Understanding the influence of design-related factors on human-AI teaming in a face matching task.

ABSTRACT: There has been a steep rise in the use of decision aids enabled by artificial intelligence (AI) for facial identity verification. The use of such systems and the impact of their implementation on human decision-making is still not well understood. The current study aimed to explore factors associated with the design of the paradigm and of the presentation of AI-enabled predictions. Across three pre-registered experiments, we examined the impact of (a) stated AI accuracy, (b) mismatch frequency (i.e. proportion of match and mismatch pairs), and (c) advice type (binary only vs. binary + similarity rating) on performance in a one-to-one face matching task. Participants' performance generally improved when aided by AI compared to a baseline without decision support. The largest improvement was

SUBMITTER: Kokje E 

PROVIDER: S-EPMC12779867 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature