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arXiv:2403.16501v1 Announce Type: new
Abstract: There is increasing interest in developing AIs for assisting human decision making in \textit{high-stakes} tasks, such as medical diagnosis, for the purpose of improving decision quality and reducing cognitive strain.
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Mainstream approaches team up an expert with a machine learning model to which safer decisions are offloaded, thus letting the former focus on cases that demand their attention.
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This \textit{separation of responsibilities} setup, however, is inadequate for high-stakes scenarios. On the one hand, the expert may end up over-relying on the machine's decisions due to \textit{anchoring bias}, thus losing the human oversight that is increasingly being required by regulatory agencies to ensure trustworthy AI. On the other hand, the expert is left entirely unassisted on the (typically hardest) decisions on which the model abstained.
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As a remedy, we introduce \textit{learning to guide} (LTG), an alternative framework in which -- rather than taking control from the human expert -- the machine provides \textit{guidance} useful for decision making, and the human is entirely responsible for coming up with a decision.
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In order to ensure guidance is \textit{interpretable} and \textit{task-specific}, we develop \method, an approach for turning \textit{any} vision-language model into a capable generator of textual guidance by leveraging a modicum of human feedback.
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Our empirical evaluation highlights the promise of \method on a challenging, real-world medical diagnosis task.