UC San Diego

Several factors influence the effectiveness of human-AI collaborations, including inherent human biases. Our research explores the role of a deceptive agent in enhancing the success of these systems by compensating for these biases. We investigate under what conditions an AI can compensate for human biases and where its use might be ethically justified. Contrary to traditional views that cast strategic deception in a negative light, our findings suggest it can, under specific conditions, improve cooperative outcomes and thereby enhance human decision-making, contributing to broader societal benefits. Our study employs game theory and reinforcement learning to observe how deceptive behaviors naturally emerge within the ongoing learning dynamics of AI agents. We support our theoretical claims with simulation results derived from Markov Decision Processes (MDP) and a signaling game example, providing a practical glimpse into how these agents learn and interact. Building on these insights, we propose an ethical framework to evaluate the permissibility of employing deceptive algorithms and reflect on the nuance the developer must adopt while deploying these algorithms. By advocating a careful approach to strategic deception, we aim to advance human-AI teamwork and decision-making, steering these collaborations toward outcomes that are both ethically sound and socially beneficial.