DATDAT-003 — Data Bias and Fairness Oversights
Algorithmic Stereotyping of Social Groups in Language Model Outputs
4/5Sector: OtherGeography: GlobalStage: OperateIngested: —
Executive Summary
Language models embed and reinforce group stereotypes through subtle output patterns, such as treating professional roles as implicitly gendered. Organisations deploying these systems face reputational, legal, and regulatory exposure if outputs reflect discriminatory assumptions at scale.
Domain
Data Management
Blindspots in data quality, privacy, bias, lineage, lifecycle, and third-party data dependencies.
Source
MIT AI Risk Repository — Sociotechnical Harms of Algorithmic Systems: Scoping a Taxonomy for Harm Reduction (Shelby2023) ↗https://airisk.mit.edu/
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