Politicians are pushing AI as a quick fix to Australia’s housing crisis. They’re risking another Robodebt
- Written by: Ehsan Nabavi, Senior Lecturer in Technology and Society, Responsible Innovation Lab, Australian National University
“This is a game changer”.
That’s how Paul Scully, New South Wales Minister for Planning and Public Spaces, described[1] the state government’s launch of a tender for an artificial intelligence (AI) solution to the housing crisis earlier this month.
The system, which is aimed at cutting red tape and getting more homes built fast, is expected[2] to be functioning by the end of 2025.
“This is allowing construction to get underway and new keys into new doors,” Scully added.
The announcement was later endorsed[3] by federal treasurer Jim Chalmers as a model for other states and territories to replicate, to “unlock more housing” and “boost productivity across the economy”.
Speeding up building approvals is a key concern of the so-called abundance agenda[4] for boosting economic growth.
Those wheels are already in motion elsewhere in Australia. Tasmania is developing an AI policy[5], and South Australia is trialling a small-scale pilot[6] for specific dwelling applications to allow users to submit digital architectural drawings to be automatically assessed against prescribed criteria.
But will AI really be a quick fix to Australia’s housing crisis?
Cutting red tape
Housing and AI were both key themes at last month’s productivity roundtable.
In a joint media release[7], federal Minister for Housing Clare O’Neil and Minister for the Environment and Water Murray Watt said “easing the regulatory burden on builders” is what Australia needs.
They point to the backlog of 26,000 homes currently stuck in assessment under environmental protection laws as a clear choke point. And AI is going to be used to “simplify and speed up assessments and approvals”.
None of this, however, explains AI’s precise role within the complex machinery of the planning system, leaving much to speculation.
Will the role of AI be limited to checking applications for completeness and classifying and validating documents, as Victorian councils[8] are already exploring? Or drafting written elements of assessments, as is already the case in the Australian Capital Territory[9]?
Or will it go further? Will AI agents, for example, have some autonomy in parts of the assessment process? If so, where exactly will this be? How will it be integrated into existing infrastructure? And most importantly, to what extent will expert judgement be displaced?
A tempting quick fix
Presenting AI as a quick fix for Australia’s housing shortage might be tempting. But it risks distracting from deeper systemic issues[10] such as labour market bottlenecks, financial and tax incentives, and shrinking social and affordable housing.
The technology is also quietly reshaping the planning system – and the role of planners within it – with serious consequences.
Planning is not just paperwork waiting to be automated. It is judgement exercised in site visits, in listening to stakeholders, and in weighing local context against the broader one.
Stripping that away can make both the system and the people brittle, displacing planners’ expertise and blurring responsibility when things go wrong. And when errors involving AI happen, it can be very hard to trace them, with research showing explainability[11] has been the technology’s Achilles’ heel.
The NSW government suggests[12] putting a human in charge of the final decision is enough to solve these concerns.
But the machine doesn’t just sit quietly in the corner waiting for the approve button to be pressed. It nudges. It frames. It shapes what gets seen and what gets ignored in different stages of assessment, often in ways that aren’t obvious at all.
For example, highlighting some ecological risks over others can simply tilt an assessor’s briefing, even when local communities might have entirely different concerns. Or when AI ranks one assessment pathway as the “best fit” based on patterns buried in its training data, the assessor may simply drift toward that option, not realising the scope and direction of their choices have already been narrowed.
Lessons from Robodebt
Centrelink’s Online Compliance Intervention program – more commonly known as Robodebt – carries some important lessons here. Sold as a way to make debt recovery more “efficient”, it soon collapsed into a $4.7 billion fiasco[13].
In that case, an automated spreadsheet – not even AI – harmed thousands of people, triggered a hefty class action and shattered public trust in the government.
If governments now see AI as a tool to reform planning and assessments, they shouldn’t rush in headlong.
The fear of missing out may be real. But the wiser move is to pause and ask first: what problem are we actually trying to solve with AI, and does everyone even agree it’s the real problem?
Only then comes the harder question of how to do it responsibly, without stumbling into the same avoidable consequences as Robodebt.
Responsible innovation offers a roadmap forward
Responsible innovation[14] means anticipating risks and unintended consequences early on – by including and deliberating with those who will use and be affected by the system, proactively looking for the blind spots, and being responsive to the impacts.
There are abundant research case studies, tools and frameworks in the field of responsible innovation that can guide the design, development and deployment of AI systems in planning. But the key is to engage with root causes and unintended consequences[15], and to question the underlying assumptions about the vision and purpose of the AI system.
We can’t afford to ignore the basics of responsible innovation. Otherwise, this so-called “gamechanger” to the housing crisis might find itself sitting alongside Robodebt as yet another cautionary tale of how innovations sold as efficiency gains can so go wrong.
The author would like to acknowledge the enormous contribution of Negar Yazdi, an experienced urban planner and a member of ANU’s Responsible Innovation Lab and Planning Institute of Australia, to this article.
References
- ^ described (www.planning.nsw.gov.au)
- ^ expected (www.planning.nsw.gov.au)
- ^ endorsed (www.afr.com)
- ^ abundance agenda (www.chifley.org.au)
- ^ developing an AI policy (ia.acs.org.au)
- ^ trialling a small-scale pilot (www.premier.sa.gov.au)
- ^ joint media release (ministers.treasury.gov.au)
- ^ Victorian councils (www.mav.asn.au)
- ^ Australian Capital Territory (ia.acs.org.au)
- ^ deeper systemic issues (nhsac.gov.au)
- ^ explainability (doi.org)
- ^ suggests (www.nsw.gov.au)
- ^ a $4.7 billion fiasco (www.afr.com)
- ^ Responsible innovation (doi.org)
- ^ root causes and unintended consequences (doi.org)
