Prior research on AI-assisted human decision-making has explored several different explainable AI (XAI) approaches. A recent paper has proposed a paradigm shift calling for hypothesis-driven XAI through a conceptual framework called evaluative AI that gives people evidence that supports or refutes hypotheses without necessarily giving a decision-aid recommendation. In this paper, we describe and evaluate an approach for hypothesis-driven XAI based on the Weight of Evidence (WoE) framework, which generates both positive and negative evidence for a given hypothesis. Through human behavioural experiments, we show that our hypothesis-driven approach increases decision accuracy and reduces reliance compared to a recommendation-driven approach and an AI-explanation-only baseline, but with a small increase in under-reliance compared to the recommendation-driven approach. Further, we show that participants used our hypothesis-driven approach in a materially different way to the two baselines.

翻译：先前关于AI辅助人类决策的研究探索了几种不同的可解释AI方法。近期一篇论文提出范式转变，呼吁通过名为“评估性AI”的概念框架实现基于假设的XAI，该框架向人们提供支持或反驳假设的证据，而无需给出决策辅助建议。本文描述并评估了一种基于证据权重（WoE）框架的假设驱动型XAI方法，该方法能为给定假设生成正向和负向证据。通过人类行为实验，我们发现与推荐驱动方法和仅含AI解释的基线相比，我们的假设驱动方法提高了决策准确率并降低了对AI的依赖，但相较于推荐驱动方法，其存在轻微的过度不依赖现象。此外，研究显示，参与者对假设驱动方法的使用方式与两种基线存在本质差异。