DATDAT-003 — Data Bias and Fairness Oversights
Bias and Discrimination Embedded in Algorithm Design and Training Data
3/5Sector: OtherGeography: GlobalStage: DevelopIngested: —
Executive Summary
Flawed datasets and developer bias during algorithm design produce discriminatory outputs across ethnicity, religion, and nationality. Organisations face regulatory exposure and reputational harm if governance frameworks fail to audit training data and model behaviour systematically.
Domain
Data Management
Blindspots in data quality, privacy, bias, lineage, lifecycle, and third-party data dependencies.
Source
MIT AI Risk Repository — AI Safety Governance Framework (TC2602024) ↗https://airisk.mit.edu/
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