From Evidence to Decision: Exploring Evaluative AI
This work addresses decision-making challenges for users in domains like healthcare and real estate, though it appears incremental as it builds on existing frameworks.
The paper tackles the problem of improving AI-supported decision-making by introducing a hypothesis-driven approach based on the Evaluative AI paradigm, which provides evidence for or against hypotheses, and demonstrates promising results in enhancing human decisions in housing price prediction and skin cancer diagnosis.
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.