{
  "id": 19554,
  "url": "https://www.frontiersin.org/articles/10.3389/frai.2026.1817529",
  "title": "Reassessing demographic bias in face attribute classification: a statistically grounded multi-model evaluation on FairFace and UTKFace",
  "summary": "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,",
  "authors": "Andisani Nemavhola",
  "category": "research",
  "topics": "bias-fairness",
  "orgs": null,
  "regions": null,
  "published_at": "2026-08-14T00:00:00.000Z",
  "fetched_at": "2026-08-15T05:10:17.122Z",
  "source_slug": "x-frontiers-in-artificial-intelligence",
  "source_name": "Frontiers in Artificial Intelligence",
  "source_homepage": "https://www.frontiersin.org/journals/artificial-intelligence",
  "ethics_ai_record_url": "https://ethics.ai/record/19554",
  "original_url": "https://www.frontiersin.org/articles/10.3389/frai.2026.1817529",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}