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• Aggregate accuracy is an insufficient metric for evaluating the fairness and reliability of facial recognition systems in high-stakes law enforcement environments.limitation 95%
• Facial recognition systems often exhibit uneven performance across demographic groups, leading to disproportionate error rates, despite high reported aggregate accuracy.result 90%
• Aggregate performance metrics can obscure critical disparities in error rates (like FPR and FNR) across demographic subgroups.result 85%
• Systems with similar overall accuracy can exhibit substantially different fairness profiles, with significant variation in subgroup error rates.result 85%
• Accuracy-centric evaluation in law enforcement poses operational risks, as misclassification may result in wrongful suspicion or missed identification.limitation 80%
• There is a disconnect between academic research on algorithmic fairness and operational practices in applied law enforcement settings.market 75%
• Fairness-aware evaluation approaches and model-agnostic auditing strategies are important for post-deployment assessment of real-world facial recognition systems.method 80%
• There is a need to move beyond accuracy as a primary metric and adopt more comprehensive evaluation frameworks for responsible AI deployment in facial recognition.method 90%

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