DATDAT-003 — Data Bias and Fairness Oversights
Explainability Tools Fail to Detect Hidden Discriminatory Bias in AI Models
3/5Sector: TechnologyGeography: GlobalStage: OperateIngested: —
Executive Summary
AI explainability techniques can be actively deceived, producing misleading outputs that conceal discriminatory use of protected attributes such as race and gender. Boards relying on explanations for compliance assurance may be exposed to undetected bias liability.
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/
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