DATDAT-003 — Data Bias and Fairness Oversights
Model Bias Arising from Algorithm Design Choices Beyond Training Data
3/5Sector: TechnologyGeography: GlobalStage: DevelopIngested: —
Executive Summary
AI model bias emerges not only from biased data but from algorithm selection, regularisation, and optimisation choices, producing presentation, evaluation, and popularity distortions. Boards relying on model outputs for decisions face systematic errors that standard data-quality audits will not detect or remediate.
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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