's copyrighted works, does it violate their copyright? These questions have yet to be fully • 34

36. Optimizing AI for health Enquiry: The 5 Key Ingredients Human-AI collaboration can counteract bias. AI systems that combine human expertise with machine learning not only improve performance but help address algorithmic bias. Humans provide context and equity, while AI adds speed and scale. This balanced approach prevents marginalized groups from being overlooked. A rtificial intelligence (AI) holds tremendous potential to improve healthcare and public health outcomes. But if not developed thoughtfully with equity in mind, AI risks exacerbating disparities. Here are five things to know about ensuring AI promotes health equity: Diversity in data and teams enables equity. To develop equitable AI, diverse real-world data and perspectives are needed. Inclusive data collection and having representative teams involved in design, validation, and evaluation helps build sensitivity to different populations into systems. AI can reveal healthcare disparities. By analyzing large, diverse health datasets, AI systems can uncover differences in access, treatment, and outcomes across demographics that may otherwise go unnoticed. For example, an algorithm may find racial disparities in cardiac care by mining electronic health records. These insights allow targeting of solutions to disadvantaged groups. Post-deployment audits uphold fairness. Ongoing testing for discrimination and regular algorithmic impact assessments after implementation are key to ensuring AI fairness in practice. This allows prompt identification and correction of emergent biases as systems are used in changing real environments. AI models can perpetuate bias. Because AI learns from data, models trained on historically biased datasets may disproportionately misdiagnose, underserve, or negatively impact marginalized populations. Studies have found automated systems that exhibit gender and race bias. Ongoing bias monitoring and mitigation is crucial throughout development and use. The increasing use of AI in medicine holds vast potential to improve care, but thoughtfully embedding equity considerations through its development and application is crucial to truly benefitting all patients. This requires conscientious effort, but the rewards will be more informed, just, and compassionate healthcare for all. 36

37. 37 www.ciobusinessworld.com

38. Articial Intelligence Human Intelligence? Taaleem, Director of Education or A most promising ventures, seeking answers to questions like: What to invest in? What new approach will increase profitability? What's the next unicorn? s we charge towards an uncertain future, Artificial Intelligence (AI) has become the primary focus of the tech world. Investors are keen to identify the students, and leaders. However, by having AI generate them, we often lose the processes that are essential in developing collaboration skills, research skills, deeper content knowledge, and more. Microsoft UAE General Manager, Naim Yazbeck, recently likened AI to electricity, emphasizing the substantial changes we will encounter in our lives with its advent. If we consider a world pre-AI and post-AI to be similar in terms of transition and change to the world pre-electricity and post-electricity, we realize the magnitude of this shift. Our children will experience a world completely foreign from the one we knew growing up or live in today, in almost every facet. In most industry sectors, the lion's share of investment is being poured into AI. As someone who has worked in education for most of my career, I can't help but ponder: if 80% of R&D funding is being pushed into the development of an artificial entity, what then for the development of human intelligence? Don't get me wrong; I am not a naysayer. I value the progress made with AI and acknowledge that it will be instrumental in our future. However, as we navigate this new road, there are some considerations we need to take. While change is the only constant, and this change is inevitable, it is crucial for every country, ministry, and organization to examine the potential change in detail and consider what they want to hold on to. What aspects of the curriculum should be retained? What learning and skills should remain part of the expectation of human intelligence? I'm not particularly bothered that post the invention of electricity most of us have not retained the knowledge required to make candles successfully, but I believe as we head into this change, we need to consider what skills and knowledge we do not wish to lose. Due to the rapid pace of change and developments in the race for the next new app, product, or breakthrough, organizations are drowning in examining the “what next.” One must ask if time is being appropriately spent to examine what we should look to replace and what we shouldn't. In what instances are we throwing out the baby with the bathwater? Where is the process more important than the product? We know the developments in the pipeline, and in some cases already in place, will increase efficiency and ensure better quality products than those produced by teachers, One significant concern is the potential decline in essential human skills. Critical thinking, creativity, and emotional intelligence are just a few areas where human intelligence currently excels over AI. While AI can process data at unimaginable speeds and offer solutions to complex problems, it lacks the innate ability to understand context, nuance, and human emotion fully. These are skills that have been honed through centuries of human experience and education. Education systems