Why Aggregate Accuracy is Inadequate for Evaluating Fairness in Law Enforcement Facial Recognition Systems
A new research paper argues that relying solely on aggregate accuracy is insufficient for evaluating the fairness of facial recognition systems used by law enforcement. The study highlights how overall high accuracy can mask significant disparities in error rates across different demographic groups. The authors emphasize the need for fairness-aware evaluation methods and post-deployment auditing to prevent potential harm from misclassifications. AI
IMPACT Highlights the need for more nuanced evaluation of AI systems in critical applications to prevent discriminatory outcomes.
