DATDAT-003 — Data Bias and Fairness Oversights
AI Value Lock-In and Outcome Homogenisation Entrench Societal Bias
3/5Sector: OtherGeography: GlobalStage: OperateIngested: —
Executive Summary
Widely deployed foundation models trained on outdated datasets risk freezing historical biases and homogenising discriminatory outputs across entire sectors. Boards face regulatory and reputational exposure as systemic exclusion becomes institutionalised at scale through shared model infrastructure.
Domain
Data Management
Blindspots in data quality, privacy, bias, lineage, lifecycle, and third-party data dependencies.
Source
MIT AI Risk Repository — Regulating under Uncertainty: Governance Options for Generative AI (G'sell2024) ↗https://airisk.mit.edu/
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