DATDAT-003 — Data Bias and Fairness Oversights
LLM Outputs Treat Similar Individuals Differently Based on Group Membership
4/5Sector: OtherGeography: GlobalStage: OperateIngested: —
Executive Summary
Large language models produce inconsistent outputs for individuals with identical relevant profiles when group attributes such as race or gender differ, violating basic impartiality standards. Organisations deploying these models face legal exposure and reputational harm if biased outputs affect decisions in hiring, lending, or public services.
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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