Artificial intelligence is changing the balance of information capability between institutions and the public.
My focus is not software engineering or technical AI development, but the governance and accountability consequences of emerging technologies. My practical information and accountability experience informs this analysis.
AI can materially increase an individual’s ability to organise large evidence sets, maintain complex chronologies, interrogate legislation, compare policy with institutional practice, analyse correspondence, identify inconsistencies and sustain scrutiny across lengthy administrative processes.
What happens to administrative systems developed for an earlier information environment when individuals can use AI to sustain institutional-level analytical capacity?
This is the connection between information governance and AI governance. The institutional response requires better records, better reasoning, better governance and better information practices.
AI-assisted analysis can support the organisation of large record sets, chronology, correspondence comparison, relationship mapping, inconsistency detection, policy and legislative interrogation, evidentiary-gap analysis and sustained scrutiny over prolonged matters. AI can support evidence analysis; it does not determine whether corruption, misconduct or unlawful conduct has occurred. Those findings require appropriate evidentiary and legal processes.
Frameworks for public sector AI, institutional decision-making, algorithmic systems and accountable governance.
Ensuring data practices respect Indigenous rights, community ownership, and cultural integrity.
Navigating privacy, personal-information and transparency risks in automated and data-intensive environments.
Preparing public organisations for increased external scrutiny through rigorous record-keeping and reasoning.
Using AI-assisted analysis to organise evidence and examine relationships without replacing human judgement or formal accountability processes.