Towards Optimizing Human-Centric Objectives in AI-Assisted Decision-Making With Offline Reinforcement Learning

TL;DR

This paper explores using offline reinforcement learning to develop AI decision-support tools that optimize human-centric objectives like accuracy and learning, demonstrating improved performance and insights in human-AI collaboration.

Contribution

It introduces offline RL as a novel approach to model and optimize diverse human-centric objectives in AI-assisted decision-making, beyond just accuracy.

Findings

Optimized policies improve decision accuracy and human-AI complementarity.

Human learning is harder to optimize than accuracy, with limited improvements.

Offline RL effectively models human-AI decision-making for multiple objectives.

Abstract

Imagine if AI decision-support tools not only complemented our ability to make accurate decisions, but also improved our skills, boosted collaboration, and elevated the joy we derive from our tasks. Despite the potential to optimize a broad spectrum of such human-centric objectives, the design of current AI tools remains focused on decision accuracy alone. We propose offline reinforcement learning (RL) as a general approach for modeling human-AI decision-making to optimize human-AI interaction for diverse objectives. RL can optimize such objectives by tailoring decision support, providing the right type of assistance to the right person at the right time. We instantiated our approach with two objectives: human-AI accuracy on the decision-making task and human learning about the task and learned decision support policies from previous human-AI interaction data. We compared the optimized policies against several baselines in AI-assisted decision-making. Across two experiments (N=316 and N=964), our results demonstrated 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 indicated that human learning was 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 human-AI decision-making, leading to policies that may optimize 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, opening up the novel research challenge of optimizing human-AI interaction for such objectives.