DATDAT-003 — Data Bias and Fairness Oversights
Conversational AI Systems Reinforce Gender and Ethnic Stereotypes
4/5Sector: OtherGeography: GlobalStage: OperateIngested: —
Executive Summary
Language models perpetuate harmful stereotypes by introducing biased associations unprompted or by affirming stereotypes raised by users. Organisations deploying conversational AI face reputational, regulatory, and equality-law exposure if stereotype propagation goes undetected at design and monitoring stages.
Domain
Data Management
Blindspots in data quality, privacy, bias, lineage, lifecycle, and third-party data dependencies.
Source
MIT AI Risk Repository — Ethical and social risks of harm from language models (Weidinger2021) ↗https://airisk.mit.edu/
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