DATDAT-003 — Data Bias and Fairness Oversights
Stereotype Bias Amplification in Large Language Models
4/5Sector: OtherGeography: GlobalStage: OperateIngested: —
Executive Summary
Pretrained large language models absorb and amplify social stereotypes present in crowdsourced training data, producing outputs that reflect discriminatory generalisations about protected groups. Organisations deploying such models face material legal, reputational, and regulatory exposure under equality and AI governance frameworks.
Domain
Data Management
Blindspots in data quality, privacy, bias, lineage, lifecycle, and third-party data dependencies.
Source
MIT AI Risk Repository — Trustworthy LLMs: A Survey and Guideline for Evaluating Large Language Models’ Alignment (Liu2024) ↗https://airisk.mit.edu/
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