Exploring the Role and Limitations of Generative AI in Policymaking: Initial Findings and Next Steps

Abstract

Governments across the world are exploring how generative AI might improve their ability to effectively pass legislation and deliver public services. This includes the UK government, which has stated that utilising AI to reduce costs and improve public services is a national priority. Current efforts to deploy AI tools are focused on streamlining internal bureaucracy, such as writing ministerial briefings or analysing public consultations, and supporting the direct delivery of public services. In contrast, there has been limited exploration of the role generative AI could play in directly improving policy development and implementation, in support of a government’s high-level agenda or manifesto commitments. This project seeks to add practical context to hypothetical considerations of how generative AI might impact policymaking. Published articles and commentary provide insights and recommendations, with varying degrees of specificity, on the opportunities, risks, and limitations of using AI to support national policymaking. However, there does not appear to be any instances of meaningful ‘direct experimentation’ with currently available generative AI tools, to explore the potential role of generative AI in more detail, identifying context-specific areas of strength, weakness, or risk. This project is designed as an initial first step towards addressing this research gap. Using our direct experience of government policymaking, we begin to assess the performance and relative ‘value-add’ of using publicly available generative AI tools to support typical policymaking functions. This is conducted through ‘policy simulations’ in which we assessed how useful (or problematic) AI tools were in helping us to perform a range of policymaking tasks. Our simulated ‘performance assessments’ were conducted in two phases. Firstly, we undertook basic assessments of the utility of publicly available generative AI across typical policymaking tasks for three different high-level policy contexts. Secondly, we conducted a more granular and in-depth assessment in an area where we have domain expertise (Regional Growth and Development), to explore the impact of output verification (by experts) and corpus design on the utility of generative AI in a national policymaking context. From this research, we have identified an indicative set of policymaking functions for which generative AI appears to offer significant value (provided this deployment is specific, deliberate, and targeted). We also found that the use of generative AI does not replace the need for in-depth policy expertise but rather increases the utilisation of this expertise by a wider range of government stakeholders, and significant risks may be introduced in the absence of meaningful human engagement with AI outputs. Significant limitations of using generative AI to support policymaking also emerged from this preliminary research, alongside the well-documented broader risks associated with generative AI (such as unclear provenance, biased outputs, hallucinations, and broader security risks). The full set of our indicative findings are below, which we use to inform a proposed set of basic recommendations for governments seeking to better utilise generative AI to support national policymaking. Most importantly, we judge that the findings from this preliminary exploratory research were sufficiently informative to indicate that there would be meaningful benefit in pursuing further ‘direct experimentation’ with generative AI tools in a policymaking context. We propose that this would be an effective way to develop more detailed understanding and subsequently inform government guidance and adoption initiatives (including for professional training purposes). We conclude this report with a set of proposals for how this further research could be conducted, including how to address the limitations of our initial exploratory research project. This research is likely to deliver the greatest benefit if conducted in close collaboration with government stakeholders, incorporating direct domain expertise and adjusting for real-world policymaking requirements and constraints. This includes wider considerations on ensuring AI tools are used in accordance with democratic principles and core national values, including retaining sovereignty over internal government data and maintaining sufficient control over AI enabled decision-making. Further, as set out in the Cabinet Secretary’s Future Civil Service initiative, achieving the widespread utilisation and adoption of AI across the UK government will require a whole-of-institution approach, incorporating changes to workforce design and development, novel procurement models, risk management and governance mechanisms, and fundamentally redesigning government processes. Future research on the role of generative AI in policymaking should account for these broader institutional considerations and align closely with existing government initiatives.