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

Thao Le, Tim Miller, Ronal Singh, Liz Sonenberg
Computer Science, Artificial Intelligence, Artificial Intelligence (cs.AI), Human-Computer Interaction (cs.HC)
2024-02-02 00:00:00
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.
PDF: Towards the new XAI: A Hypothesis-Driven Approach to Decision Support Using Evidence.pdf
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