Limits of Interventions in AI-Assisted Decision Making

Abstract

As AI decision-support systems are increasingly deployed in high-stakes domains, understanding how to prevent automation bias is critical. This study examines whether three common interventions, including accountability requirements, consequence framing, and sequential disclosure of AI recommendations, reduce over-reliance on AI in socially consequential decisions. We conducted a controlled experiment using simulated case studies of AI-assisted child maltreatment risk assessment. We found that accountability requirements increased deliberation time but did not reduce over-reliance on AI or improve alignment with expert judgment. Sequential disclosure revealed that participants frequently abandoned an expert-aligned initial assessment in favor of the AI recommendation, producing no meaningful improvement in appropriate reliance. Consequence framing, by contrast, reduced over-reliance without increasing under-reliance and produced the highest rate of decisions consistent with expert judgment. Across conditions, greater deliberation and more elaborate moral reasoning were associated with increased AI reliance and lower alignment with expert judgment. Furthermore, results from the sequential disclosure condition suggest that AI recommendations can override the cognitive anchoring effect of a participant’s own prior judgment. These findings highlight the limits of deliberation-based interventions and point to consequence framing as a more promising approach for calibrating human reliance on AI in high-stakes settings.