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arXiv · 2504.15894

Supporting Data-Frame Dynamics in AI-assisted Decision Making

Chengbo Zheng·Tim Miller·Alina Bialkowski·H Peter Soyer·Monika Janda
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Abstract

High stakes decision-making often requires a continuous interplay between evolving evidence and shifting hypotheses, a dynamic that is not well supported by current AI decision support systems. In this paper, we introduce a mixed-initiative framework for AI assisted decision making that is grounded in the data-frame theory of sensemaking and the evaluative AI paradigm. Our approach enables both humans and AI to collaboratively construct, validate, and adapt hypotheses. We demonstrate our framework with an AI-assisted skin cancer diagnosis prototype that leverages a concept bottleneck model to facilitate interpretable interactions and dynamic updates to diagnostic hypotheses.

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Chengbo Zheng, Tim Miller, Alina Bialkowski, H Peter Soyer, Monika Janda. 2025-04-22. Supporting Data-Frame Dynamics in AI-assisted Decision Making. https://arxiv.org/abs/2504.15894

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