Governments worldwide are investing in AI to improve efficiency and service delivery. However, scaling AI initiatives presents challenges, from ethical concerns to bureaucratic resistance.
Explore AI in government applications, best practices, and real-world examples.
What does artificial intelligence offer to governments?
Artificial intelligence provides governments with capabilities similar to those in the private sector, enhancing government operations across various domains. These offerings can be categorized into three key areas:
1. Savings due to operational efficiency
AI-driven automation helps government agencies optimize workflows, manage service delivery, and reduce administrative burdens. AI tools powered by machine learning techniques can process data sets more efficiently than traditional methods, leading to improved cost savings.
Federal agencies and local governments can leverage AI for fraud detection, personnel management, and code generation, ensuring more effective resource allocation.
2. New and improved services
AI adoption enables state and local governments to enhance customer experience through intelligent AI applications. Some examples include autonomous vehicles like self-driving shuttles improving public transportation and natural language processing enabling better citizen engagement.
Personalized AI training in education and AI-powered healthcare solutions further demonstrate how emerging technologies can improve services for all citizens, including underserved communities and marginalized communities.
3. Enhanced data-driven decision making
Governments collect tens of thousands of data points daily, but without advanced analytics, this input data is underutilized. AI technologies allow decision makers to analyze data, predict outcomes, and identify patterns more effectively.
By using AI-powered computer vision, deep learning, and data science, public agencies can make informed policy decisions, enhance security measures, and protect national interests.
Additionally, AI aids in technology policy development, ensuring the responsible use of AI in governance.
Use cases of AI in government: Government services
Social welfare
Identifying fraudulent benefits claims: Fraudulent claims can cost governments billions. For instance, the federal government is projected to incur annual losses ranging from $233 billion to $521 billion due to fraud.1 AI-powered fraud detection can enable governments to track down large-scale corruption of the benefit and welfare programs by:
- Identifying patterns in claims such as the same phone number or applications written in the same style
- Processing social media profiles to check if there are any conflicting information compared to the applications. However, this may be perceived as an infringement of personal data in many countries.
Healthcare
Tracking disease spreads: AI can be used to prevent disease spread:
- Building a machine learning algorithm that cross-checks patients with similar symptoms from different locations, detects patterns, and warns when an outbreak might occur.
- Using graph analytics, as in the case of China during COVID-19, to identify contacts with a known carrier of the virus
Triaging patients: Though triaging patients has been used in hospitals’ emergency services, triaging became necessary after Coronavirus spread. AI-powered tools can analyze patient data to predict patients’ risk scores so that doctors can prioritize.
Handling citizens health related queries: Public health was endangered by misinformation about pandemic measures, particularly at the beginning of the COVID-19 pandemic. For example, misinformation about COVID-19 in Canada resulted in at least 2,800 deaths and $300 million in hospital costs over a nine-month period during the pandemic.2
Conversational AI technologies can assist governments in informing their people and assisting authorities in responding to frequently requested queries about health.
Domestic security
Predicting a crime and recommending optimal police presence: AI can be used to identify patterns in policing heat maps to forecast where and when next crimes are likely to occur (See figure below).
Though AI algorithms’ fairness in predictive policing is still questionable and it doesn’t favor minority groups, AI-based recommendations can be used to identify optimal police patrol presence.
Figure 1: Oakland PD’s crime map for 90 days.3
Surveillance: AI surveillance describes the process of ML and DL-based algorithms analyzing images, videos, and data recorded from CCTV cameras.
Though techniques like facial recognition enable governments to identify people from video records, the ethical side of AI-powered surveillance is still controversial. For instance, IBM stopped offering, developing, or researching facial recognition technology for mass surveillance due to racial profiling and violations of basic human rights and freedoms.
Military
Autonomous drones: Autonomous military drones are also referred to as Unmanned combat aerial vehicles (UCAV), are military weapons that carry combat payloads like missiles are usually under real-time human control, with varying levels of autonomy.
One of the latest examples of military drones, though they were mostly piloted by humans, were used by Azerbaijan at Nagorno-Karabakh in the combat against Armenia.4
Transportation
Self-driving shuttles: Autonomous shuttles are a flexible solution to move people at sub-50km/h speeds along predetermined, learned paths like industrial campuses, city centers, or suburban neighborhoods. Self-driving shuttle trial deployments are expected to accelerate quickly because:
- The shuttle segment is less regulated than the automotive market.
