{
  "id": 1175,
  "url": "https://arxiv.org/abs/2606.11457v1",
  "title": "Investigating Gender Bias in Touch Biometrics",
  "summary": "Behavioral biometrics offer a promising approach for continuous authentication, but their fairness across demographic groups remains largely unexplored. This paper investigates gender bias in swipe-based authentication using the BBMAS (117 users) and ANTAL (71 users) datasets and evaluates XGBoost and DenseNet classifiers through False Acceptance Rate (FAR) and False Rejection Rate (FRR). XGBoost achieved authentication accuracies of 92% and 94% on the BBMAS and ANTAL datasets, respectively, whi",
  "authors": "Joshua Lee, Ben Khant, Rajesh Kumar",
  "category": "research",
  "topics": "bias-fairness,finance-investment",
  "orgs": null,
  "regions": null,
  "published_at": "2026-06-09T21:19:42.000Z",
  "fetched_at": "2026-07-14T14:15:03.616Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/1175",
  "original_url": "https://arxiv.org/abs/2606.11457v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}