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arXiv · 2402.01292

From Evidence to Decision: Exploring Evaluative AI

Thao Le·Tim Miller·Liz Sonenberg·Ronal Singh·H. Peter Soyer
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Abstract

This paper presents a hypothesis-driven approach to improve AI-supported decision-making that is based on the Evaluative AI paradigm - a conceptual framework that proposes providing users with evidence for or against a given hypothesis. We propose an implementation of Evaluative AI by extending the Weight of Evidence framework, leading to hypothesis-driven models that support both tabular and image data. We demonstrate the application of the new decision-support approach in two domains: housing price prediction and skin cancer diagnosis. The findings show promising results in improving human decisions, as well as providing insights on the strengths and weaknesses of different decision-support approaches.

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Thao Le, Tim Miller, Liz Sonenberg, Ronal Singh, H. Peter Soyer. 2024-02-02. From Evidence to Decision: Exploring Evaluative AI. https://arxiv.org/abs/2402.01292

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