DATDAT-001 — Data Quality and Completeness Issues
LLM Cultural Bias from Western-Centric Training Data
3/5Sector: OtherGeography: GlobalStage: DevelopIngested: —
Executive Summary
Large language models trained on non-representative datasets embed culturally biased values that conflict with regional political, religious, and social norms. Organisations deploying these models across markets face regulatory exposure and reputational harm from outputs that offend or marginalise local users.
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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