Newswise — Facial recognition is now a fixture of modern life, powering everything from national border security to the simple convenience of unlocking a smartphone. However, these advancements bring significant risks to privacy, equity and civil rights. While AI can now match faces as accurately as humans, the specific logic these algorithms use to calculate similarity remains a “black box” (closed source). To bridge this gap, new research from the University of Notre Dame compared commercial and open-source AI against the judgment of 4,000 human participants. While humans and AI generally agree on facial similarity, three factors heavily influence accuracy: the race of the participant, the race of the face being viewed and the individual’s natural recognition skill, according to Ahmed Abbasi, the Joe and Jane Giovanini Professor of IT, Analytics and Operations at Notre Dame’s Mendoza College of Business. Abbasi’s research, “The influence of face recognition ability and race on the relationship between psychological and algorithmic similarity,” is forthcoming in the Journal of Applied Research in Memory and Cognition. “Top-tier AI is now as good as the best human experts," noted Abbasi, the director of Notre Dame’s Lucy Family Institute for Data & Society, deputy director of the Data, AI, and Computing Initiative and co-director of the Human-centered Analytics Lab. “If we use AI as the benchmark for ‘good’ recognition, it suggests a large portion of the population is actually not that great at recognizing faces.” This overlap is critical because law enforcement often uses a “human-in-the-loop” system, where an algorithm flags matches and a person makes the final call. “Humans and AI usually see face similarity the same way,” Abbasi said. “However, people who are naturally better at recognizing faces match the AI’s judgments much more closely — at least 15 percent more often, depending on the model.”