Learning to Make Adherence-Aware Advice

arXiv:2310.00817v2 Announce Type: replace-cross
Abstract: As artificial intelligence (AI) systems play an increasingly prominent role in human decision-making, challenges surface in the realm of human-AI interactions. One challenge arises from the suboptimal AI policies due to the inadequate consideration of humans disregarding AI recommendations, as well as the need for AI to provide advice selectively when it is most pertinent. This paper presents a sequential decision-making model that (i) takes into account the human's adherence level (the probability that the …

abstract advice ai interactions ai policies ai recommendations artificial artificial intelligence arxiv challenge challenges cs.lg decision human humans intelligence interactions making recommendations role stat.ml surface systems type