DATDAT-003 — Data Bias and Fairness Oversights
Healthcare AI Systems Deliver Biased Outputs Due to Unrepresentative Training Data
4/5Sector: HealthcareGeography: GlobalStage: OperateIngested: —
Executive Summary
General-purpose AI systems trained predominantly on Western, English-language data produce outputs that systematically disadvantage patients defined by race, gender, age, or disability. Boards deploying such systems in clinical settings face material liability and regulatory exposure if dataset representativeness is not audited before deployment.
Domain
Data Management
Blindspots in data quality, privacy, bias, lineage, lifecycle, and third-party data dependencies.
Source
MIT AI Risk Repository — International Scientific Report on the Safety of Advanced AI (Bengio2024) ↗https://airisk.mit.edu/
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