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AUSTRALIA. A structured SWOT on the Policy for the Responsible Use of AI in Government (v2.0)

The most powerful and rights-sensitive deployments (mass surveillance, targeting, border profiling, information ops) are exempt from the discipline imposed on the “civilian” state.

Author

Pascal Hetzscholdt
December 05, 2025

A structured SWOT on the Policy for the Responsible Use of AI in Government (v2.0)

by ChatGPT-5.1

Strengths

1. Clear, government-wide governance spine

  • Applies (mandatorily) to all non-corporate Commonwealth entities, with encouragement for corporate entities to follow.

  • Requires designated accountable officials for AI policy implementation, plus accountable use case owners for each in-scope AI use case, and a central use-case register shared with the DTA every 6 months.

    → This directly tackles the “AI just appeared in the workflow and nobody owns it” problem.

2. Risk-based, use-case level oversight

  • In-scope AI is defined via concrete “harm / influence / data sensitivity / public interaction / elevated risk” criteria in Appendix C.

  • All in-scope use cases must undergo an AI impact assessment (using the government tool or an equivalent internal process) before deployment, with explicit handling of medium- and high-risk systems (board or senior-exec oversight, regular review, DTA notification for high-risk).

    → This is more mature than many public-sector practices we have seen in Europe and the US, where risk assessment is often ad hoc or purely privacy-driven.

3. Transparency and public-facing signals

  • Mandates a public AI transparency statement per agency, aligned with a specific transparency standard, reviewed at least annually and notified to DTA.

  • Requires an internal register of AI use cases, giving the centre of government a line of sight on AI deployment across agencies.…

    → Even if the internal register isn’t public, this is a strong starting point for later public disclosure of specific high-risk systems.

4. Embedding “responsible AI” operationally, not just rhetorically

  • Agencies must “operationalise” responsible AI within 12 months, including:

    • an AI adoption process aligned with risk management,

    • staff and public reporting pathways for AI incidents,

    • incident handling integrated with ICT incident management.

  • Mandatory training on responsible AI use for all staff, with additional training for those procuring / building AI.

    → This recognises that AI risk is socio-technical and organisational, not just a model card problem.

5. Coherence with wider frameworks

  • Explicitly plugs into existing AI Ethics Principles, privacy guidance, automated decision-making guidelines and technical standards, rather than reinventing them.

    → You can see the intent: “govern AI as part of the whole data / digital / ethics / security stack”, not as a bolt-on.

6. Thoughtful scoping

  • Excludes “incidental, low-risk” AI (spell-check, search with AI snippets) to avoid bureaucratic overload, while explicitly flagging sensitive domains (recruitment, ADM of discretionary decisions, justice, law enforcement, border control, health, education, critical infrastructure) as needing careful attention.

  • Allows safe experimentation where there’s no committed deployment, no harm, and no new privacy/security risk.

    → This avoids killing experimentation while still pulling real deployments into governance.

Weaknesses

1. Carve-outs exactly where power and risk are highest

  • Defence and the national intelligence community are fully carved out, with only voluntary adoption “where they are able to do so without compromising national security”.

    → In a “technology of power” framing, this is the most worrying part: the most powerful and rights-sensitive deployments (mass surveillance, targeting, border profiling, information ops) are exempt from the discipline imposed on the “civilian” state.

2. Strong on process, weak on substantive limits

  • The policy requires assessments, boards, reviews and transparency—but does notsay:

    • “Some uses are prohibited (e.g. certain biometric mass surveillance)”, or

    • “Some uses require explicit statutory authorisation”.

  • High-risk AI can still be deployed as long as the process boxes are ticked and a board signs it off.

    → This is classic “governance as paperwork”: it may help manage risk, but it doesn’t redraw the boundary of what government should and shouldn’t do with AI.

3. Very little about power asymmetries and vendor dependence

  • The policy barely touches on:

    • concentration of power in a handful of AI vendors,

    • negotiating leverage/safeguards in contracts (data access, logging, auditability, IP and Indigenous data protections in practice),

    • avoiding lock-in or extractive data flows back to Big Tech.
      → In the context of AI as an “architecture of extraction” and technology of power, the policy mostly treats AI as a neutral tool that just needs risk management—rather than as an infrastructure that can re-centralise political, economic and epistemic power.

