Human-algorithm teaming in face recognition: How algorithm outcomes cognitively bias human decision-making

In face recognition applications, humans often team with algorithms, reviewing algorithm results to make an identity decision. However, few studies have explicitly measured how algorithms influence human face matching performance. One study that did examine this interaction found a concerning deterioration of human accuracy in the presence of algorithm errors. We conducted an experiment to examine how prior face identity decisions influence subsequent human judgements about face similarity. 376 volunteers were asked to rate the similarity of face pairs along a scale. Volunteers performing the task were told that they were reviewing identity decisions made by different sources, either a computer or human, or were told to make their own judgement without prior information. Replicating past results, we found that prior identity decisions, presented as labels, influenced volunteers’ own identity judgements. We extend these results as follows. First, we show that the influence of identity decision labels was independent of indicated decision source (human or computer) despite volunteers’ greater distrust of human identification ability. Second, applying a signal detection theory framework, we show that prior identity decision labels did not reduce volunteers’ attention to the face pair. Discrimination performance was the same with and without the labels. Instead, prior identity decision labels altered volunteers’ internal criterion used to judge a face pair as “matching” or “non-matching”. This shifted volunteers’ face pair similarity judgements by a full step along the response scale. Our work shows how human face matching is affected by prior identity decision labels and we discuss how this may limit the total accuracy of human-algorithm teams performing face matching tasks.