DATDAT-003 — Data Bias and Fairness Oversights
Systematic Data Bias Distorts AI and ML Model Outputs
4/5Sector: OtherGeography: GlobalStage: DevelopIngested: —
Executive Summary
AI and ML models trained on skewed data over-represent certain groups or omit critical variables, producing outputs that mischaracterise the phenomena they are designed to assess. Boards face material liability where biased models underpin decisions affecting customers, operations, or regulatory compliance.
Domain
Data Management
Blindspots in data quality, privacy, bias, lineage, lifecycle, and third-party data dependencies.
Source
MIT AI Risk Repository — Towards risk-aware artificial intelligence and machine learning systems: An overview (Zhang2022) ↗https://airisk.mit.edu/
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