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, 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：人工智能辅助人类决策的前期研究已探索多种可解释AI（XAI）方法。近期一篇论文提出范式转变，主张通过名为评估式AI的概念框架实现假设驱动型XAI——该框架通过提供支持或反驳假设的证据，而非必然给出决策辅助建议。本文基于证据权重（WoE）框架，描述并评估了一种为给定假设同时生成正面与负面证据的假设驱动型XAI方法。通过人类行为实验，我们证明：与推荐驱动型方法及仅包含AI解释的基线相比，该假设驱动型方法在提升决策准确率的同时降低了依赖度，但相较于推荐驱动型方法，出现了轻微过度不信任现象。此外，研究显示参与者对假设驱动型方法的使用方式与前两种基线存在实质性差异。