研究解释性交互中顺序效应如何影响用户信任与反馈质量。
Human Cognitive Biases in Explanation-Based Interaction: The Case of Within and Between Session Order Effect
- 通过两轮大规模实验模拟真实交互场景，考察讲解顺序对用户的影响。
- 顺序效应仅在单次调试会话内轻微影响用户信任，对反馈质量影响微弱且不一致。
- 结论表明顺序偏差不构成解释性交互的显著障碍，适合关注人机协作的开发者参考。
解释性交互学习（XIL）是一种让用户通过与AI解释互动来定制和修正模型的框架。算法选取模型决策的项目（如图像及其标签），并展示对应解释（如驱动决策的图像区域），用户据此提供修正反馈以改进模型。尽管在调试任务中表现良好，但近期研究担忧解释性交互可能引发顺序效应——一种认知偏差，即呈现顺序影响用户信任及反馈质量。本文认为此前研究设计与实际使用场景差异大，结论不够可靠。为此，我们开展了两项大规模用户研究（共713人），模拟常见XIL任务，分别在单次会话内和跨会话间操纵正确与错误解释的呈现顺序。结果显示，顺序效应仅在单次会话内对用户信任产生轻微但显著影响，而对反馈质量影响极小且不一致。总体表明，顺序效应不会显著阻碍XIL的应用。本研究有助于理解人类因素在人工智能中的作用。
原文摘要 · Abstract (English)
Explanatory Interactive Learning (XIL) is a powerful interactive learning framework designed to enable users to customize and correct AI models by interacting with their explanations. In a nutshell, XIL algorithms select a number of items on which an AI model made a decision (e.g. images and their tags) and present them to users, together with corresponding explanations (e.g. image regions that drive the model's decision). Then, users supply corrective feedback for the explanations, which the algorithm uses to improve the model. Despite showing promise in debugging tasks, recent studies have raised concerns that explanatory interaction may trigger order effects, a well-known cognitive bias in which the sequence of presented items influences users' trust and, critically, the quality of their feedback. We argue that these studies are not entirely conclusive, as the experimental designs and tasks employed differ substantially from common XIL use cases, complicating interpretation. To clarify the interplay between order effects and explanatory interaction, we ran two larger-scale user studies (n = 713 total) designed to mimic common XIL tasks. Specifically, we assessed order effects both within and between debugging sessions by manipulating the order in which correct and wrong explanations are presented to participants. Order effects had a limited, through significant impact on users' agreement with the model (i.e., a behavioral measure of their trust), and only when examined withing debugging sessions, not between them. The quality of users' feedback was generally satisfactory, with order effects exerting only a small and inconsistent influence in both experiments. Overall, our findings suggest that order effects do not pose a significant issue for the successful employment of XIL approaches. More broadly, our work contributes to the ongoing efforts for understanding human factors in AI.
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