DATDAT-003 — Data Bias and Fairness Oversights
Opaque Algorithmic Bias in Public-Sector Systems Causes Severe Personal Harm
4/5Sector: EducationGeography: GlobalStage: OperateIngested: —
Executive Summary
Algorithms assigning disproportionate weight to protected variables such as race and gender produce unreliable outputs with no transparency, resulting in incarceration, home loss, and prosecution. Boards must treat ethics training and developer-user coordination as governance obligations, not optional curriculum additions.
Domain
Data Management
Blindspots in data quality, privacy, bias, lineage, lifecycle, and third-party data dependencies.
Source
MIT AI Risk Repository — Navigating the Landscape of AI Ethics and Responsibility (Cunha2023) ↗https://airisk.mit.edu/
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