- Consumers’ trust in autonomous shuttles is higher than other autonomous vehicles. According to a survey conducted by the University of Michigan, 86% of riders said they trusted shuttles after riding in it, as did 66% of non-riders.5
Monitoring social media to identify incidents: Traffic congestions are an issue for citizens and governments alike. Congestions happen mostly due to accidents on roads, and it negatively impacts travel times, fuel consumption, and carbon emissions. Artificial intelligence can be used to monitor social media to identify tweets about recent accidents.
Education
- Personalized education: ML algorithms can help provide personalized education irrespective of the number of students. AI can analyze students’ progress and find the gaps between what is taught and what is not yet understood.
- Marking exam papers: Automated text analysis reviews students’ work to identify strengths and recommend revisions.
Emergency
- Classifying emergency calls based on their urgency: Voice recognition technologies & ML algorithms can help governments automate emergency call lines by understanding and categorizing queries.
- Fire prediction: ML & DL algorithms can map the dryness of forests to predict wildfire better. For example, researchers at the University of Southern California (USC) have developed a model that integrates generative AI with satellite imagery to predict the spread of wildfires accurately. The research team analyzed historical wildfire data from high-resolution satellite images to identify patterns in ignition, spread, and containment based on weather, fuel types, and terrain. Using this data, they trained a generative AI model (cWGAN) to predict wildfire behavior, which accurately forecasted fire spread in California from 2020 to 2022.6
Use cases of AI in government: Public relations
Customer service chatbots: Chatbots enable governments to perform a variety of tasks, including:
- Scheduling meetings.
- Answering FAQs.
- Directing requests to the appropriate area within government.
- Filling out forms.
- Assisting with searching documents.
Other use cases
- Document automation: Itincludes extraction and inputting of invoices, architectural drawings, certificates, charts, drawings, forms, legal documents, and letters.
- Drafting documents & announcements: Automated content can be generated with Natural Language Generation (NLG), which is already being used in some newsrooms.
- Translation: AI enables a more efficient translation of government information.
AI in government case studies
| Country | Instituite | Application | Results |
|---|---|---|---|
| Australia | Taxation Office | Chatbot/Virtual assistant | Had more than 3 million conversations and resolved 88% of queries on first contact. |
| Australia | Department of Human Services | Chatbot/Virtual assistant | Answered general questions about family, job seeker and student payments and related information. |
| Canada | Surrey Municipial | Chatbot/Virtual assistant | Helped the residents of the city get answers to questions related to municipal infrastructure. |
| United States | Atlanta Fire Rescue Department (AFRD) | Predictive Analytics | Accurately predicted 73% of fire incidents in the building. |
| United States | Department of Energy | Solar Forecasting | Provided city residents with answers to municipal infrastructure questions up to 30% faster than traditional methods. |
| United States | New York City Department of Social Services (DSS) | Machine Vision | Achieved digitization of documents. |
| United States | City of Pittsburgh | Automated traffic optimization | Scalable Urban Traffic Control (SURTrAC) connected to a network of nine traffic signals, optimizing traffic flow across three major Pittsburgh roads. |
What are the challenges of AI in the public sector?
Employment
Unemployment can be the scariest part of artificial intelligence if we disregard the hypothetical scenario of an AI takeover. Governments, as public service providers, should be concerned about the impact of AI on human jobs in government.
To mitigate the impact of potential unemployment due to automation, governments need to ensure that humans focus on higher value-added tasks or move on to the private sector if their current tasks are going to be automated.
According to the European Commission’s Eurobarometer survey7 that presents European citizens’ thoughts on the influence of digitalization and automation on daily life:
- A majority (73%) agree that robots and AI accelerate the speed at which workers perform tasks, with nearly a quarter (24%) strongly agreeing, while 20% disagree.
- Two-thirds (66%) believe that AI and robots take away jobs from workers, with 28% in full agreement.
- Over six in ten (61%) think AI harms workplace communication, with almost a quarter (24%) expressing strong agreement.
AI biases
AI algorithms may contain biases due to prejudices of the algorithm development team or misleading data. Though building an unbiased AI algorithm is technically possible, AI can be as good as data, and people are the ones who create data. Therefore, the best thing governments can do for AI bias is minimizing it by applying best practices.
Explainability
It is not easy to explain how all AI algorithms arrive at their predictions (i.e., inferences) however there are technical approaches being developed to overcome this shortcoming.
This is problematic for the public sector, where providing a rationale for decisions is more important than the private sector since the public sector is accountable to the public. In contrast, the private sector is foremost accountable to shareholders.
