Fairness and Trust in AI Decision-Making: The Role of Human Involvement and Outcome Favorability

The growing integration of artificial intelligence (AI) into decision-making processes has raised important concerns about fairness and trust. This research investigates how the presence of AI, human collaboration, and outcome favorability shape perceptions of fairness and trust in decision-making in a college-admission scenario. Across two experimental studies, we examine decision-making scenarios involving AI-only, human-AI collaboration, and human-only agents, under decision outcomes that were either favorable or unfavorable. Study 1 demonstrates that human involvement, either alone or in collaboration with AI, enhances fairness and trust relative to AI-only decisions, with fairness partially explaining trust differences. Study 2 extends these findings by showing that outcome favorability strongly influences fairness and trust, often outweighing the effects of who made the decisions. Notably, while human-only decisions elicited the highest trust under favorable outcomes, trust differences across agents diminished when outcomes were unfavorable. These findings highlight the complex interplay between decision-making agents and outcome favorability, underscoring the importance of integrating human involvement and managing outcome expectations to foster fairness and trust in AI-driven decision-making systems.