DATDAT-003 — Data Bias and Fairness Oversights
Algorithmic erasure of minority social groups through biased training data
3/5Sector: OtherGeography: GlobalStage: OperateIngested: —
Executive Summary
Systematic under-representation of minority social groups in training data causes AI systems to render certain identities and experiences invisible in outputs. Organisations deploying such systems face reputational, legal, and equality-duty exposure as discriminatory design choices become embedded at scale.
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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