DATDAT-003 — Data Bias and Fairness Oversights
Automated Gender Classifiers Force Binary Categories on Non-Binary Users
4/5Sector: OtherGeography: GlobalStage: OperateIngested: —
Executive Summary
Algorithmic classification systems impose binary gender labels on individuals who identify outside that framework, removing their autonomy over self-disclosure. Organisations deploying such systems face material equality, regulatory, and reputational exposure under UK equalities law.
Domain
Data Management
Blindspots in data quality, privacy, bias, lineage, lifecycle, and third-party data dependencies.
Source
MIT AI Risk Repository — Sociotechnical Harms of Algorithmic Systems: Scoping a Taxonomy for Harm Reduction (Shelby2023) ↗https://airisk.mit.edu/
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