arXiv:2610.33045v1 Announce Type: new Abstract: Integration of artificial intelligence (AI) technology into identity workflows, such as automated face recognition (FR), has enhanced operational capabilities, with FR algorithms achieving super-human scale and accuracy. However, these gains can make human oversight of FR more difficult, raising questions about the effectiveness of these human-AI teams. We measured human reviewers' ability to detect false positive errors made by commercial FR algorithms across varying security configurations. Using a novel dataset, the Difficult Facial Image Comparison Test (DFICT), derived from false positives made by four comparable commercial face recognition algorithms, we measured the ability of 801human reviewers to detect algorithmic false positives across three security configurations, representing operationally relevant biometric thresholds. Our findings show that human reviewers can reduce algorithmic false positives, but performance declines at...