AI-Assisted Decision Making with Human Learning

Résumé fourni par la source

AI systems are increasingly used to support human decision-making. In many cases, despite the algorithm's superior performance, the final decision remains in human hands. For example, an AI may assist doctors in determining which diagnostic tests to run, but the doctor ultimately makes the diagnosis. Focusing on these scenarios, this paper studies AI-assisted decision-making where the human learns through repeated interactions with the algorithm. In our framework, the algorithm - designed to maximize decision accuracy according to its own model - determines which features the human can consider. The human then makes a prediction based on their own, less accurate model. Additionally, we consider the possibility of a constraint on the number of features that can be taken into account.

Ce résumé expose les affirmations des auteurs. BNTIC ne l’interprète pas comme une validation indépendante des résultats.