Supporting Data-Frame Dynamics in AI-assisted Decision Making

• URL: http://arxiv.org/abs/2504.15894v1
• Date: Tue, 22 Apr 2025 13:36:06 GMT
• Title: Supporting Data-Frame Dynamics in AI-assisted Decision Making
• Authors: Chengbo Zheng, Tim Miller, Alina Bialkowski, H Peter Soyer, Monika Janda,
• Abstract summary: High stakes decision-making requires continuous interplay between evolving evidence and shifting hypotheses.<n>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.
• Score: 6.4219774981192455
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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