This paper addresses the problem of how to select 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. As the development of XAI made various explanations available, we believe that IDSSs can be greatly improved if they can strategically select explanations that guide users to better decisions. This paper proposes X-Selector, a method for dynamically selecting explanations. X-Selector aims to guide users to better decisions by predicting the impact of different combinations of explanations on user decisions. We compared X-Selector's performance 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 recommended decisions and improve the performance when AI accuracy is high and a challenge when it is low.

翻译：本文探讨了如何为基于XAI（可解释人工智能）的智能决策支持系统（IDSSs）选择解释方案。IDSSs通过AI预测与XAI生成的解释相结合，在改善用户决策方面展现出潜力。随着XAI的发展使多种解释成为可能，我们认为IDSSs若能策略性地选择引导用户做出更优决策的解释方案，其性能将得到显著提升。本文提出一种动态解释选择方法X-Selector，该方法通过预测不同解释组合对用户决策的影响，旨在引导用户做出更优决策。我们将X-Selector的性能与两种朴素策略（全部可能解释、仅针对最可能预测的解释）及两种基线方法（无解释、无AI支持）进行了比较。实验结果表明，X-Selector在引导用户接受推荐决策方面具有潜力：当AI准确性较高时能改善决策性能，而在AI准确性较低时则面临挑战。