Accountability
Accountability of AI systems is an issue of AI ethics. Governments in the US and the UK are introducing new laws about companies’ AI algorithms’ accountability. It will be hypocrisy if governments and companies are not held accountable for accidents & false predictions their AI algorithms make.
Check out responsible AI best practices to learn more.
Difficulty of transformation
AI transformation in government can be difficult because:
- Age of public servants: As of 2024, the average age of U.S. federal government employees is approximately 47 years.8 The workforce at the government is older than the private sector, making it potentially harder to implement to the culture change.
- More ambiguous/complex KPIs: Compared to the private sector’s drive for profit, governments have more complex, harder-to-measure goals. As a result, government KPIs tend to be more activity-oriented rather than result-oriented making it harder to measure improvements.
- Number of stakeholders: Government watchdogs, labor unions, and opposition parties are all stakeholders whose view of AI will shape how the public will perceive AI in government. This makes communication about transformation projects even more important.
What are the best practices of AI for governments?
By investing in AI capabilities, fostering public-private partnerships, and prioritizing AI workforce development, government agencies can responsibly harness the full potential of AI:
1. Public-private partnerships: Driving AI innovation
Governments should collaborate with AI vendors, research institutions, and private-sector organizations to accelerate discovery and enhance AI capabilities.
For example, federal agencies have engaged with universities and the National Institute of Standards and Technology (NIST) to advance fundamental AI research and establish AI governance frameworks. Such collaborations can fuel AI investments and improve services by leveraging expertise from subject matter experts in data science, computer science, and machine learning techniques.
2. AI sandboxes for testing and ethical evaluation
AI regulatory sandboxes provide controlled environments where government agencies can test AI tools before full-scale deployment. These sandboxes allow decision-makers to analyze data, identify patterns, and refine AI algorithms while ensuring compliance with ethical and legal standards.
For example, The UK’s Information Commissioner’s Office (ICO) introduced AI regulatory sandboxes to evaluate ethical AI use, providing insights into AI applications in fraud detection and public service delivery.
3. Modernizing technology infrastructure for AI integration
The successful implementation of AI technologies in government requires upgrading legacy IT systems. Modern cloud computing solutions and edge AI enhance scalability, enabling real-time data processing and AI-driven decision-making.
Federal and localgovernments investing in AI infrastructure can leverage machine learning techniques and deep learning models to optimize government operations.
AI adoption in public-sector IT systems also helps predict outcomes and automate service delivery, reducing the burden on government employees.
4. AI workforce development and talent recruitment
Federal and state agencies must prioritize AI talent recruitment, offering AI training programs to up skill government personnel. AI task forces should be established to oversee AI system development and implementation, ensuring agencies are equipped with AI talent to handle complex AI applications.
With the expanding government use of AI, specialized expertise in computer vision, data science, and machine learning is required. AI training initiatives can bridge the talent gap, ensuring public agencies have the necessary skills to deploy AI tools responsibly.
Additionally, partnerships with universities can provide structured AI development programs to strengthen personnel management in AI-driven roles.
5. Ensuring AI fairness, accountability, and compliance
To mitigate biased results and ensure AI is used ethically, governments must implement strong oversight mechanisms. AI ethics boards, in coordination with subject matter experts, can help establish guidelines for AI research, AI investments, and AI system governance.
Regulatory frameworks like the EU AI Act and the U.S. Executive Order on AI emphasize protecting privacy, ensuring human rights, and preventing the misuse of AI technologies.
Transparency laws require AI algorithms to be explainable, reducing the risk of discrimination against marginalized and underserved communities. AI-powered systems in government use should align with principles of responsible AI, ensuring that decision-making processes remain transparent and equitable.
External Links
- 1. Fraud Risk Management: 2018-2022 Data Show Federal Government Loses an Estimated $233 Billion to $521 Billion Annually to Fraud, Based on Various Risk Environments | U.S. GAO.
- 2. COVID-19 misinformation cost at least 2,800 lives and $300M, new report says | CBC News. CBC
- 3. The Truth About Predictive Policing and Race | by Andrew Guthrie Ferguson | IN JUSTICE TODAY | Medium. IN JUSTICE TODAY
- 4. nytimes.com. The New York Times
- 5. Mcity Driverless Shuttle research reports findings after safe project conclusion | University of Michigan News.
- 6. Scientists use AI to predict a wildfire’s next move — USC News. University of Southern California
- 7. Eurobarometer.
- 8. Six charts on the age of federal workers | USAFacts. USAFacts
Comments
Your email address will not be published. All fields are required.