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.

通过权重证据（WoE）框架生成正负两方面的证据来支持或反驳假设，本研究描述和评估了一种基于假设驱动的可解释人工智能（XAI）的方法，并通过人类行为实验表明该方法提高了决策准确性、减少对推荐驱动方法和仅有 AI 解释的基准的依赖，但与推荐驱动方法相比在依赖性不足方面稍有增加，并进一步显示参与者使用我们的假设驱动方法与两个基准方法有着实质性的不同。