AI for Public Sector Governance

AI for public-sector governance is the discipline of bringing AI into government in a way that respects democratic legitimacy, civil rights, due process, transparency, public participation, and the public good. It sits at the intersection of e-government and digital-transformation practice, the canonical regulatory regimes (US OMB M-24-10 / M-24-18, the AI Bill of Rights, NIST AI RMF, the EU AI Act with its FRIA Article 27 and public-sector Annex III entries, UK ATRS and ADM Code of Practice, Canada Directive on ADM, NZ Algorithm Charter), the procurement and vendor-management machinery that decides what actually gets deployed, the citizen-facing services and ADM surface where rights live or die, the civic-tech / open-government layer, election integrity, sectoral applications (justice / policing / immigration / welfare / tax / health), defence and national security, and the oversight architecture (legislative, judicial, IG / SAI, ombudspersons, civil society, whistleblowers) that makes accountability real.

This track is written for the practitioners doing this work day to day: federal / state / local Chief AI Officers, agency programme leads, government CIOs and CDOs, procurement officers, civic technologists, RAI leads in government, lawyers and policy advisors, ombudspersons and inspectors-general staff, civil-society researchers, journalists covering public-sector AI, and the cross-functional partners who interlock with public AI deployments. Every topic explains the underlying discipline (drawing on the OMB / EU / UK / Canada / Australia / NZ / Singapore / Korea frameworks, OECD / UNESCO / GPAI international work, the canonical case lineage from Robodebt to SyRI to MIDAS to NYC AEDT, and the rich civil-society and academic literature), the practical methodology that operationalises it, the artefacts and rituals that make it stick, and the failure modes where public-sector AI work quietly fails the public it should serve.