Towards the New XAI: A Hypothesis-Driven Approach to Decision Support Using Evidence
<|reference_start|>Towards the New XAI: A Hypothesis-Driven Approach to Decision Support Using Evidence: 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.<|reference_end|>
@article{le2024towards,
    title={Towards the New XAI: A Hypothesis-Driven Approach to Decision Support
  Using Evidence},
    author={Thao Le, Tim Miller, Liz Sonenberg, Ronal Singh},
    journal={arXiv preprint arXiv:2402.01292},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.01292},
    primaryClass={cs.AI cs.HC}
}