用户决策引导中的可解释AI选择性解释呈现
User Decision Guidance with Selective Explanation Presentation from Explainable-AI
AI总结：
本文提出X-Selector方法，通过选择性呈现XAI解释并预测其影响，引导用户采纳AI建议，实验显示在高AI准确率下能提升决策一致性及任务表现。
摘要：
本文解决了为基于可解释人工智能（XAI）的智能决策支持系统（IDSSs）选择解释的挑战。IDSSs通过XAI生成的解释以及AI预测，在改善用户决策方面显示出潜力，而XAI的发展使得生成多种此类解释成为可能。然而，IDSSs应如何选择解释以增强用户决策仍是一个开放性问题。本文提出了X-Selector，一种选择性呈现XAI解释的方法。它通过预测不同解释组合对用户决策的影响，并选择预期能最小化AI建议与用户决策之间差异的组合，使IDSSs能够战略性地引导用户做出AI建议的决策。我们将X-Selector的效果与两种朴素策略（所有可能的解释和仅针对最可能预测的解释）以及两种基线（无解释和无AI支持）进行了比较。结果表明，在AI准确率较高的条件下，X-Selector有潜力引导用户做出AI建议的决策并提高任务表现。
英文摘要：
This paper addresses the challenge of selecting explanations for XAI (Explainable AI)-based Intelligent Decision Support Systems (IDSSs). IDSSs have shown promise in improving user decisions through XAI-generated explanations along with AI predictions, and the development of XAI made it possible to generate a variety of such explanations. However, how IDSSs should select explanations to enhance user decision-making remains an open question. This paper proposes X-Selector, a method for selectively presenting XAI explanations. It enables IDSSs to strategically guide users to an AI-suggested decision by predicting the impact of different combinations of explanations on a user's decision and selecting the combination that is expected to minimize the discrepancy between an AI suggestion and a user decision. We compared the efficacy of X-Selector with two naive strategies (all possible explanations and explanations only for the most likely prediction) and two baselines (no explanation and no AI support). The results suggest the potential of X-Selector to guide users to AI-suggested decisions and improve task performance under the condition of a high AI accuracy.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