4. Limited attention to datasets, provenance and IP in day-to-day use

  • IP and Indigenous cultural and intellectual property only appear in the definition of an AI incident (harms that “violate human rights or obligations under applicable law, including intellectual property, privacy and Indigenous cultural and intellectual property”).

  • There is no explicit requirement that:

    • training data or vendor models be checked for lawful provenance;

    • government not feed sensitive third-party content (e.g. publisher content, commercial datasets, citizen documents) into public or opaque models without appropriate licences;

    • generative systems mark or watermark outputs where appropriate.
      → From a scholarly-publishing / content-integrity perspective, this is a hole.

5. Timelines may be slow given AI deployment velocity

  • Agencies have:

    • 6 months to define an AI strategic position,

    • 12 months to stand up responsible AI operational processes and training,

    • 12 months to start use-case assessments,

    • until 30 April 2027 to bring existing use cases into scope and fully compliant.

      → In a world where frontier models, agent frameworks and new use cases are shipping monthly, these timelines risk letting legacy systems run “free” for too long.

6. Transparency stops short of meaningful public contestation

  • Agencies must publish a general AI transparency statement and share detailed use-case registers with DTA—but there is no requirement to publish:

    • a public catalogue of high-risk AI systems,

    • public-facing impact assessments, or

    • channels for affected individuals to contest AI-assisted decisions beyond existing admin-law routes.

      → This weakens democratic oversight and aligns uneasily with concerns about the opacity of AI infrastructures and the erosion of accountability.

Opportunities

1. Use government as a “demand-side regulator”

  • By requiring risk assessments, accountable owners and technical standards, the Commonwealth can nudge vendors toward:

    • better documentation and logging,

    • stronger safety / evaluation pipelines,

    • more robust data, IP and privacy safeguards.
      → Australia could quietly do what the US “Genesis” R&D EO and OSTP RFI are trying to do for AI + science: use procurement and funding as a lever to shape the market, not just regulate it ex post.

2. Bridge to broader AI regulation

  • The policy is explicitly described as something that will “evolve over time as the technology changes, leading practices emerge, the broader regulatory environment develops, and government’s AI maturity improves”.

    → It can become the “public-sector chapter” of a more comprehensive Australian AI framework covering private-sector deployments, infrastructure, and systemic risks.

3. Strengthen protections for data, IP and Indigenous knowledge

  • The inclusion of Indigenous cultural and intellectual property in the AI incident definition is a hook that could be built into:

    • stronger procurement clauses about data provenance and reuse,

    • internal rules about using frontier models on Indigenous or culturally sensitive content,

    • policy on how government data are made available (or not) for training external systems.

      → This aligns well with the concerns about scientific datasets, publisher content and “AI scraping by default”.

4. Lead by example for other mid-sized democracies

  • Compared to many countries, this is:

    • concrete (clear roles, timelines, tools, criteria),

    • aligned with OECD definitions and ethics principles,

    • pragmatic about experimentation.
      → If strengthened, it could become a reference model for governments that lack the EU’s legislative machinery but want something more than a one-page AI ethics pledge.

5. Catalyst for building internal AI literacy

  • Mandatory training plus an internal governance process creates an opportunity to build real AI literacy across the APS—critical for resisting vendor hype, recognising extractive patterns, and understanding when “AI solutions” are misaligned with public value.

Threats

1. Governance theatre / box-ticking risk

  • Without clear consequences (sanctions, public scrutiny, or legal enforceability), the policy could degrade into:

    • pro-forma impact assessments,

    • rubber-stamp governance boards,

    • high-risk systems that are blessed rather than seriously challenged.
      → That is precisely the sort of “alignment theatre” and proceduralism experts frequently critique: a thin layer of process over structurally extractive power.

2. Carve-outs enable a dual AI state

  • Civilian agencies operate under a relatively robust framework, while defence / intelligence systems (often the most powerful and rights-intrusive) operate under looser or secret rules.
    → This dual regime can:

    • undermine public trust,

    • encourage “function creep” via security justifications,

    • make it easier for tools originally built for war / intelligence to bleed back into civilian policing and administration.

3. Vendor capture and “AI as infrastructure” risks

  • If procurement guidance and technical standards are applied weakly, large frontier-model vendors can still:

    • entrench themselves as default infrastructure across government,

    • extract public data and usage patterns as training or product fuel,

    • define de facto standards based on their APIs and dashboards.
      → This dovetails with mapping of AI as an architecture of extraction: the state may become an integrated customer rather than an independent power centre capable of resisting capture.

