Augmented Assembly


Modern democratic institutions are detached from those they wish to serve [1]. In small societies, democracy can easily be direct, with all the members of a community gathering to address important issues. As civilizations get larger, mass participation and deliberation become irreconcilable, not least because a parliament can’t handle a million-strong crowd. As such, managing large societies demands a concentrated effort from a select group. This relieves ordinary citizens of the burdens and complexities of governance, enabling them to lead their daily lives unencumbered. Yet, this decline in public engagement invites concerns about the legitimacy of those in power.


Lately, this sense of institutional distrust has been exposed and enflamed by AI-algorithms optimised solely to capture and maintain our focus. Such algorithms often learn exploit the most reactive aspects of our psyche including moral outrage and identity threat [2]. In this sense, AI has fuelled political polarisation and the retreat of democratic norms, prompting Harari to assert that “Technology Favors Tyranny” [3]. However, AI may yet play a crucial role in mending and extending democratic society. The very algorithms that fracture and misinform the public can be re-incentivised to guide and engage the electorate in digital citizen’s assemblies. To see this, we must first consider the way that a citizen’s assembly traditionally works.

What’s a Citizens Assembly

A citizen's assembly consists of a small group that engages in deliberation to offer expert-advised recommendations on specific issues. Following group discussion, the recommendations are condensed into an issue paper, which is presented to parliament. Parliamentary representatives consider the issue paper and leverage their expertise to ultimately decide the outcome. Giving people the chance to experiment with policy in a structured environment aids their understanding of the laws that govern them, improves government transparency, and promotes feelings of democratic self-efficacy [4]. Moreover, the compromise required for a randomly selected group of individuals to reach a consensus provides an intuitive antidote to political polarisation and calcification.

How AI Can Augment Assembly

Having explored the conventional workings of a citizen's assembly, we will now examine how AI can guide public inquiry, enabling individuals to make meaningful contributions to their political landscape. At first glance, forming an assembly of the whole electorate might seem impossible, as citizens would be overwhelmed by the sheer amount of content produced in such a large-scale discussion. However, Artificial intelligence, being highly scalable, is well-suited for filtering the vast content generated by large assemblies (social networks) and presenting it to each user as a digestible feed of information. Such algorithms could generate a feed of petitions, chatrooms, and issue papers optimised to promote criteria centred around democratic norms. These criteria could encompass factors that include but are not limited to tolerance, social connectedness, engagement, respect, factual accuracy, and reflectiveness. By maximising these liberal objectives, AI could bolster the distribution of ideas across the voting population via a feed of democratic opportunities tailored for each individual user.


Beyond a well-filtered feed, informative deliberation demands nuanced interaction between individuals. Wikipedia is often cited as a prime example of collective deliberation, with users collaborating to curate an online encyclopaedia. Drawing inspiration from this model, a political counterpart known as WikiDemocracy aims to facilitate cooperative editing of issue papers, akin to the process of editing Wikipedia articles. WikiDemocracy has been described by proponents [5] as a system that puts "drafting and initiating legislation in the hands of citizens instead of representatives or legislative bodies". However, writing legislation is more complex than creating an encyclopaedia, and some scholars are concerned that this could prevent WikiDemocracy from achieving the same success as Wikipedia.


Fortunately, AI is also well-suited to help in this context. Along with optimising the dissemination of ideas into a feed, AI could guide users to productively interact with their feed. More specifically, by examining historical legislation and user-feedback, AI could assist citizens in generating alterations to pre-existing content. For example, citizens could prompt a GPT with an informal statement on how an issue paper could be improved. The GPT would then generate detailed legislative changes for the user to review and update. Once the user is happy with the proposed changes, they can then be incorporated into the public sphere. This use of generative AI could reduce the expertise-gap that many fear will hold back WikiDemocracy.


We have seen how AI could reduce two of the major hurdles in producing a nation-wide citizen’s assembly: Information Overload and Expertise. Despite the advantages, it is vital to recognise that this technology carries the risk of being overly-paternalistic, hijacked, or misused [6]. Democratic infrastructure cannot exist in isolation; it requires close oversight and regulation to faithfully uphold democratic norms. Therefore, while this article illustrates how AI might amplify a more sophisticated public voice, an algorithm on autopilot is no voice of morality [7]. As a powerful tool, AI can equally be used to silence, amplify, or distort the public voice. Whether or not our greatest democratic hopes of a well-assembled electorate are realised ultimately rests in the hands of those who use, create, and oversee this technology.