Co-Designing with Users the Explanations for a Proactive Auto-Response Messaging Agent
Co-Designing with Users the Explanations for a Proactive Auto-Response Messaging Agent
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DOI：
10.1145/3604248
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发表时间：
2023-09
影响因子：
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通讯作者：
Pranut Jain;Rosta Farzan;Adam J. Lee
中科院分区：
文献类型：
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作者：
Pranut Jain;Rosta Farzan;Adam J. Lee
对人工智能代理行为的解释被认为是提高用户对自主人工智能系统决策信任的重要因素。然而，随着这些自治系统从被动（即根据用户输入行事）演变为主动（即不需要用户干预即可行事），有必要探索对这些代理行为的解释应该如何演变。在这项工作中，我们通过参与式设计方法探索了主动自动响应消息传递代理的解释设计，该代理可以通过提供与不可用性相关的上下文来减少快速响应传入消息的感知义务和社会压力。我们招募了14名参与者，他们在协作设计会议上结对工作，对代理的设计和行为进行推理。我们定性分析了通过这些会议收集的数据，发现参与者对代理行为的推理导致他们对其设计进行了大量推测。这些推测显著地影响了参与者对解释的渴望，以及他们试图告知代理人行为的控制。我们的研究结果表明，有必要将用户的猜测转化为智能体设计的准确心理模型。此外，由于代理人在人与人之间的交流中充当中介，因此在其解释设计中也有必要考虑社会规范。最后，用户在理解他们的习惯和行为方面的专业知识允许代理在为其行为辩护时从用户那里学习他们的偏好。
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.