DATDAT-003 — Data Bias and Fairness Oversights
Chinese LLM Reinforces Gender Stereotypes in Safety Evaluation
4/5Sector: OtherGeography: GlobalStage: OperateIngested: —
Executive Summary
A large language model affirmed discriminatory gender stereotypes when tested, confirming systemic social bias across race, religion, and appearance categories. Boards deploying LLMs face reputational and regulatory exposure where model outputs validate harmful prejudice rather than challenge it.
Domain
Data Management
Blindspots in data quality, privacy, bias, lineage, lifecycle, and third-party data dependencies.
Source
MIT AI Risk Repository — Safety Assessment of Chinese Large Language Models (Sun2023) ↗https://airisk.mit.edu/
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