worldwide are already feeling the pressure to adapt to a more AI-centric world. Schools are integrating more technology into the classroom, and curricula are being updated to include coding and data analysis. While these changes are necessary, they should not come at the expense of traditional educational values. Reading, writing, and arithmetic are still foundational skills that every child needs. Additionally, subjects like history, philosophy, and the arts play a crucial role in developing well-rounded individuals who can think critically and appreciate the human experience. If we don't pump the brakes and examine each step of the way, if we land in a world with an overreliance on AI, that could lead to a diminished capacity for problem-solving. When students or leaders rely too heavily on AI to provide answers, we miss out on the critical thinking process involved in arriving at those answers. If we are not mindful, this could result in a generation of individuals who can operate advanced technologies but lack any understanding of the principles behind them. Additionally, ethical considerations must be addressed as AI continues to develop. Issues such as data privacy, algorithmic bias, and the transparency of AI decision- making processes are critical areas that require careful oversight. Ensuring that AI is developed and deployed responsibly will be essential to harnessing its benefits while mitigating its risks. The development of AI is undoubtedly exciting and holds immense potential benefits, however, it is crucial that we do not lose sight of the importance of human intelligence. Balancing the advancement of AI with the nurturing of human skills and knowledge is essential for creating a future where technology enhances rather than diminishes our lives. As we charge towards this uncertain future, let us invest not only in artificial intelligence but also in the limitless potential of human intelligence. -Glen Radojkovich 38

39. currently excels over AI. While AI can process data at unimaginable speeds and offer solutions to complex problems, it lacks the innate ability to understand context, nuance, and human emotion fully. These are skills that have been honed through centuries of human experience and education. Articial Intelligence Human Intelligence? Taaleem, Director of Education or Education systems worldwide are already feeling the pressure to adapt to a more AI-centric world. Schools are integrating more technology into the classroom, and curricula are being updated to include coding and data analysis. While these changes are necessary, they should not come at the expense of traditional educational values. Reading, writing, and arithmetic are still foundational skills that every child needs. Additionally, subjects like history, philosophy, and the arts play a crucial role in developing well-rounded individuals who can think critically and appreciate the human experience. A most promising ventures, seeking answers to questions like: What to invest in? What new approach will increase profitability? What's the next unicorn? If we don't pump the brakes and examine each step of the way, if we land in a world with an overreliance on AI, that could lead to a diminished capacity for problem-solving. When students or leaders rely too heavily on AI to provide answers, we miss out on the critical thinking process involved in arriving at those answers. If we are not mindful, this could result in a generation of individuals who can operate advanced technologies but lack any understanding of the principles behind them. s we charge towards an uncertain future, Artificial Intelligence (AI) has become the primary focus of the tech world. Investors are keen to identify the students, and leaders. However, by having AI generate them, we often lose the processes that are essential in developing collaboration skills, research skills, deeper content knowledge, and more. Microsoft UAE General Manager, Naim Yazbeck, recently likened AI to electricity, emphasizing the substantial changes we will encounter in our lives with its advent. If we consider a world pre-AI and post-AI to be similar in terms of transition and change to the world pre-electricity and post-electricity, we realize the magnitude of this shift. Our children will experience a world completely foreign from the one we knew growing up or live in today, in almost every facet. In most industry sectors, the lion's share of investment is being poured into AI. As someone who has worked in education for most of my career, I can't help but ponder: if 80% of R&D funding is being pushed into the development of an artificial entity, what then for the development of human intelligence? Don't get me wrong; I am not a naysayer. I value the progress made with AI and acknowledge that it will be instrumental in our future. However, as we navigate this new road, there are some considerations we need to take. Additionally, ethical considerations must be addressed as AI continues to develop. Issues such as data privacy, algorithmic bias, and the transparency of AI decision- making processes are critical areas that require careful oversight. Ensuring that AI is developed and deployed responsibly will be essential to harnessing its benefits while mitigating its risks. While change is the only constant, and this change is inevitable, it is crucial for every country, ministry, and organization to examine the potential change in detail and consider what they want to hold on to. What aspects of the curriculum should be retained? What learning and skills should remain part of the expectation of human intelligence? I'm not particularly bothered that