Resume Screening, Fast and Slow: (Biased) AI Recommendations' Influence on Human Decision Making

2026-06-20

AI Summary

This study investigates how AI recommendations imbued with social bias influence human judgment and fairness in high-stakes decision-making, such as résumé screening. Integrating experimental psychology methods, the Implicit Association Test (IAT), and behavioral log analysis, this work pioneers the linkage of cognitive processing time with IAT measures to reveal limitations in human oversight of algorithmic bias. The findings show that, in the absence of AI recommendations, reviewers spent 55.6% more time evaluating candidates; each additional unit of review time increased the likelihood of selecting non-recommended candidates by 3–4%; and IAT scores significantly predicted the fairness of time allocation across candidates. These results underscore the subtle yet consequential ways algorithmic bias can shape human decision processes even when individuals attempt to exercise careful judgment.

Abstract

AI is increasingly being used collaboratively with people to make decisions in high-stakes domains, but this new paradigm is still not well-understood in many respects -- particularly regarding how AI that replicates human social biases influences people's decision making processes and how that can influence outcomes. In this study, we analyzed the time people spend viewing candidate resumes from an experiment investigating biased AI resume screening to evaluate decision-making fairness and cognitive processes underlying human-AI collaboration. We found that spending more time viewing resumes corresponds to candidates' selection chance increasing by 3-4% if they are not recommended, and people may spend up to 55.6% longer viewing resumes when no AI recommendations are given. Furthermore, people who completed an implicit association test (IAT) before resume screening were significantly more likely to evaluate candidates of different races for the same amount of time, and their IAT scores were also predictive of the time spent in human-AI collaboration. These results demonstrate how people's decision-making processes can be insufficient for overseeing AI in high-stakes domains.

Problem

• AI bias
• resume screening
• human-AI collaboration
• decision-making fairness
• implicit bias

Innovation

• biased AI
• human-AI collaboration
• resume screening
• implicit association test (IAT)
• decision-making fairness