Towards Effective Human-AI Decision-Making: The Role of Human Learning in Appropriate Reliance on AI Advice

TL;DR

This paper investigates how human learning influences appropriate reliance on AI advice, emphasizing that effective human-AI collaboration depends on both mental models and learning to achieve superior joint performance.

Contribution

It introduces the role of human learning as a key factor in appropriate reliance on AI, extending beyond mental models to improve human-AI decision-making.

Findings

Learning mediates appropriate reliance on AI advice

Experiment with 100 participants supports the hypothesis

Implications for designing better human-AI collaboration systems

Abstract

The true potential of human-AI collaboration lies in exploiting the complementary capabilities of humans and AI to achieve a joint performance superior to that of the individual AI or human, i.e., to achieve complementary team performance (CTP). To realize this complementarity potential, humans need to exercise discretion in following AI 's advice, i.e., appropriately relying on the AI's advice. While previous work has focused on building a mental model of the AI to assess AI recommendations, recent research has shown that the mental model alone cannot explain appropriate reliance. We hypothesize that, in addition to the mental model, human learning is a key mediator of appropriate reliance and, thus, CTP. In this study, we demonstrate the relationship between learning and appropriate reliance in an experiment with 100 participants. This work provides fundamental concepts for analyzing reliance and derives implications for the effective design of human-AI decision-making.