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Evidence record 19554 · automatically gathered

Reassessing demographic bias in face attribute classification: a statistically grounded multi-model evaluation on FairFace and UTKFace

Face analysis systems are widely used in security, authentication, and public-sector applications; however, demographic bias and the statistical reliability of reported performance remain key concerns. Many studies rely on aggregate accuracy without quantifying subgroup disparities or uncertainty, potentially overstating model fairness. This study presents a statistically grounded evaluation of demographic bias in face attribute classification across three representative architectures, ResNet50,

Record details

Published: 14 August 2026
Source: Frontiers in Artificial Intelligence
Category: Research
Topics: Bias & fairness
Retrieved: 15 August 2026

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These records share source-supplied organisations, an exact publisher byline, automatic topics or regions. The reason is shown on every link; related does not mean supporting, agreeing with or verifying this record.

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How to cite this record

ethics.ai (14 August 2026), “Reassessing demographic bias in face attribute classification: a statistically grounded multi-model evaluation on FairFace and UTKFace,” evidence record 19554, https://ethics.ai/record/19554 (originally published by Frontiers in Artificial Intelligence).

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