Learning Complementary Policies for Human-AI Teams

TL;DR

This paper introduces a robust policy learning approach for human-AI teams that strategically allocates decision tasks to maximize team performance, even under model misspecifications, demonstrating significant improvements over independent decision-making.

Contribution

It presents a novel deferral collaboration method that exploits human-AI behavioral differences and is robust to model misspecifications, advancing human-AI decision-making strategies.

Findings

Our method outperforms independent decision-making in synthetic and real scenarios.

Routing a small fraction of instances to humans yields substantial performance gains.

The approach is robust to misspecifications in human behavior and reward models.

Abstract

This paper tackles the critical challenge of human-AI complementarity in decision-making. Departing from the traditional focus on algorithmic performance in favor of performance of the human-AI team, and moving past the framing of collaboration as classification to focus on decision-making tasks, we introduce a novel approach to policy learning. Specifically, we develop a robust solution for human-AI collaboration when outcomes are only observed under assigned actions. We propose a deferral collaboration approach that maximizes decision rewards by exploiting the distinct strengths of humans and AI, strategically allocating instances among them. Critically, our method is robust to misspecifications in both the human behavior and reward models. Leveraging the insight that performance gains stem from divergent human and AI behavioral patterns, we demonstrate, using synthetic and real human responses, that our proposed method significantly outperforms independent human and algorithmic decision-making. Moreover, we show that substantial performance improvements are achievable by routing only a small fraction of instances to human decision-makers, highlighting the potential for efficient and effective human-AI collaboration in complex management settings.