GOVGOV-001 — Accountability Framework Gaps
Post-deployment benchmark contamination skews AI performance evaluations
3/5Sector: OtherGeography: GlobalStage: OperateIngested: —
Executive Summary
AI models exposed to benchmark data through user inputs during live deployment can absorb that data via further training, invalidating subsequent performance assessments. Regulators and procurement bodies lose reliable evidence for compliance and capability oversight.
Domain
Governance & Compliance
Blindspots in accountability, regulatory compliance, ethics, risk management, data governance, and audit.
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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