‘Polyanna policy’ – is NZ’s framework for AI use in government overly optimistic?

The New Yorker magazine’s recently published investigation^[1] into OpenAI chief executive Sam Altman posed a loaded question: can the people building this powerful technology actually be trusted?

The report described a system where commercial incentives drive behaviour and oversight is treated as a nuisance. In Aotearoa New Zealand, there is a similarly urgent question: can the governance frameworks we are building to manage that technology be trusted to work?

This is particularly relevant to the public service and government agencies, now being encouraged to embrace AI^[2]. At a recent International Research Society for Public Management^[3] conference, the global research community grappled with how AI can align with the public interest.

A clear divergence is emerging. Some jurisdictions are building surveillance-heavy data systems, while others are constructing robust, binding systems to protect citizen consent.

Aotearoa New Zealand occupies a precarious middle ground. The Public Service AI Framework^[4] names the right principles: transparency, fairness and human oversight. But it is explicitly non-binding.

We have dubbed it a “Pollyanna policy” – based on the Pollyanna principle^[5] which describes a general bias towards positivity and optimism about outcomes.

AI and institutional complexity

In the area of AI governance, this becomes a matter of stating good intentions and issuing non-binding guidance, trusting existing frameworks will absorb genuinely novel challenges.

We argue this underestimates the institutional constraints, conflicting incentives and strategic vulnerability of that middle ground, without legislative armour to protect citizen data.

It also underestimates the “institutional friction^[6]” that defines modern public institutions where many people and departments have power over policy. This tends to weaken responsiveness to problems.

Governance in a typical public sector agency is not a clean, ordered structure. It is an accumulation^[7] of layer upon layer of policy, operational procedure, ministerial expectation, legislative obligation and professional conventions.

New regulatory instruments rarely replace old ones. They are added alongside them^[8], often interacting in unpredictable ways.

AI is a “flat” technology that processes information as a statistical landscape. It lacks the institutional memory to know that a prompt today might quietly undermine the kinds of political and constitutional compromises, made over time, that are central to effective government.

The accountability gap

Before AI can be usefully deployed, agencies must do the diagnostic work^[9] of understanding what that governance environment actually is.

The instinct instead is to add further AI governance guidance to a system already straining under accumulated advice.

As Australia’s Royal Commission into the Robodebt Scheme^[10] demonstrated, algorithmic systems deployed without this kind of clarity can produce catastrophic harm.

The non-binding nature of the Public Service AI Framework abdicates central responsibility, offloading accountability to individual agencies with vastly different levels of capability.

Algorithmic decision-making disrupts traditional accountability^[11] within organisations because information, justification and consequences can no longer be traced through a single responsible chain.

The framework assumes organisational readiness, but the evidence does not support this. The 2025 Public Service Census^[12] found that while a third of public servants had used AI for work, only 14% used it regularly.

This gap is driven by the friction that arises when a general-purpose tool like AI meets the accumulated complexity of how decisions actually get made.

Don’t get us wrong: the framework names the right principles. But principles without a legislative mandate become aspirational without accountability. In an already strained system, another non-binding document changes very little.

Māori data sovereignty

In Aotearoa New Zealand, this governance vacuum has an added dimension. Māori data sovereignty^[13] is a constitutional imperative under the Treaty of Waitangi, not a technical add-on.

The bureaucracy has an obligation to protect Indigenous sovereignty, yet the current approach leaves the gate unguarded. As legal scholars Woodrow Hartzog and Jessica Silbey argue in their 2025 article “How AI Destroys Institutions^[14]”:

AI systems are built to function in ways that degrade and are likely to destroy our civic institutions. [They] have the effect of eroding expertise, short-circuiting decision-making, and isolating people from each other.

This risk is amplified by the current state of AI technology. “Hallucinations^[15]” are not bugs but inherent statistical features^[16] of large language models. And evidence confirms^[17] they remain a risk in high-stakes settings such as medicine.

The Five Eyes security agencies evidently agree: their April 2026 joint guidance^[18] on agentic AI^[19] calls explicitly for incremental deployment, continuous threat assessment and sustained human oversight.

When these models hallucinate legal facts^[20] they risk overwriting Indigenous knowledge with plausible fictions.

This is compounded by the “sycophantic” tendency of large language models to mirror the user’s bias. In a policy system grappling with the legacy of colonisation, this can simply reinforce an echo chamber.

Building a counterweight

Used well, AI does present opportunities. It can reveal inconsistencies in policy frameworks and interrogate inherited assumptions.

But realising that opportunity requires that organisations understand themselves well enough to know where the technology will fail.

If these AI tools are built by companies where incentives outpace ethics^[21], the burden on public institutions is to act as a genuine counterweight.

This means moving beyond “responsible” adoption toward creating formal, protected roles where officials interrogate AI output for bias and fabrication, rather than accepting speed as a proxy for quality.

The strategy, standards and guidance documents^[22] point in the right direction. But in the gap between aspiration and accountability, we must ask whether we continue to rely on optimism, or will we build the strong, ethical oversight capable of catching what the technology cannot?

References

1. ^{^} recently published investigation (www.newyorker.com)
2. ^{^} encouraged to embrace AI (www.beehive.govt.nz)
3. ^{^} International Research Society for Public Management (www.irspm.org)
4. ^{^} Public Service AI Framework (www.digital.govt.nz)
5. ^{^} Pollyanna principle (positivepsychology.com)
6. ^{^} institutional friction (journals.sagepub.com)
7. ^{^} an accumulation (doi.org)
8. ^{^} added alongside them (global.oup.com)
9. ^{^} diagnostic work (doi.org)
10. ^{^} Royal Commission into the Robodebt Scheme (robodebt.royalcommission.gov.au)
11. ^{^} disrupts traditional accountability (doi.org)
12. ^{^} 2025 Public Service Census (www.publicservice.govt.nz)
13. ^{^} Māori data sovereignty (press.anu.edu.au)
14. ^{^} How AI Destroys Institutions (papers.ssrn.com)
15. ^{^} Hallucinations (arxiv.org)
16. ^{^} inherent statistical features (arxiv.org)
17. ^{^} evidence confirms (doi.org)
18. ^{^} joint guidance (www.ncsc.govt.nz)
19. ^{^} agentic AI (mitsloan.mit.edu)
20. ^{^} hallucinate legal facts (doi.org)
21. ^{^} incentives outpace ethics (katiecouric.com)
22. ^{^} strategy, standards and guidance documents (www.digital.govt.nz)