DATDAT-003 — Data Bias and Fairness Oversights
General-Purpose AI Systems Producing Biased Outputs Against Specific Communities
4/5Sector: OtherGeography: GlobalStage: OperateIngested: —
Executive Summary
General-purpose AI systems embed and amplify bias across tasks, producing outputs that exclude, misrepresent, or harm specific communities including through deepfake-enabled sexual violence. Boards face regulatory exposure and reputational liability where deployed systems lack controls to detect and mitigate discriminatory outputs.
Domain
Data Management
Blindspots in data quality, privacy, bias, lineage, lifecycle, and third-party data dependencies.
Source
MIT AI Risk Repository — Risk Sources and Risk Management Measures in Support of Standards for General-Purpose AI Systems (Gipiškis2024) ↗https://airisk.mit.edu/
Could this happen in your organisation?
A Velinor AI Audit maps your active AI portfolio against the 50+ blindspots and benchmarks against documented sector failures like this one. A board-ready foresight document in 5 weeks.