The Role of Initial Acceptance Attitudes Toward AI Decisions in Algorithmic Recourse

Algorithmic recourse provides counterfactual suggestions to individuals who receive unfavorable AI decisions; the aim is to help them understand the reasoning and guide future actions. While most research focuses on generating reasonable and actionable recourse, it often overlooks how individuals’ initial reactions to AI decisions influence their perceptions of subsequent recourses and their ultimate acceptance of the decision. To explore this, we conducted a user experiment (N = 534) simulating an automobile loan application scenario. Statistical analysis revealed that participants who initially reacted negatively to the AI decision perceived the recourse as less reasonable and actionable, reinforcing their negative attitudes. However, when the recourse was perceived as explaining decision criteria or proposing realistic action plans, participants’ attitudes shifted from negative to positive. These findings offer design implications for recourse systems that enhance the acceptance of individuals negatively affected by AI decisions.