Local governments are using AI more and more. But are they doing it wisely?

This article is republished from The Conversation under a Creative Commons license. Read the original article.

The most visible faces of artificial intelligence (AI) are things like ChatGPT, self-driving cars and humanoid robots. But AI is also quietly making inroads in our neighbourhoods.

Across the world, local governments already use AI to answer questions, guide planning enquiries, manage infrastructure, monitor the environment, and improve public services.

AI can help local governments meet rising community expectations while facing budget pressure, ageing infrastructure and workforce constraints.

But becoming smarter is only half the challenge. The real test is whether AI is used responsibly, transparently and in ways communities can understand and trust.

In a new report, we and our colleagues examine how AI is reshaping local government – and how we can make sure it really serves the public interest.

AI Is Already Changing Local Government

Local governments around the world are already implementing AI systems in customer service, operations, infrastructure, environmental management and surveillance.

One of the most visible changes is in customer service, where AI-powered systems often handle routine enquiries. In the United States, the City of Amarillo uses a virtual assistant to handle about 16,000 resident enquiries each month, helping defer a US$1.8 million call centre expansion.

In the United Kingdom, two councils use a chatbot named ELLIS to respond across more than 1,000 local government topics, allowing staff to focus on complex cases.

In less-visible administrative work, Cumbria County Council in the UK introduced a new tool to automatically process some parking permit applications and school invoices, saving over one tonne of paper in three months.

Planning and infrastructure management can also benefit. In Sweden, Helsingborg City equipped waste-collection vehicles with AI image recognition to detect potholes.

In the US, Boston adopted an AI system to optimise traffic signals, reducing average delays by 13.5% and unnecessary stops by 20% at some intersections.

Environmental management is another expanding application. In Australia, Brimbank City Council uses computer vision on waste collection vehicles to identify damaged roadside assets.

In China, Chongqing city uses an AI-enabled system to monitor vegetation and identify pest infestations and generate maintenance work orders.

Smarter Local Governments Need Smarter Governance

How can local governments make sure these technologies are used fairly, transparently and in the public interest?

Local governments remain accountable for outcomes, even when AI systems make decisions. Residents rightly expect those decisions to be fair, transparent and aligned with public values.

An example from the UK shows what this might look like. One London council is using an AI tool to cut the time social workers spend on writing case notes and assessments by up to 60%, while retaining privacy safeguards, consent and human review.

Researchers often regard responsible AI as a broad policy ideal. Our findings show that, for local governments, it is becoming something more tangible: a public expectation that differs from place to place.

6 Priorities for Local Government AI

Drawing on policy analysis, community surveys, and interviews with local government practitioners, our study found AI adoption is outpacing the governance needed to ensure its responsible use.

We identify six priorities for responsible AI adoption in local government:

1. clear accountability. Governments need to define where AI can be used, who is accountable for its use, and when human oversight is required

2. organisational capability. Elected representatives, executives, and staff need a thorough understanding of what AI can do and what its limitations are

3. data and technical safeguards. Strong systems for data governance and privacy protection, as well as built-in safeguards, can make sure AI systems are secure and reliable, and keep bias and other harms to a minimum

4. transparency and human review. Residents need to know when AI is being used, how it influences decisions, and how they can request human review where appropriate

5. community participation. Communities need meaningful opportunities to help shape how AI is used in local government and the services that affect their lives

6. workforce preparation. Local governments should invest in the skills of their workers so AI can augment human expertise, enhance public service delivery, and support the local government workforce rather than replacing it.

Public Trust Is Essential

AI has enormous potential to help local governments deliver faster, fairer and more responsive public services.

But they will need to build public trust by showing their AI systems are reliable, accountable and used for a clear public purpose. That requires transparency, human oversight, ongoing evaluation and meaningful community engagement, not simply better algorithms.

Our research found that, across five countries (Australia, the US, Spain, Hong Kong, and Saudi Arabia), public trust consistently shaped how people perceived the risks of AI use by local governments.

One way to build public trust is to make AI visible, accessible and useful in people’s interactions with local government. In Australia, the City of Stirling introduced an AI-supported engagement platform that helps residents to ask questions and participate in consultations.

In the UK, a town planning body has piloted a system for summarising public submissions which has made responses much faster. By letting residents engage with AI directly or see its benefits in consultation, these tools can build public confidence.

Responsible AI governance is not just a technical or regulatory challenge. It is essential for ensuring AI is used ethically, transparently, and in ways that reflect community values and expectations.

Tan Yigitcanlar, Professor of Urban Studies and Planning, Queensland University of Technology; Anne Jeevana David, PhD Researcher, Queensland University of Technology; Ke Liu, PhD Researcher, Queensland University of Technology; Marasinghe Pelige Raveena Lakmini, PhD Researcher, Queensland University of Technology, and Sajani Senadheera, PhD Researcher, School of Architecture and Built Environment, Queensland University of Technology