Context

A facial recognition system is being developed for secure access control in a multinational corporation with employees from diverse demographic backgrounds. Previous facial recognition systems have shown significant disparities in accuracy across different demographic groups, particularly for individuals with darker skin tones and women. The organization is committed to ensuring equitable performance across all demographic groups to prevent discriminatory outcomes and maintain security integrity.

Trigger

The facial recognition algorithm processes images captured by security cameras at entry points and compares them against the employee database to grant or deny access.

Acceptance Criteria

• Maximum false non-match rate (FNMR) difference of 1.5% between any demographic groups defined by intersections of gender, age range, and skin tone
• Overall accuracy rate of at least 98% across all demographic groups
• Documented testing demonstrates accuracy disparities between demographic groups reduced to within specified threshold
• Demographically balanced training dataset with representation proportional to global workforce demographics (±5% margin)
• Continuous monitoring automatically flags when performance disparities between demographic groups exceed 2% in production
• Transparent reporting of performance metrics across demographic groups in monthly system audits
• Fallback authentication mechanism triggered automatically when confidence scores fall below predetermined threshold
• Independent third-party testing for demographic performance disparities before deployment and annually thereafter