The Creation and Analysis of Government AI Transparency Statements in Australia

2026-04-28

AI Summary

Governments are increasingly deploying AI in public services, yet the actual implementation of transparency commitments remains empirically unassessed, resulting in a gap between policy and practice. This study addresses this shortfall by constructing and publicly releasing AITS-101, the first dataset of Australian government AI transparency statements. Employing a mixed-methods approach that integrates stylometric analysis, quantitative content analysis, and qualitative document analysis, the research systematically examines the content, structure, and patterns of these disclosures. Findings reveal substantial variation across agencies in how AI practices are reported and identify critical gaps in policy implementation. The study thus provides an empirical foundation and an innovative methodological framework to inform the development of more effective, actionable transparency standards for AI use in the public sector.

Abstract

Governments increasingly deploy AI in public services, making transparency essential for accountability and public trust. Australia's Standard for AI Transparency Statements (AITS) requires government bodies to disclose how AI is used in practice, yet little empirical evidence exists on how these requirements are realised in documents. This paper presents the first government AITS dataset, dubbed AITS-101, and provides the first systematic analysis of their content. Using stylometric, quantitative, and qualitative document analyses, we examine disclosure coverage, structure, and recurring patterns. Our findings reveal substantial variation in AI-related practice disclosure, highlight gaps between policy intent and implementation, and inform the design of more effective public-sector AI transparency standards.

Problem

• AI transparency
• government AI
• transparency statements
• public sector
• accountability

Innovation

• AI transparency
• government AI
• disclosure analysis
• stylometric analysis
• public sector AI