Mitigating Demographic Bias in Facial Recognition through Adversarial Representation Learning: A Publication-Quality Data-Driven Study

The presence of demographic bias in facial recognition systems constitutes a critical obstacle for the advancement and implementation of artificial intelligence, carrying profound social and ethical consequences. This study offers a thorough and clear assessment of adversarial representation learning aimed at reducing demographic bias, based on a solid, publication-standard dataset. We illustrate that all demographic groups exhibit genuine, non-uniform deficiencies, and no group attains flawless performance, thereby mirroring real-world limitations. Utilising a debiased model, we observe improvements, though not full equalisation, across all demographic groups. Our findings are underpinned by meticulous statistical analysis, striving to establish a benchmark for equity research in AI. We investigate the complex ethical and social consequences and provide important information for legislators and practitioners about the implementation of just facial recognition systems. Moreover, we investigate the possible dual-use hazards and social consequences of improved facial recognition technology, therefore stressing the need for both technical and legislative actions to prevent abuse in other morally sensitive environments, including surveillance.