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.
Keywords: advisory systems; artificial intelligence; decision making under uncertainty; team performance; trust in AI.