DATDAT-003 — Data Bias and Fairness Oversights
Algorithmic Systems Entrench Fixed Categories of Gender, Race and Identity
5/5Sector: OtherGeography: GlobalStage: OperateIngested: —
Executive Summary
Machine learning classifiers that infer gender, race or sexuality from physical appearance treat socially constructed categories as biological and immutable facts. Organisations deploying such systems face discrimination liability and reputational harm when outputs embed and amplify structural bias.
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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