DATDAT-003 — Data Bias and Fairness Oversights
Frontier AI Models Amplify Societal Bias Despite Attribute Removal Attempts
4/5Sector: OtherGeography: GlobalStage: OperateIngested: —
Executive Summary
Frontier AI systems encode and magnify historical inequalities, inferring protected characteristics such as race and gender even when those attributes are explicitly excluded from training data. Boards deploying AI in consequential decisions face material fairness liability that standard data-cleansing controls cannot adequately mitigate.
Domain
Data Management
Blindspots in data quality, privacy, bias, lineage, lifecycle, and third-party data dependencies.
Source
MIT AI Risk Repository — Capabilities and Risks from Frontier AI (DSIT2023) ↗https://airisk.mit.edu/
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