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Practical website guidance for state and federal program managers balancing mission innovation with statutory compliance.
Public sector agency leaders face dual imperatives: harness generative and predictive AI to improve public services and operational efficiency, while upholding rigorous standards of privacy, non-discrimination, security, and statutory compliance.
The National Institute of Standards and Technology (NIST) AI Risk Management Framework (AI RMF 1.0) provides an established framework for managing AI risks. Structuring agency workflows informed by this framework involves addressing its core functions:
Govern & Map: Establishing agency AI oversight, defining risk thresholds for specific use cases, and cataloging automated decision systems in compliance with statutory and executive directives.
Measure & Test: Conducting empirical evaluations of bias, accuracy, latency, and failure modes across candidate model architectures prior to public deployment.
Manage & Monitor: Establishing continuous logging, drift detection, and automated fail-safe fallbacks that route anomalous queries to human specialists.
Audit & Documentation: Maintaining provenance records of prompt inputs, retrieved context chunks, and model responses to support accountability and auditability.
By adhering to these established framework functions, public sector organizations can navigate modernization while maintaining public trust and statutory compliance.