The university of Queensland

A key priority would be to see a set of practical steps toward interoperability such as shared taxonomies, common definitions, baseline risk categories, and mechanisms for mutual recognition. This will assist avodiing or minimisign fragmentation while respecting national contexts.

• Social, economic, ethical, cultural, linguistic and technical implications of AI
• Interoperability of governance approaches
• Transparency, accountability, and human oversight
• AI capacity-building

Strengthening global AI capability is essential for ensuring all countries can participate in and benefit from AI. For Australia, capacity building is critical to reduce global asymmetries that could destabilise security or economic systems (amongst other reasons). Australia has a diverse population and a commitment to inclusive digital transformation. Focusing on these implications ensures that AI systems reflect cultural and linguistic diversity, and recognise the importance of indigenous data sovereignty.

The importance of indigenous data sovereignty.

Australia is already experiencing the effects of governance gaps and rapid advances across the four selected thematic areas, particularly within the public sector where AI adoption is accelerating but institutional readiness varies significantly. Capacity‑building gaps are creating uneven capabilities across jurisdictions, leaving agencies reliant on external vendors and limiting the government's ability to independently assure AI tools. At the same time, rapid advances in generative and predictive AI offer major opportunities for service delivery; however, without coordinated uplift in skills, technical infrastructure, and regulatory literacy, I fear these benefits cannot be realised safely or consistently.

A key opportunity may be the ability of the AI Dialogue to enable coordinated responses to emerging critical risks, including cross‑border harms, supply‑chain vulnerabilities, and societal impacts of AI models. The Dialogue can illuminate the need for human rights, transparency, and accountability to be global norms.

I would love to see a dedicated working group on Indigenous Data Sovereignty and the use of AI systems - there seems to be a lack of focus on this in Australia.

I would see industry contributing technical expertise and real‑time insights into emerging capabilities and risks, and practical knowledge of implementation challenges - this is a key component of building capability. Academia provides independent research, ethical analysis, and community perspectives, ensuring that governance frameworks reflect societal values, protect rights, and address impacts on diverse populations. The structure could include plenary sessions, as well as thematic working groups. Advisory panels on "Wicked problems" would also be interesting and generate insights.

Indigenous peoples are underrepresented, despite being disproportionately affected by data practices, algorithmic bias, and the deployment of AI in public services. Is it possible for the AI Dialogue to provide funded participation pathways for resource‑constrained countries? I would suggest establishing dedicated advisory panels for Indigenous and marginalised communities.

Virtual and augmented reality environments could be used to simulate AI‑driven scenarios - this would be innovative and engaging! Also, a series of "governance labs" could bring together governments, industry, academia, to co‑design policy prototypes

Within Australia, I suggest the following artefacts: 1. Digital Transformation Agency - AI Policy Update: Strengthening Responsible Use Across Government 2. The NSW AI Assessment Framework (AIAF) is an operationalised AI assurance framework and provides a mandatory, whole-of-government risk assessment and assurance process for all AI systems used across NSW Government agencies. It is widely regarded as a leading example of practical, risk-based AI governance. Refer to: NSW AI Assessment Framework (Digital NSW)