DATDAT-003 — Data Bias and Fairness Oversights
Systemic Bias and Fairness Failures in Generative AI Models
4/5Sector: TechnologyGeography: GlobalStage: OperateIngested: —
Executive Summary
Training data biases propagate into generative AI outputs, producing stereotyping, racism, and cultural value imposition at scale. Boards face reputational, regulatory, and equity risks as power concentrates in large AI labs and access remains unequal.
Domain
Data Management
Blindspots in data quality, privacy, bias, lineage, lifecycle, and third-party data dependencies.
Source
MIT AI Risk Repository — Mapping the Ethics of Generative AI: A Comprehensive Scoping Review (Hagendorff2024) ↗https://airisk.mit.edu/
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