post the invention of electricity most of us have not retained the knowledge required to make candles successfully, but I believe as we head into this change, we need to consider what skills and knowledge we do not wish to lose. The development of AI is undoubtedly exciting and holds immense potential benefits, however, it is crucial that we do not lose sight of the importance of human intelligence. Balancing the advancement of AI with the nurturing of human skills and knowledge is essential for creating a future where technology enhances rather than diminishes our lives. As we charge towards this uncertain future, let us invest not only in artificial intelligence but also in the limitless potential of human intelligence. Due to the rapid pace of change and developments in the race for the next new app, product, or breakthrough, organizations are drowning in examining the “what next.” One must ask if time is being appropriately spent to examine what we should look to replace and what we shouldn't. In what instances are we throwing out the baby with the bathwater? Where is the process more important than the product? We know the developments in the pipeline, and in some cases already in place, will increase efficiency and ensure better quality products than those produced by teachers, One significant concern is the potential decline in essential human skills. Critical thinking, creativity, and emotional intelligence are just a few areas where human intelligence -Glen Radojkovich 39 www.ciobusinessworld.com

40. currently excels over AI. While AI can process data at unimaginable speeds and offer solutions to complex problems, it lacks the innate ability to understand context, nuance, and human emotion fully. These are skills that have been honed through centuries of human experience and education. Education systems worldwide are already feeling the pressure to adapt to a more AI-centric world. Schools are integrating more technology into the classroom, and curricula are being updated to include coding and data analysis. While these changes are necessary, they should not come at the expense of traditional educational values. Reading, writing, and arithmetic are still foundational skills that every child needs. Additionally, subjects like history, philosophy, and the arts play a crucial role in developing well-rounded individuals who can think critically and appreciate the human experience. If we don't pump the brakes and examine each step of the way, if we land in a world with an overreliance on AI, that could lead to a diminished capacity for problem-solving. When students or leaders rely too heavily on AI to provide answers, we miss out on the critical thinking process involved in arriving at those answers. If we are not mindful, this could result in a generation of individuals who can operate advanced technologies but lack any understanding of the principles behind them. Additionally, ethical considerations must be addressed as AI continues to develop. Issues such as data privacy, algorithmic bias, and the transparency of AI decision- making processes are critical areas that require careful oversight. Ensuring that AI is developed and deployed responsibly will be essential to harnessing its benefits while mitigating its risks. The development of AI is undoubtedly exciting and holds immense potential benefits, however, it is crucial that we do not lose sight of the importance of human intelligence. Balancing the advancement of AI with the nurturing of human skills and knowledge is essential for creating a future where technology enhances rather than diminishes our lives. As we charge towards this uncertain future, let us invest not only in artificial intelligence but also in the limitless potential of human intelligence. -Glen Radojkovich 40

42. individual students, making education more inclusive and personalized. Emerging leaders are developing adaptive learning technologies that adjust to students' needs, ensuring that everyone can progress at their own pace. AI is also playing a pivotal role in the fight against climate change. Innovators are using AI to optimize energy usage, predict climate patterns, and develop sustainable solutions for industries. These leaders recognize that AI can help reduce environmental impact, providing tools for a more sustainable future. bring unique insights into the challenges of algorithmic bias and inequality. Their experiences push the AI industry to create solutions that benefit all communities, driving social good alongside technological advancement. Key Skills of Tomorrow's Leaders The emerging AI leaders are not just technically proficient; they are creative, adaptable, and lifelong learners. The rapid pace of AI development requires leaders who can quickly adapt to new technologies and ideas. They are also strong communicators, capable of explaining complex AI concepts in a way that is accessible to non-technical stakeholders, including policymakers, investors, and the public. Diversity in Leadership One of the most exciting trends in the next generation of AI leaders is the increasing diversity in the field. Historically, technology leadership has lacked representation, but that's changing as more women, people of color, and individuals from diverse backgrounds step into leadership roles. Organizations like Black in AI and Women in Machine Learning are fostering diversity and inclusion, ensuring that AI is shaped by a broader range of perspectives. Collaboration is another critical skill. The most successful AI projects are interdisciplinary, requiring input from data scientists, engineers, ethicists, and business strategists. Emerging