  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.
