Utilization of Artificial Neural Networks in the Prevention of Child Abuse By Jeffrey D. Jacob

1. Utilization of Artificial Neural Networks in the Prevention of Child Abuse By Jeffrey D. Jacob

2. What is an Artificial Neural Network?

3. How can an Artificial Neural Network help in the prediction and prevention of child abuse? This presentation is dependent upon the ASSUMPTION that: Child Abuse, in its varying degrees, is always a “deterministic output” which is always preceded by a common pattern of variables. This is true, even when the child abuse incident seems to be a “random” act. If this ASSUMPTION is true, then an Artificial Neural Network, which has proven to be an extremely useful method[1][2] of accurately determining pattern recognition, could potentially be a powerful tool in recognizing the common patterns of variables that precede child abuse, and predict the potential for abuse with a high degree of accuracy.

4. Possible Roadmap For The Creation of a Neural Network That Can Predict Child Abuse 5 4 3 2 1

5. Development Life Cycle of CAP-ABUSE-NET The artificial neural network will be denoted as: Correlation-Analysis-Prediction of Abuse Network or CAP-ABUSE-NET Test CAP-ABUSE-NET Acquire 2nd data set to test and compare predicted results. Re-train if necessary. Train CAP-ABUSE-NET Use acquired data to assign synaptic weights and train data 5 Outputs & Descriptors Define “Predicted Output Identifiers” and “Child abuse Descriptors/Inhibitors 4 Live Data Acquisition Acquire real world data and reorient and redefine TCAP 3 1 2 Theory Definition Define Theory of Child Abuse Prediction (TCAP) Development Life Cycle To Prototype: 10-12 Months

6. Funding of CAP-ABUSE-NET • There will be no cost to the Los Angeles County Taxpayer! • A 501(c)(3) non-profit entity, Creative Cyber Systems ( C2Systems ) will be formed. • C2Systems will fund the project. • C2Systems will offer a platform through which scientists, information technology experts, etc. can donate their knowledge and time to building CAP-ABUSE-NET • All the science and technology will be open source, for any municipality to use at no charge • The anticipated cost to the non-profit entity is projected to be under $500,000

7. MILESTONE 1: Define Theory of Child Abuse Prediction The available academic literature indicates that there are four main areas of abuse that can be categorized [3]: PHYSICAL – SEXUAL – PSYCHOLOGICAL/EMOTIONAL – NEGLECT Within each category, there are varying degrees, which will need to be decomposed into the output neurons and labeled “Predicted Output Identifiers.” Because of the critical nature of this endeavor all pride, assumptions, prejudices, and bias must be sacrificed in favor of determining the Output neurons that most accurately represent the realities of child abuse!

8. MILESTONE 2: Live Data Acquisition Developers will reorient and redefine “Theory of Child Abuse Prediction” as necessary based on the acquired data using John Boyd’s Observation-Orientation-Decision-Action (OODA) Loop [4].

9. MILESTONE 3: Define Outputs & Descriptors The goal is to hire 65-75 post-graduate college students for the summer. They will attend a 3 day training symposium that instructs the decomposition of WIC 300 petitions*, social worker reports*, social worker case notes*, dismissed WIC 300 petitions*, cases where “substantiated” allegations changed to “unfounded”* and other data* into definitive “Predicted Output Identifiers” (PRE-IDs) and further define Child Abuse Descriptors / Inhibitors (CHILD-ADs / CHILD-AIs) for each PRE-ID. Slides 8,9,10 that follow represent example PRE-IDs, CHILD-ADs, and CHILD-IDs extracted from the live data. *It is assumed that any identifying information as to child’s / parent’s name, address, will be removed before data provided.