leaders excel at bringing together these diverse teams, ensuring that AI initiatives are aligned with both technological capabilities and business objectives. This diversity is vital for creating more equitable AI systems. Leaders from underrepresented backgrounds often The Role of Startups and Academia like healthcare, criminal justice, and autonomous vehicles. Leaders must navigate questions around fairness, accountability, and transparency to ensure AI is developed responsibly. The Next Generation of Data and AI Leaders: Emerging Innovators A innovation. These emerging innovators are using AI and data to drive advancements in sectors like healthcare, education, and sustainability, while also addressing ethical challenges. Their interdisciplinary approach, combined with a focus on responsible AI, is redefining what it means to lead in the digital age. Startups and academic institutions are playing a major role in nurturing the next generation of AI leaders. AI startups, often supported by incubators and venture capital, are driving innovation by focusing on niche problems and developing specialized solutions. These environments encourage risk-taking and experimentation, which are essential for advancing the capabilities of AI. There's also the need to advocate for regulations that govern the responsible use of AI. Emerging leaders will play a key role in shaping policies that protect consumer rights, promote innovation, and address the unintended consequences of AI-driven automation. Academia, too, remains a key source of leadership. Universities and research institutions are at the forefront of AI development, producing leaders who combine theoretical knowledge with practical applications. Through partnerships with industry, academic leaders are ensuring that AI research has real-world impact, addressing societal challenges and contributing to technological progress. Conclusion The next generation of data and AI leaders is driving innovation across industries while addressing the ethical challenges posed by these powerful technologies. These emerging leaders are defined by their technical expertise, creative problem-solving, and commitment to diversity and social good. As they continue to push the boundaries of AI and data science, they are setting the stage for a future where technology not only transforms industries but also benefits society as a whole. s artificial intelligence (AI) and data science continue to transform industries, a new generation of leaders is emerging, shaping the future of emerging leaders embrace collaboration and agility, key traits for navigating a constantly evolving field. Technical skills are critical, but successful AI leaders also have a strong grasp of the ethical and societal impacts of their work. As AI becomes more ingrained in daily life, issues like fairness, transparency, and accountability have come to the forefront. Leaders must not only focus on the potential of AI but also on how it can be used responsibly to benefit society. The Challenges Ahead As AI continues to evolve, the next generation of leaders will face new challenges. Ethical considerations will become even more critical as AI is applied to sensitive areas The Changing Role of Leadership in AI Innovators Transforming Industries Today's AI leaders are distinguished by their ability to combine technical expertise with a strategic vision. They understand that digital transformation is more than just adopting new technologies; it's about embedding data and AI into the core of business operations to improve decision- making and foster innovation. Unlike traditional leadership, which often relied on hierarchical decision-making, these In healthcare, AI is revolutionizing the way diseases are diagnosed, treatments are personalized, and drugs are discovered. Innovators like Fei-Fei Li, co-director of Stanford's Human-Centered AI Institute, are championing the ethical use of AI in healthcare, ensuring that technology enhances care while respecting patient rights. In education, AI is helping tailor learning experiences to 42

43. The Role of Startups and Academia individual students, making education more inclusive and personalized. Emerging leaders are developing adaptive learning technologies that adjust to students' needs, ensuring that everyone can progress at their own pace. AI is also playing a pivotal role in the fight against climate change. Innovators are using AI to optimize energy usage, predict climate patterns, and develop sustainable solutions for industries. These leaders recognize that AI can help reduce environmental impact, providing tools for a more sustainable future. bring unique insights into the challenges of algorithmic bias and inequality. Their experiences push the AI industry to create solutions that benefit all communities, driving social good alongside technological advancement. like healthcare, criminal justice, and autonomous vehicles. Leaders must navigate questions around fairness, accountability, and transparency to ensure AI is developed responsibly. Startups and academic institutions are playing a major role in nurturing the next generation of AI leaders. AI startups, often supported by incubators and venture capital, are driving innovation by focusing on niche problems and developing specialized solutions. These environments encourage risk-taking and experimentation, which are essential for advancing the capabilities of AI. Key Skills of Tomorrow's Leaders There's also the need to advocate for regulations that govern the responsible use of AI. Emerging leaders will play a key role in shaping policies that protect consumer rights, promote innovation, and