Towards Optimizing Human-Centric Objectives in AI-Assisted Decision-Making With Offline Reinforcement Learning
arxiv(2024)
摘要
As AI assistance is increasingly infused into decision-making processes, we
may seek to optimize human-centric objectives beyond decision accuracy, such as
skill improvement or task enjoyment of individuals interacting with these
systems. With this aspiration in mind, we propose offline reinforcement
learning (RL) as a general approach for modeling human-AI decision-making to
optimize such human-centric objectives. Our approach seeks to optimize
different objectives by adaptively providing decision support to humans – the
right type of assistance, to the right person, at the right time. We
instantiate our approach with two objectives: human-AI accuracy on the
decision-making task and human learning about the task, and learn policies that
optimize these two objectives from previous human-AI interaction data. We
compare the optimized policies against various baselines in AI-assisted
decision-making. Across two experiments (N = 316 and N = 964), our results
consistently demonstrate that people interacting with policies optimized for
accuracy achieve significantly better accuracy – and even human-AI
complementarity – compared to those interacting with any other type of AI
support. Our results further indicate that human learning is more difficult to
optimize than accuracy, with participants who interacted with
learning-optimized policies showing significant learning improvement only at
times. Our research (1) demonstrates offline RL to be a promising approach to
model dynamics of human-AI decision-making, leading to policies that may
optimize various human-centric objectives and provide novel insights about the
AI-assisted decision-making space, and (2) emphasizes the importance of
considering human-centric objectives beyond decision accuracy in AI-assisted
decision-making, while also opening up the novel research challenge of
optimizing such objectives.
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