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.

翻译：本文针对基于可解释人工智能（XAI）的智能决策支持系统（IDSSs）中解释选择的挑战展开研究。IDSSs通过XAI生成的解释与AI预测相结合，在改善用户决策方面展现出潜力，而XAI的发展使得生成多种此类解释成为可能。然而，IDSSs如何选择解释以增强用户决策效果仍是一个未解问题。本文提出X-Selector方法，用于选择性呈现XAI解释。该方法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能够策略性地引导用户采纳AI建议的决策。我们将X-Selector与两种朴素策略（呈现所有可能解释、仅呈现最可能预测的解释）及两种基线方法（无解释、无AI支持）进行效能对比。结果表明，在AI高精度条件下，X-Selector具有引导用户采纳AI建议决策并提升任务绩效的潜力。