address the unintended consequences of AI-driven automation. The emerging AI leaders are not just technically proficient; they are creative, adaptable, and lifelong learners. The rapid pace of AI development requires leaders who can quickly adapt to new technologies and ideas. They are also strong communicators, capable of explaining complex AI concepts in a way that is accessible to non-technical stakeholders, including policymakers, investors, and the public. Academia, too, remains a key source of leadership. Universities and research institutions are at the forefront of AI development, producing leaders who combine theoretical knowledge with practical applications. Through partnerships with industry, academic leaders are ensuring that AI research has real-world impact, addressing societal challenges and contributing to technological progress. Diversity in Leadership Conclusion One of the most exciting trends in the next generation of AI leaders is the increasing diversity in the field. Historically, technology leadership has lacked representation, but that's changing as more women, people of color, and individuals from diverse backgrounds step into leadership roles. Organizations like Black in AI and Women in Machine Learning are fostering diversity and inclusion, ensuring that AI is shaped by a broader range of perspectives. The next generation of data and AI leaders is driving innovation across industries while addressing the ethical challenges posed by these powerful technologies. These emerging leaders are defined by their technical expertise, creative problem-solving, and commitment to diversity and social good. As they continue to push the boundaries of AI and data science, they are setting the stage for a future where technology not only transforms industries but also benefits society as a whole. Collaboration is another critical skill. The most successful AI projects are interdisciplinary, requiring input from data scientists, engineers, ethicists, and business strategists. Emerging leaders excel at bringing together these diverse teams, ensuring that AI initiatives are aligned with both technological capabilities and business objectives. The Challenges Ahead As AI continues to evolve, the next generation of leaders will face new challenges. Ethical considerations will become even more critical as AI is applied to sensitive areas This diversity is vital for creating more equitable AI systems. Leaders from underrepresented backgrounds often 43 www.ciobusinessworld.com

44. The Role of Startups and Academia like healthcare, criminal justice, and autonomous vehicles. Leaders must navigate questions around fairness, accountability, and transparency to ensure AI is developed responsibly. Startups and academic institutions are playing a major role in nurturing the next generation of AI leaders. AI startups, often supported by incubators and venture capital, are driving innovation by focusing on niche problems and developing specialized solutions. These environments encourage risk-taking and experimentation, which are essential for advancing the capabilities of AI. There's also the need to advocate for regulations that govern the responsible use of AI. Emerging leaders will play a key role in shaping policies that protect consumer rights, promote innovation, and address the unintended consequences of AI-driven automation. Academia, too, remains a key source of leadership. Universities and research institutions are at the forefront of AI development, producing leaders who combine theoretical knowledge with practical applications. Through partnerships with industry, academic leaders are ensuring that AI research has real-world impact, addressing societal challenges and contributing to technological progress. Conclusion The next generation of data and AI leaders is driving innovation across industries while addressing the ethical challenges posed by these powerful technologies. These emerging leaders are defined by their technical expertise, creative problem-solving, and commitment to diversity and social good. As they continue to push the boundaries of AI and data science, they are setting the stage for a future where technology not only transforms industries but also benefits society as a whole. The Challenges Ahead As AI continues to evolve, the next generation of leaders will face new challenges. Ethical considerations will become even more critical as AI is applied to sensitive areas 44

46. Building Awareness ? the Implicatƣns of AI Usage in A with great power comes great responsibility, and the rise of AI in workplaces has introduced significant security concerns that need to be addressed. In this article, we will explore the critical need for awareness about the implications of AI usage, both within organizations and in personal use, emphasizing the importance of open discussions and incorporating AI pitfalls into security awareness programs. Organizatƣns promising efficiency and innovation. However, - May Brooks-Kempler rtificial Intelligence (AI) has become an integral part of modern organizational operations, Data Leakage Beyond Public AI Tools Data leakage is a multifaceted problem exacerbated by AI. It's not just about posting sensitive information on public AI platforms. For example, an employee might input proprietary data into an AI tool to get insights, not realizing that this action could lead to unauthorized access or misuse. However, even when organizations implement internal AI tools, doesn't completely solve the problem. The Rise of Shadow AI One of the basic principles of information security is maintaining information confidentiality. Part of that is