Supporting Data-Frame Dynamics in AI-assisted Decision Making
This addresses the need for more flexible AI support in high-stakes decision-making, though it appears incremental as it builds on existing theories and paradigms.
The paper tackles the problem of AI decision support systems not accommodating the dynamic interplay between evolving evidence and shifting hypotheses in high-stakes decision-making by introducing a mixed-initiative framework grounded in data-frame theory and evaluative AI, resulting in a prototype for AI-assisted skin cancer diagnosis that enables collaborative hypothesis construction and adaptation.
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.