What US GAO Artificial Intelligence Accountability Framework for Federal Agencies and Other Entities (GAO-21-519SP, June 2021) requires
The Government Accountability Office published the AI Accountability Framework (GAO-21-519SP) in June 2021 to help federal agencies and other entities ensure accountability and responsible AI use. The framework is organised around four complementary principles - Governance, Data, Performance, and Monitoring - and identifies eleven Key Practices supported by 33 Key Questions and 50+ types of supporting evidence. Principle 1 Governance covers structures and processes to manage, operate, and oversee implementation across the AI lifecycle. Principle 2 Data addresses data quality, reliability, and representativeness in both model development and operational data. Principle 3 Performance covers verification, validation, and ongoing assessment that the system meets its intended objectives. Principle 4 Monitoring covers continuous oversight to ensure performance over time and to detect drift, bias, and unintended consequences. The framework targets four audiences: agency officials and policymakers, AI developers, third-party assessors, and oversight bodies including inspectors general. GAO uses it for federal program audits and it has become the de facto US public-sector AI audit standard pending formal codification through OMB memoranda (M-24-10, succeeded by M-25-21).
Pillar: AI Governance & Law · Authority: US Government Accountability Office, GAO-21-519SP, June 2021 · Version: 1.0.0 · Last updated:
Primary source: https://www.gao.gov/products/gao-21-519sp
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Primary Citations — 7 traced to source
- US Government Accountability Office, Artificial Intelligence: An Accountability Framework for Federal Agencies and Other Entities, GAO-21-519SP (June 2021)
- GAO-21-519SP Principle 1 Governance: structures and processes to manage, operate, and oversee implementation across the entire AI system life cycle
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