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Evaluating Face Identification Expertise: Turning Theory into Practice. Digested Analysis
Abstract: Critical face identification decisions that underpin security, forensic and legal processes are often made by people. Border control officers compare a passport photo to a traveller, surveillance officers see a person of interest in a crowd, police officers compare mugshots to CCTV images. Nowadays, many of these tasks are supported by AI facial recognition technology 2. Policymakers, academics and the general public are debating how this technology should be used, and the appropriate privacy and human rights safeguards. An important point that is often overlooked when deploying face recognition technology is that it does not fully automate face identification decisions. People are integral to these decisions because human oversight can ensure accuracy, accountability and ethical use. Face identification decisions can have negative impacts on people’s lives, potentially restricting their access to government services, freedom to travel across national borders or even leading to their wrongful arrest3,4 . Face identification systems, which incorporate AI and human decision-making, can be designed to limit these negative impacts, and ensure that they do not disproportionately affect socio-economic or demographic groups. To address these emerging issues, we convened an international workshop of researchers in face identification from psychology, forensic science, artificial intelligence and law — with practitioners and policy-makers from police and government (see Workshop Members). We hope that outcomes can assist in development of policy and implementation of face identification and identity management systems in government, police, private industry and the judicial system. The main conclusions and recommendations of the workshop are: • Face identification is now a mature multi-disciplinary field incorporating forensic science, cognitive psychology and artificial intelligence research. Compared to other biometric and pattern-matching disciplines, there is extensive research on the performance of humans and face recognition technology in face identification tasks. This research provides a foundation of scientific understanding that can provide the basis for designing accurate, fair, responsible and transparent human use of face recognition technology. • Recent research shows that accuracy of the best Artificial Intelligence (AI) face recognition technology and the best humans are comparable, but performance is optimized by combining decisions made by the best AI and the best humans. A key challenge is to incorporate these research findings into operational systems with appropriate human oversight. To do this, it is first necessary to have agreed protocols