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.

该研究论文提出了一种名为 X-Selector 的方法，用于动态选择可解释的人工智能决策支持系统中的解释，以引导用户做出更好的决策。通过与两种幼稚策略和两种基准策略进行比较，结果表明 X-Selector 在高人工智能准确性时能够引导用户做出推荐决策并提高性能，在低人工智能准确性时则面临挑战。