DATDAT-003 — Data Bias and Fairness Oversights
LLM Fairness Failures Drive Discriminatory Outputs and Legal Exposure
4/5Sector: OtherGeography: GlobalStage: OperateIngested: —
Executive Summary
Large language models misaligned with human values produce biased outputs that discriminate against users across protected characteristics. Deployers face regulatory breach under anti-discrimination law, reputational damage, and loss of user trust at scale.
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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