When Do Humans Heed AI Agents’ Advice? When Should They?

• Published in: Human factors Vol. 66; no. 7; pp. 1914 - 1927
• Main Authors: Dunning, Richard E., Fischhoff, Baruch, Davis, Alex L.
• Format: Journal Article
• Language: English
• Published: Los Angeles, CA SAGE Publications 01.07.2024; Human Factors and Ergonomics Society
• Subjects: Adult; Agents (artificial intelligence); Artificial Intelligence; Decision Making; Decision support systems; Factorial experiments; Female; Format; Human-Computer Interaction, Computer Systems; Humans; Male; Neural networks; Neural Networks, Computer; Young Adult; trust in AI; team performance; advisory systems; decision making under uncertainty; artificial intelligence
• ISSN: 0018-7208; 1547-8181; 1547-8181
• DOI: 10.1177/00187208231190459

Objective We manipulate the presence, skill, and display of artificial intelligence (AI) recommendations in a strategy game to measure their effect on users’ performance. Background Many applications of AI require humans and AI agents to make decisions collaboratively. Success depends on how appropriately humans rely on the AI agent. We demonstrate an evaluation method for a platform that uses neural network agents of varying skill levels for the simple strategic game of Connect Four. Methods We report results from a 2 × 3 between-subjects factorial experiment that varies the format of AI recommendations (categorical or probabilistic) and the AI agent’s amount of training (low, medium, or high). On each round of 10 games, participants proposed a move, saw the AI agent’s recommendations, and then moved. Results Participants’ performance improved with a highly skilled agent, but quickly plateaued, as they relied uncritically on the agent. Participants relied too little on lower skilled agents. The display format had no effect on users’ skill or choices. Conclusions The value of these AI agents depended on their skill level and users’ ability to extract lessons from their advice. Application Organizations employing AI decision support systems must consider behavioral aspects of the human-agent team. We demonstrate an approach to evaluating competing designs and assessing their performance.