When Bias Backfires: The Modulatory Role of Counterfactual Explanations on the Adoption of Algorithmic Bias in XAI-Supported Human Decision-Making
This addresses the problem of AI bias influencing human decision-making in hiring, with incremental insights into the role of explanations.
The study investigated how biased AI recommendations, with or without counterfactual explanations, affect human hiring decisions over time, finding that participants followed AI 70% of the time and that exposure to bias altered their independent decisions, reversing bias when explanations were provided.
Although the integration of artificial intelligence (AI) into everyday tasks improves efficiency and objectivity, it also risks transmitting bias to human decision-making. In this study, we conducted a controlled experiment that simulated hiring decisions to examine how biased AI recommendations - augmented with or without counterfactual explanations - influence human judgment over time. Participants, acting as hiring managers, completed 60 decision trials divided into a baseline phase without AI, followed by a phase with biased (X)AI recommendations (favoring either male or female candidates), and a final post-interaction phase without AI. Our results indicate that the participants followed the AI recommendations 70% of the time when the qualifications of the given candidates were comparable. Yet, only a fraction of participants detected the gender bias (8 out of 294). Crucially, exposure to biased AI altered participants' inherent preferences: in the post-interaction phase, participants' independent decisions aligned with the bias when no counterfactual explanations were provided before, but reversed the bias when explanations were given. Reported trust did not differ significantly across conditions. Confidence varied throughout the study phases after exposure to male-biased AI, indicating nuanced effects of AI bias on decision certainty. Our findings point to the importance of calibrating XAI to avoid unintended behavioral shifts in order to safeguard equitable decision-making and prevent the adoption of algorithmic bias.