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.

机器学习模型在高风险任务中辅助人类决策，通过提供可解释且任务特定的指导，而不是取代人类专家的决策能力。

使用视觉语言模型指导人类决策者的学习

Learning To Guide Human Decision Makers With Vision-Language Models

In learning to defer, a predictor identifies risky decisions and defers them
to a human expert. One key issue with this setup is that the expert may end up
over-relying on the machine's decisions, due to anchoring bias. At the same
time, whenever the machine chooses the deferral option the expert has to take
decisions entirely unassisted. As a remedy, we propose learning to guide (LTG),
an alternative framework in which -- rather than suggesting ready-made
decisions -- the machine provides guidance useful to guide decision-making, and
the human is entirely responsible for coming up with a decision. We also
introduce SLOG, an LTG implementation that leverages (a small amount of) human
supervision to convert a generic large language model into a module capable of
generating textual guidance, and present preliminary but promising results on a
medical diagnosis task.

通过学习指导机制，我们建议让机器提供有助于决策的指导而不是直接给出决策，从而避免了权重倾向，并且对于医学诊断任务进行初步但有希望的结果展示。