{
  "id": 19164,
  "url": "https://arxiv.org/abs/2608.13444v1",
  "title": "Algorithmic Gender Prediction Is Illegitimate, But Gender Imputation Can Yield Valid Measurements",
  "summary": "Machine learning ethics researchers and critical HCI scholars have argued that algorithmically predicting gender is wrong. At the same time, other researchers rely on predicted gender labels to study gender disparities and develop algorithmic fairness techniques. How do we reconcile these two seemingly contradictory intuitions? We differentiate two ways gender prediction may be wrong: being illegitimate, thereby contributing to harm; and being invalid, thereby producing unusable measurements. Ou",
  "authors": "Evan Dong, Angelina Wang",
  "category": "research",
  "topics": "bias-fairness",
  "orgs": null,
  "regions": null,
  "published_at": "2026-08-13T16:30:47.000Z",
  "fetched_at": "2026-08-14T05:10:49.168Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/19164",
  "original_url": "https://arxiv.org/abs/2608.13444v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}