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Explanations of AI Agents' actions are considered to be an important factor in improving users' trust in the decisions made by autonomous AI systems. However, as these autonomous systems evolve from reactive, i.e., acting on user input, to proactive, i.e., acting without requiring user intervention, there is a need to explore how the explanation for the actions of these agents should evolve. In this work, we explore the design of explanations through participatory design methods for a proactive auto-response messaging agent that can reduce perceived obligations and social pressure to respond quickly to incoming messages by providing unavailability-related context. We recruited 14 participants who worked in pairs during collaborative design sessions where they reasoned about the agent's design and actions. We qualitatively analyzed the data collected through these sessions and found that participants' reasoning about agent actions led them to speculate heavily on its design. These speculations significantly influenced participants' desire for explanations and the controls they sought to inform the agents' behavior. Our findings indicate a need to transform users' speculations into accurate mental models of agent design. Further, since the agent acts as a mediator in human-human communication, it is also necessary to account for social norms in its explanation design. Finally, user expertise in understanding their habits and behaviors allows the agent to learn from the user their preferences when justifying its actions.
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Verification and Validation of AI Systems Using Explanations
Verification and validation of AI systems, particularly learning-enabled systems, is hard because often they lack formal specifications and rely instead on incomplete data and human subjective feedback. Aligning the behavior of such systems with the intended objectives and values of human designers and stakeholders is very challenging, and deploying AI systems that are misaligned can be risky. We propose to use both existing and new forms of explanations to improve the verification and validation of AI systems. Toward that goal, we preseant a framework, the agent explains its behavior and a critic signals whether the explanation passes a test. In cases where the explanation fails, the agent offers alternative explanations to gather feedback, which is then used to improve the system's alignment. We discuss examples of this approach that proved to be effective, and how to extend the scope of explanations and minimize human effort involved in this process.
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- Award ID(s):
- 2416459
- PAR ID:
- 10599148
- Publisher / Repository:
- AAAI
- Date Published:
- Journal Name:
- Proceedings of the AAAI Symposium Series
- Volume:
- 4
- Issue:
- 1
- ISSN:
- 2994-4317
- Page Range / eLocation ID:
- 76 to 80
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1609/aaaiss.v4i1.31774
