  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.
