Can humans use facial recognition algorithms to improve their identification decisions?
What is it about?
We often need to verify an individual's identity from their facial appearance. One common method of verification is the "one-to-one matching task", in which an observer is asked to decide whether a photo ID document (e.g., a passport or driver's licence) matches the person presenting it for inspection. Although this is a common task, average human performance is surprisingly poor - with error rates regularly between 20-30%. Recent technological advances mean that many facial recognition algorithms now outperform the average human on these verification tasks. Even so, often the human operator is responsible for reviewing the algorithm's response and then making the final identification decision. Despite such arrangements already being used for identity verification, very little is known about the collaborative performance of these human-algorithm teams. Here we investigate how knowing the decision of a facial recognition system influences the final identification decision made by the human operator in a one-to-one face matching task.
Why is it important?
We show that although humans can use the decisions from highly accurate facial recognition algorithms to improve their own performance, the decisions they make with the help of the system are actually less accurate than those the system makes alone. In other words, humans often failed to correct errors made by the facial recognition system, but also overruled many of the algorithm's correct decisions. While human oversight of facial recognition algorithms is vital, our research suggests that human ability might be a factor limiting the effectiveness of the human-algorithm team. Our findings have implications for the effective implementation and oversight of facial recognition technologies.