DATDAT-003 — Data Bias and Fairness Oversights
Toxic and Biased Training Data Embedded in Large Language Models
3/5Sector: OtherGeography: GlobalStage: DevelopIngested: —
Executive Summary
Large language models inherit toxic content and stereotypical bias directly from their training corpora, making harmful outputs a systemic rather than incidental risk. Boards deploying LLMs face reputational, legal, and regulatory exposure unless data provenance and bias controls are subject to formal governance oversight.
Domain
Data Management
Blindspots in data quality, privacy, bias, lineage, lifecycle, and third-party data dependencies.
Source
MIT AI Risk Repository — Risk Taxonomy, Mitigation, and Assessment Benchmarks of Large Language Model Systems (Cui2024) ↗https://airisk.mit.edu/
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