implementing access controls including Need-to-Know controls. Internal AI modules do not currently include need-to-know restrictions, meaning that if a data analyst and a CEO ask the AI tool a question, they will get the same data. The biggest issue with that is the lack of understanding some of the employees may have on the implications of sharing data received. In the M&A world, a premature publication of a potential M&A may become the kiss of death for the project. People who are read in to the potential transaction are well aware of it. However, if people who are not in that inner-circle learn about it because of AI indiscretions, they may not understand how sharing that data can have catastrophic consequences. Shadow IT—where employees use unauthorized applications and devices—is a well-known challenge. Now, we face a new frontier: shadow AI. Take for example the use of AI companions during virtual calls. Many people are now relying on these tools to summarize meetings and generate clear action items. Un-authorized use of such tools by employees, contractors and other third parties, potentially leads to data being sent to uncontrolled accounts. This practice poses a significant security risk, as sensitive organizational data could be exposed through unmonitored channels. Organizations must recognize this threat and implement measures to detect and mitigate shadow AI usage. Blind Trust in AI Responses Building a Culture of AI Awareness One of the most pressing issues is the blind trust users place in data produced by AI. AI tools are designed to simulate human-like responses, often leading users to accept these outputs without question. This uncritical acceptance can result in flawed decision-making. Trying to stop use of AI is, in my opinion, similar to taking a horse and buggy to work. We cannot and should not stop innovation, meaning that we have to foster a culture of awareness and vigilance which will allow our organizations to embrace innovation while protecting the organization. AI bias and misinformation is one element, however, even when AI gives us correct answers, we should not blindly follow them. AI doesn't invent new ideas, it's not creative, it simply repeats things that were given to it. For example if company A is using AI to generate a marketing plan. The Company A team is challenging the AI tool again and again, perfecting the plan to its needs. Later on company B asks AI to generate a marketing plan. They receive a great output, based on company A's reiterations. However, if they blindly follow the recipe, they will still probably be a few steps behind company A, and once company A releases its campaign, company B will realize they have very little to show for. The key to embracing safe AI usage is awareness. Incorporate AI pitfalls into existing security awareness programs. Educate employees about the potential risks of blindly trusting AI responses and the implications of sharing data with AI tools. Make sure that people exposed to sensitive data understand AI risks pertaining to use and disclosure of such information. And most importantly, encourage open dialogues about AI usage within the organization. Create forums where employees can share their experiences, concerns, needs and best practices regarding AI tools. 46

47. Building Awareness ? the Implicatƣns of AI Usage in A with great power comes great responsibility, and the rise of AI in workplaces has introduced significant security concerns that need to be addressed. In this article, we will explore the critical need for awareness about the implications of AI usage, both within organizations and in personal use, emphasizing the importance of open discussions and incorporating AI pitfalls into security awareness programs. The Rise of Shadow AI One of the basic principles of information security is maintaining information confidentiality. Part of that is implementing access controls including Need-to-Know controls. Internal AI modules do not currently include need-to-know restrictions, meaning that if a data analyst and a CEO ask the AI tool a question, they will get the same data. The biggest issue with that is the lack of understanding some of the employees may have on the implications of sharing data received. In the M&A world, a premature publication of a potential M&A may become the kiss of death for the project. People who are read in to the potential transaction are well aware of it. However, if people who are not in that inner-circle learn about it because of AI indiscretions, they may not understand how sharing that data can have catastrophic consequences. Organizatƣns promising efficiency and innovation. However, Shadow IT—where employees use unauthorized applications and devices—is a well-known challenge. Now, we face a new frontier: shadow AI. Take for example the use of AI companions during virtual calls. Many people are now relying on these tools to summarize meetings and generate clear action items. Un-authorized use of such tools by employees, contractors and other third parties, potentially leads to data being sent to uncontrolled accounts. This practice poses a significant security risk, as sensitive organizational data could be exposed through unmonitored channels. Organizations must recognize this threat and implement measures to detect and mitigate shadow AI usage. - May Brooks-Kempler Blind Trust in AI Responses Building a Culture of AI Awareness rtificial Intelligence (AI) has become an integral part of modern organizational operations, One of the most pressing issues is the blind trust users place in data produced by AI. AI tools are designed to simulate human-like responses, often