A new cross-disciplinary study by Washington University in St. Louis researchers has uncovered an unexpected psychological phenomenon at the intersection of human behavior and artificial intelligence (AI): When told they were training AI to play a bargaining game, participants actively adjusted their own behavior to appear more fair and just, an impulse with potentially important implications for real-world AI developers.
“The participants seemed to have a motivation to train AI for fairness, which is encouraging. But other people might have different agendas,” said Lauren Treiman, a PhD student in the Division of Computational and Data Sciences and lead author of the study. “Developers should know that people will intentionally change their behavior when they know it will be used to train AI.”
The study, published in the Proceedings of the National Academy of Sciences (PNAS), was supported by a seed grant from the Transdisciplinary Institute in Applied Data Sciences (TRIADS), a signature initiative of the Arts & Sciences strategic plan. The co-authors are Wouter Kool, PhD, an assistant professor of psychological and brain sciences in Arts & Sciences, and Chien-Ju Ho, PhD, an assistant professor of computer science and engineering at the McKelvey School of Engineering. Kool and Ho are Treiman’s graduate advisers.
The study included five experiments, each with roughly 200-300 participants. Subjects were asked to play the “Ultimatum Game,” a challenge that requires them to negotiate small cash payouts (just $1 to $6) with other human players or a computer. In some cases, they were told their decisions would be used to teach an AI bot how to play the game.