Supporting Data-Frame Dynamics in AI-assisted Decision Making
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866915253420294144 |
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| author | Zheng, Chengbo Miller, Tim Bialkowski, Alina Soyer, H Peter Janda, Monika |
| author_facet | Zheng, Chengbo Miller, Tim Bialkowski, Alina Soyer, H Peter Janda, Monika |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_15894 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Supporting Data-Frame Dynamics in AI-assisted Decision Making Zheng, Chengbo Miller, Tim Bialkowski, Alina Soyer, H Peter Janda, Monika Human-Computer Interaction Artificial Intelligence 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. |
| title | Supporting Data-Frame Dynamics in AI-assisted Decision Making |
| topic | Human-Computer Interaction Artificial Intelligence |
| url | https://arxiv.org/abs/2504.15894 |