10. MILESTONE 3: Define Outputs & Descriptors Cont’d Example : Predicted Output Identifier (PRE-IDs)

11. MILESTONE 3: Define Outputs & Descriptors Cont’d Example : Child Abuse Descriptors (CHILD-ADs) Example : Child Abuse Inhibitors (CHILD-IDs)

12. MILESTONE 3: Define Outputs & Descriptors Cont’d Example : Additional Child Abuse Descriptors (CHILD-ADs)

13. MILESTONE 4: Train Model for CAP-ABUSE-NET Once the data is decomposed and compiled into Child Abuse Descriptors & Inhibitors (CHILD-ADs / CHILD-IDs), these inputs will be fed into a decision neuron. This decision neuron can have any number of inputs from one to n, where n is the total number of inputs. The inputs may be represented  therefore as x1, x2, x3… xn. And the corresponding weights for the inputs as w1, w2, w3… wn. The summation of the weights multiplied by the inputs be written as x1w1 + x2w2 + x3w3 …. + xnwn,  which is the activation value. This may also be mathematically represented by the following equation: If activation value u is greater than threshold value θ, the decision neuron outputs 1. If activation value u is less than threshold value θ, the decision neuron outputs 0.

14. MILESTONE 4: Train Model for CAP-ABUSE-NET Cont’d The feed forward process can be represented by the following diagram where x0, x1, x2… xn represent inputs CHILD-AD / CHILD-ID and the corresponding weights for the inputs as wk0, wk1, wk2… wkn ( k can be implemented as a bias factor). Output as yk will represent Predicted Output Identifiers (PRE-IDs) which will either be 1 or 0 (1=Positive Prediction; 0=Negative Prediction)

15. MILESTONE 5: Test Model for CAP-ABUSE-NET Once CAP-ABUSE-NET has been trained, a second segment of data should be fed into the network and the yk and the positive/negative Predicted Output Identifiers (PRE-IDs) should be compared to the actual positive/negative results (Vk) of the test data. (Model can be re-trained as necessary)

16. Practical Implementation of CAP-ABUSE-NET The CAP-ABUSE-NET should be packaged into a simple smart phone, desktop, or web application format that correlates simple form based input to its respective CHILD-ADs / CHILD-IDs. This can be accomplished by posing simple survey questions to case workers with confidence values (can also be programmed into a playful format that children can answer): The survey should not take longer than 15 minutes per incident visit to perform.

17. CAP-ABUSE-NET Accountability Feature • CAP-ABUSE-NET can be programmed to with the ability to allow an export function whereby two xml encoded files containing the results of any IDABUSE jobs (containing all +/- results of per PRE-IDs) may be uploaded into the case worker’s management system and attached to the respective record of the child. • One is confidential and for use only by DCFS (IDABUSE-Confidential.xml) and the other may be provided to an oversight board for accountability purposes (IDABUSE-oversight.xml) • IDABUSE Job – Confidential • For DCFS use ONLY • Contains identifying information in IDABUSE_Confidential.xml output file • IDABUSE Job – Oversight • For Oversight usage if necessary • Contains no person identifying information in IDABUSE_Oversight.xml output file

18. What CAP-ABUSE-NET is Not and Should Not Become • Providing a Case Worker with the CAP-ABUSE-NET tool SHOULD NOT add an unnecessary burden through either a steep learning curve or time required to use the tool. • The tool should be a separate and standalone utility from the Case Worker’s management system to avoid integration headaches. • It is NOT part of the Case Worker’s management system. • The tool will exist as a smart phone, desktop, or web application for the Case Worker, as well as a reader (sanitized of identifying information) for any required oversight board.

19. Final Thoughts The social workers are the ultimate neural network. They are responsible for accurately predicting the potential for child abuse and inhibiting the manifestation of actual abuse thousands of times per year. When they get it right, they are rarely, if ever congratulated or thanked. When they get it wrong, irrespective of insufficient predictive information or just a bad judgment, the thousands of correct judgments and children they have protected up to that point, mean nothing, as all of the focus shifts to the one wrong/bad decision. Any technology or utility that is designed to predict a future likelihood of child abuse should exist simply as a tool for a case worker to utilize in appropriate situations; It should not add an undue burden to an already difficult job!

20. Bibliography Johnson, G. E., Mimic Nets, IEEE Transactions on Neural Networks, Vol. 4, No. 5, September 1993, pp. 803-815. Patterson, D. W., Artificial Neural Networks, Prentice Hall, Singapore, 1996. Finkelhorand Baron 1986; Beitchman et al. 1991; Russell 1986; Peters 1988; Mullen et al. 1993. Various Child Abuse Publications Boyd, John R. Destruction and Creation, US Army Command and General Staff College, September 3, 1976