Trust-based Decision-Making for Energy-Aware Device Management

1. Trust-based Decision-Making forEnergy-Aware Device Management Stephan Hammer, Michael Wißner,and Elisabeth André Human Centered Multimedia Augsburg University, Germany

2. Motivation Smart environmentthat is able to support users in saving energy by proactively performing energy-aware adaptations. HomeMatic CCU tocontrolelectronic appliances • Sensors: • to recognize situations such as „user leaves room and light is on“ • Examples: • Smart Plugs • Brightness • Ultrasound Displays

3. Motivation Problem: If the system performs an adaptation which: • the users do not understand, • the users consider inconvenient, • makes the users feel they are no longer in control, • … then the users’ trust in the system might be impaired, resulting in lesser acceptance of and, in the worst case, disuse of the system.

4. Our Goals • Develop a user model, which: • chooses adequate actions to reduce energy consumption • models user trust in adaptive environments • chooses that action that will result in the highest user trust • User Trust Model (UTM) • Initialize the UTM with data gathered in an online survey • Evaluate users’ experience, acceptance, and trust towards a system that uses the UTM in a real setting

5. Building theUTM - Whatis „Trust“? • Trust is a very subjective concept • Trust is a non-deterministic concept • Trust is a multi-dimensional concept: • Comfort of use • Controllability • Transparency • Reliability • Security • Credibility • Seriousness

6. Building theUTM – Example: Device = Light Application-specific layer Generic part (applicable for different kinds of self-adaptive systems) [1]

7. Initializing the UTM – GatheringEmpirical Data • Online survey (38 Participants) • Descriptions of concrete system reactions in concrete situations • Example: “You leave your desk for a short time (for example to get something from a shelf) and your display is switched off automatically.” Switch off Display AskTo Switch off Display Via Mobile Phone Do Nothing Switch off Light Ask Via Mobile Phone Ask Via Display Do Nothing

8. Initializing the UTM – GatheringEmpirical Data • Online study (38 Participants) • Descriptions of concrete system reactions in concrete situations • Example: “You leave your desk for a short time (e.g. to get something from a shelf) and your display is switched off automatically.” • Ratings for the following statements (5-point Likert scale): • Q1: I understood why the system was reacting in this way. • Q2: I had control over the system. • Q3: I found the system comfortable to use.

9. Initializing the UTM Questions1-3 =>

10. Evaluating the UTM – User Study 24 Participants (18 male, 6 female, Age: 23-33) Setting: • “Typical” day in an office • Different tasks • Changing context • After each system reaction: • Transparency, User Control, Comfort of Use, Trust • Preferred system action • User Experience and User Trust

11. Evaluating the UTM – Results Ratings on a 5-point LikertScale • System actions (Light): • Consistently high ratings concerning Transparency, Controllability, Comfort of Use and Trust • Lowest average rating (M: 3.92, SD: .86): • Criterion: Trust • Situation: User is leaving the room • System action: Ask to switch the light off via the user’s mobile phone • Reason: No Feedback on Phone • System actions and users’ preferences differed • Reason: Repeated confirmations of system actions via the mobile phone are uncomfortable and obtrusive.

12. Evaluating the UTM – Results Ratings on a 5-point LikertScale • System actions (Display): • System reactions matched the users’ preferences in all situations • Users wanted the system to decide autonomously • Only moderate ratings concerning Controllability (M: 2.5 – 3.46) • Lower ratings concerning Trust (M: 3.63 – 3.88) • Reasons: No Feedback when leaving, No authentication mechanism when arriving • Still high ratings concerning Transparency (M: 3.79 – 5.0) and Comfort of Use (M: 4.0 – 4.58)

13. Evaluating the UTM – Further Results • Participants were satisfied (M: 3.96; SD: .68) • Participants did not feel: • distracted (M: 2.00; SD: 1.00) • restricted (M: 1.83; SD: 1.07) • observed (M: 2.33; SD: 1.18) • Participants acknowledged that the system: • supported them in saving energy (M: 4.71; SD: .54) • behaved adequately (M: 4.38; SD: .70) • was unobtrusive (M: 3.71; SD: 1.10) • was transparent (M: 4.96; SD: .20)

14. Conclusion • User Trust Model (UTM): • Generic approach for trust-based decision-making for the adaptation of smart environments • Based on an empirically grounded Bayesian Network which aims at maintaining user trust • Construction, initialization with empirical data, integration in an office setting • User Study: • UTM succeeded in maintaining users’ trust in a smart office environment

15. Future Steps • Further analysis of the collected data: • Influence of user-specific attitudes (e.g. trust disposition) on preferences concerning system actions and trust dimensions (e.g. Distrust towards technical systems -> Higher level of control by the user) • Decision-making for more than one user

16. UMAP 2014 Thank you! Any Question? For more detailed information about the generic part of the UTM: [1] Kurdyukova, E., Andre, E., Leichtenstern, K.: Trust managementofubiquitous multi-display environments. In Krueger, A., Kuik, T., eds.: UbiquitousDisplay Environments. Cognitive Technologies. Springer (2012) http://www.informatik.uni-augsburg.de/en/chairs/swt/se/projects/oc-trust/ http://www.it4se.net/