4. Under-resourced implementation

  • Agencies may lack:

    • skilled staff to do meaningful impact assessments,

    • capacity to monitor deployed systems and interrogate vendor models,

    • the organisational clout to push back on departments wedded to “AI-driven transformation”.
      → If resourcing doesn’t match the ambition, the policy will under-deliver.

5. Global race to the bottom if copied uncritically

  • If other countries copy this as is—particularly the carve-outs and the lack of hard red-lines on certain uses—this could normalise a purely procedural, risk-management framing that treats almost any AI deployment as acceptable with enough paperwork.

How good is this policy?

I’d rate it as:

  • Governance + process quality: relatively high

  • Substantive rights / power-rebalancing quality: moderate at best

It’s clearly written, internally coherent, aligned with international definitions, and strong on roles, registers, risk assessments and training. For the “ordinary” parts of the state—tax, welfare, licensing, service delivery—this is a solid spine and well ahead of jurisdictions that still have almost nothing.

But in the broader lens:

Key changes I’d recommend

If I were advising Australia (or another country) from a scholarly-publishing / democratic-governance standpoint, I’d push for:

  1. Narrow the carve-outs and add guardrails for security-sector AI

    • Require defence and NIC to:

      • adopt core elements (impact assessments, incident reporting, accountable owners),

      • report (even if classified) to an independent parliamentary or inspector-general body.

    • Explicitly ban certain uses (e.g. untargeted biometric mass surveillance, certain predictive policing tools) absent specific legislation.

  2. Add substantive red-lines and “special regime” categories

    • Identify use classes that are:

      • Prohibited (unless authorised by law with strict safeguards),

      • Strictly regulated (e.g. decisions with major impact on liberty, livelihood, or fundamental rights).

    • Tie these categories to enhanced transparency (public registers), independent audits and rights of explanation / redress.

  3. Strengthen data, IP, and content-provenance obligations

    • Require agencies to ensure, and vendors to contractually warrant, that:

      • training data used for models consumed by government have lawful provenance and respect IP and Indigenous rights;

      • government does not feed third-party content (e.g. scientific articles, proprietary databases) into public models without appropriate licences;

      • content provenance and watermarking standards are used where appropriate for generated outputs.

  4. Make transparency more meaningful

    • Move towards:

      • a public register of high-risk AI systems and key impact-assessment findings;

      • plain-language notices where citizens are subject to AI-assisted decisions;

      • standardised mechanisms for contesting AI-influenced decisions.

  5. Resource and empower central oversight

    • Ensure the DTA (or successor) has:

      • technical capacity to interrogate models and vendor claims;

      • powers to issue guidance, demand remediation, or recommend suspension of non-compliant systems.

  6. Explicitly address structural power and vendor dependence

    • Integrate into procurement and strategy:

      • preferences for interoperable, open standards, and open-source or local models where viable;

      • caps or checks on single-vendor dependency;

      • evaluation of geopolitical and economic risks of outsourcing core informational functions to foreign frontier-model providers.

Should other countries follow this model?

Yes, but with modifications and eyes open.

  • For countries with almost nothing in place:

    • This is a good first-generation blueprint for public-sector AI governance: clear roles, risk assessments, training, and alignment with OECD definitions and ethics. Importing this framework (with local adjustments) is far better than doing nothing or relying on generic digital-strategy language.

  • For countries already moving toward comprehensive AI acts (EU, UK in part, Canada, etc.):

    • This can serve as an internal-government implementation layer under a broader legal framework—but they should add:

      • stronger prohibitions,

      • explicit fundamental-rights protections,

      • tighter controls on high-risk security / surveillance uses,

      • industrial-policy elements that reduce dependence on a small cluster of frontier providers.

  • From an “architecture of extraction” perspective:

    • If adopted unchanged by others, it risks entrenching AI governance as a paperwork exercise that leaves underlying power structures intact.

    • If used as a base and extended with serious constraints on vendor power, data extraction and high-risk deployments, it can be a constructive step toward a more balanced AI ecosystem.

In short:

  • As a governance skeleton: worth emulating.

  • As a complete answer to AI as a technology of power: not nearly enough, and it needs precisely the kind of structural, rights-focused and anti-extraction additions AI experts have been arguing for.