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From Evidence to Decision: Exploring Evaluative AI
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From Evidence to Decision: Exploring Evaluative AI
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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.
Forward citations
Cited by 2 Pith papers
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Beyond Explainable AI (XAI): An Overdue Paradigm Shift and Post-XAI Research Directions
Current XAI methods for DNNs and LLMs rest on paradoxes and false assumptions that demand a paradigm shift to verification protocols, scientific foundations, context-aware design, and faithful model analysis rather th...
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Beyond Explainable AI (XAI): An Overdue Paradigm Shift and Post-XAI Research Directions
A position paper argues that post-hoc XAI explanations are unfaithful and paradoxical, proposing a shift to expert-based verification and certification of AI systems.
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