Towards the New XAI: A Hypothesis-Driven Approach to Decision Support Using Evidence

• URL: http://arxiv.org/abs/2402.01292v3
• Date: Mon, 26 Aug 2024 04:45:02 GMT
• Title: Towards the New XAI: A Hypothesis-Driven Approach to Decision Support Using Evidence
• Authors: Thao Le, Tim Miller, Liz Sonenberg, Ronal Singh,
• Abstract summary: We describe and evaluate an approach for hypothesis-driven AI based on the Weight of Evidence (WoE) framework. We show that our hypothesis-driven approach increases decision accuracy and reduces reliance compared to a recommendation-driven approach.
• Score: 9.916507773707917
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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