leading users to accept these outputs without question. This uncritical acceptance can result in flawed decision-making. Trying to stop use of AI is, in my opinion, similar to taking a horse and buggy to work. We cannot and should not stop innovation, meaning that we have to foster a culture of awareness and vigilance which will allow our organizations to embrace innovation while protecting the organization. AI bias and misinformation is one element, however, even when AI gives us correct answers, we should not blindly follow them. AI doesn't invent new ideas, it's not creative, it simply repeats things that were given to it. For example if company A is using AI to generate a marketing plan. The Company A team is challenging the AI tool again and again, perfecting the plan to its needs. Later on company B asks AI to generate a marketing plan. They receive a great output, based on company A's reiterations. However, if they blindly follow the recipe, they will still probably be a few steps behind company A, and once company A releases its campaign, company B will realize they have very little to show for. The key to embracing safe AI usage is awareness. Incorporate AI pitfalls into existing security awareness programs. Educate employees about the potential risks of blindly trusting AI responses and the implications of sharing data with AI tools. Make sure that people exposed to sensitive data understand AI risks pertaining to use and disclosure of such information. And most importantly, encourage open dialogues about AI usage within the organization. Create forums where employees can share their experiences, concerns, needs and best practices regarding AI tools. Data Leakage Beyond Public AI Tools Data leakage is a multifaceted problem exacerbated by AI. It's not just about posting sensitive information on public AI platforms. For example, an employee might input proprietary data into an AI tool to get insights, not realizing that this action could lead to unauthorized access or misuse. However, even when organizations implement internal AI tools, doesn't completely solve the problem. 47 www.ciobusinessworld.com

48. The Rise of Shadow AI One of the basic principles of information security is maintaining information confidentiality. Part of that is implementing access controls including Need-to-Know controls. Internal AI modules do not currently include need-to-know restrictions, meaning that if a data analyst and a CEO ask the AI tool a question, they will get the same data. The biggest issue with that is the lack of understanding some of the employees may have on the implications of sharing data received. In the M&A world, a premature publication of a potential M&A may become the kiss of death for the project. People who are read in to the potential transaction are well aware of it. However, if people who are not in that inner-circle learn about it because of AI indiscretions, they may not understand how sharing that data can have catastrophic consequences. Shadow IT—where employees use unauthorized applications and devices—is a well-known challenge. Now, we face a new frontier: shadow AI. Take for example the use of AI companions during virtual calls. Many people are now relying on these tools to summarize meetings and generate clear action items. Un-authorized use of such tools by employees, contractors and other third parties, potentially leads to data being sent to uncontrolled accounts. This practice poses a significant security risk, as sensitive organizational data could be exposed through unmonitored channels. Organizations must recognize this threat and implement measures to detect and mitigate shadow AI usage. Blind Trust in AI Responses Building a Culture of AI Awareness One of the most pressing issues is the blind trust users place in data produced by AI. AI tools are designed to simulate human-like responses, often leading users to accept these outputs without question. This uncritical acceptance can result in flawed decision-making. Trying to stop use of AI is, in my opinion, similar to taking a horse and buggy to work. We cannot and should not stop innovation, meaning that we have to foster a culture of awareness and vigilance which will allow our organizations to embrace innovation while protecting the organization. AI bias and misinformation is one element, however, even when AI gives us correct answers, we should not blindly follow them. AI doesn't invent new ideas, it's not creative, it simply repeats things that were given to it. For example if company A is using AI to generate a marketing plan. The Company A team is challenging the AI tool again and again, perfecting the plan to its needs. Later on company B asks AI to generate a marketing plan. They receive a great output, based on company A's reiterations. However, if they blindly follow the recipe, they will still probably be a few steps behind company A, and once company A releases its campaign, company B will realize they have very little to show for. The key to embracing safe AI usage is awareness. Incorporate AI pitfalls into existing security awareness programs. Educate employees about the potential risks of blindly trusting AI responses and the implications of sharing data with AI tools. Make sure that people exposed to sensitive data understand AI risks pertaining to use and disclosure of such information. And most importantly, encourage open dialogues about AI usage within the organization. Create forums where employees can share their experiences, concerns, needs and best practices regarding AI tools. 48

50. Capturing the SUCCESS JOURNEY OF BUSINESS LEADERS DON’T MISS AN ISSUE www.ciobusinessworld.com