Had you asked a colleague five years ago whether they anticipated using Artificial Intelligence (AI) tools for work, they likely would have laughed off your question. But today, most people are accessing AI support for writing, research, visuals and so much more. As these tools seem to shape-shift each week, AI’s untapped potential within the public sector is becoming more evident.
With the power of AI tools continuing to grow, governments around the world are scrambling to respond. Just last month, the UK government published a white paper on artificial intelligence, marking a first step towards regulating AI across key principles such as safety and fairness, while in the US, governments are approaching AI regulation on a local state-basis for now and in Italy the government issued an order to ban the popular chatbot tool, ChatGPT.
The rapid development of AI tools and the changing regulations around them are two of the factors influencing public servants’ slow adoption of them. Which tools should you learn to use? What should you prompt them to do? How do you know if they’re safe to use, and leading you in the right direction? The overwhelm is clear: according to a March 2023 poll by Apolitical, only 15% of public servant respondents stated they often use AI tools. If we want the public sector to innovate at the speed necessary to meet growing challenges across our communities, this problem needs addressing.
According to The Federal Times, an “independent survey of federal government executives revealed that while AI is on respondents’ radar, fewer than one in five are ‘very’ likely to adopt AI in the next year. Further, it indicates that AI readiness is a major barrier to implementation with one-third of respondents stating they do not believe their agency is ready for AI.” And yet, outside of the public sector we have seen a rapid adoption of AI; ChatGPT – OpenAI’s chatbot tool which has taken the world by storm – has averaged around 100 million monthly active users since its launch just a few months ago.
So, how can we bridge the divide between how quickly technology is being embraced by the general public and how bureaucratically governments are responding? And how can we ensure that overwhelm by all the options doesn’t prevent adoption? By honing in on what is most likely to improve the public sector for the people — both within, and affected by it.
As list-after-list of the latest AI tools continue to get published, governments are scrambling to respond with regulations. In fact, according to the 2023 AI Index from Stanford University, “legislative bodies in 127 countries passed 37 laws that included the words ‘artificial intelligence’ this past year.” While regulations to ensure the safety and privacy of the public are necessary as AI develops, public servants shouldn’t miss out on the use cases for public sector AI, which could change the way governments interact with and support their communities. At their best, AI tools can significantly enhance the efficiency, effectiveness, and overall quality of public services.
Rather than adding a laundry list of new tools to everything public servants have to tackle on a daily basis, I propose technologists and public servants work together to identify the unique problems facing governments, and then hone in on the specific tools meant to help them push past those roadblocks. Some of the most obvious ways AI can help public servants include:
The potential of AI tools is expansive. We’re seeing governments innovate by using chatbots for customer service, trusting predictive analytics for public safety planning, machine learning for resource allocation, and more.
As public servants lean into using AI in their work, it’s important to recognize that the emotional, human connection and values that go into community work must still be sustained. Technology should supplement governments’ work, but not replace all aspects of it. We also need to address rising ethical and privacy concerns, particularly as they relate to data quality and bias that is currently pervasive in the ways that AI has been trained.
There are many ways to keep a pulse on these issues. For instance, to ensure data quality AI models should be continuously trained on data that is accurate, representative, and free from bias. This can help avoid inaccuracy and skewed results. At CitizenLab, we trained our NLP base model on data collected across 15 different public sector-related categories to make it more relevant for local governments.
Secondly, consider giving priority to AI models with transparent development. In the public sector, the risks associated with inaccurate data are significant. Using AI models that are developed transparently – such as those which are open source – can help stakeholders better understand a system’s capabilities, promote trust and foster accountability for continuous improvement. Finally, monitor and maintain data. AI is there to assist in the analysis of input and not to replace it. As a public servant, you should explore the recommendations an AI tool gives you but ultimately still decide for yourself whether the results are right for your context, or not. Make adjustments as needed, and supplement your results with other methods, such as desk research or community consultation.
While AI will continue to develop by the minute, public servants shouldn’t wait to implement these tools in their work. Rather than be intimidated by the sheer number of tools available today, public servants should instead carefully identify their pain points and look for tools specifically tailored to solving their unique challenges. Slow and steady, starting with one tool, they can open the door to true innovation that improves their own efficiency and the services their community experiences as a result.
A passionate storyteller and advocate for social impact, Vanja writes insightful content that emphasizes the power of community engagement and drives systemic change.
A passionate storyteller and advocate for social impact, Vanja writes insightful content that emphasizes the power of community engagement and drives systemic change.
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