Does the Whole Exceed its Parts? The Effect of AI Explanations on Complementary Team Performance

TL;DR

This study investigates whether AI explanations can foster complementary team performance, finding that explanations increase trust but do not necessarily improve combined accuracy in human-AI teams.

Contribution

The paper provides empirical evidence that AI explanations do not enhance complementary performance, highlighting the need for explanations that promote appropriate trust.

Findings

Explanations increase trust in AI recommendations.

Complementary performance was observed but not enhanced by explanations.

Explanations did not lead to higher team accuracy than AI or humans alone.

Abstract

Many researchers motivate explainable AI with studies showing that human-AI team performance on decision-making tasks improves when the AI explains its recommendations. However, prior studies observed improvements from explanations only when the AI, alone, outperformed both the human and the best team. Can explanations help lead to complementary performance, where team accuracy is higher than either the human or the AI working solo? We conduct mixed-method user studies on three datasets, where an AI with accuracy comparable to humans helps participants solve a task (explaining itself in some conditions). While we observed complementary improvements from AI augmentation, they were not increased by explanations. Rather, explanations increased the chance that humans will accept the AI's recommendation, regardless of its correctness. Our result poses new challenges for human-centered AI: Can we develop explanatory approaches that encourage appropriate trust in AI, and therefore help generate (or improve) complementary performance?