Utopian governance using technology, inc generative AI
Electrohabermas, digital deliberation, platform democracy
2025-10-27 — 2026-04-16
Wherein AI tools for collective governance are surveyed, particular attention being given to the Habermas Machine, an AI mediator found to exceed human facilitators in building group consensus.
The companion notebook on utopian governance asks what systems should we have? — sortition, futarchy, liquid democracy, and other institutional designs. This notebook asks a different question: what platforms and tools might help us actually govern better? In particular, what’s the best, kindest, and wisest collective behaviour we could achieve if generative AI and digital platforms helped mediate governance?
The counterpart to AI disempowerment of humans is AI empowerment of collective decision-making. This isn’t the same as wondering how we might democratize AI — that’s the inverse question and also interesting.
1 AI-mediated deliberation
Can AI help divided groups find common ground? Early experiments suggest yes — and perhaps better than human facilitators.
1.1 The Habermas Machine
Ekeoma Uzogara’s summary of Tessler et al. (2024):
To act collectively, groups must reach agreement; however, this can be challenging when discussants present very different but valid opinions. Tessler et al. (2024) investigated whether artificial intelligence (AI) can help groups reach a consensus during democratic debate (see Nyhan and Titiunik (2024)). The authors trained a large language model called the Habermas Machine to serve as an AI mediator that helped small UK groups find common ground while discussing divisive political issues such as Brexit, immigration, the minimum wage, climate change, and universal childcare. Compared with human mediators, AI mediators produced more palatable statements that generated wide agreement and left groups less divided. The AI’s statements were more clear, logical, and informative without alienating minority perspectives. This work carries policy implications for AI’s potential to unify deeply divided groups.
See also: (Hernández 2025; Volpe 2025). For how this kind of deliberation works at smaller scales without AI, see community governance.
1.2 Team Mirai
The Habermas Machine is a lab experiment. Team Mirai is what it looks like when you ship the idea.
Imagine an election where every voter has the opportunity to opine directly to politicians on precisely the issues they care about. They’re not expected to spend hours becoming policy experts. Instead, an AI Interviewer walks them through the subject, answering their questions, interrogating their experience, even challenging their thinking.
Voters get immediate feedback on how their individual point of view matches—or doesn’t—a party’s platform, and they can see whether and how the party adopts their feedback. This isn’t like an opinion poll that politicians use for calculating short-term electoral tactics. It’s a deliberative reasoning process that scales, engaging voters in defining policy and helping candidates to listen deeply to their constituents.
This is happening today in Japan. Constituents have spent about eight thousand hours engaging with Mirai’s AI Interviewer since 2025. The party’s gamified volunteer mobilization app, Action Board, captured about 100,000 organizer actions per day in the runup to last week’s election.
It’s how Team Mirai, which translates to ’The Future Party,’ does politics.
TODO: compare and contrast with the Habermas Machine—both use AI as mediator but at very different scales and with different mechanisms. The Habermas Machine finds consensus statements; Mirai structures individual deliberative interviews at scale.
1.3 Bridging-based ranking
A family of aggregation mechanisms with a shared move: use the signal in who agrees with whom to find content, statements, or policy positions that cross ideological divides. The two deployed examples worth knowing are X’s Community Notes and the Polis / vTaiwan stack.
Community Notes. Community Notes (formerly Birdwatch) surfaces fact-checks on X posts only when the system infers that a note is rated helpful by raters who usually disagree. The published algorithm is a matrix-factorization model:
\[ r_{un} \;=\; \mu \;+\; i_n \;+\; b_u \;+\; f_u^\top f_n \;+\; \varepsilon_{un}. \]
Here \(r_{un}\) is user \(u\)’s rating of note \(n\) (helpful / not helpful, coded numerically), \(\mu\) is a global intercept, \(b_u\) is a per-user “how positive is this rater” bias, \(i_n\) is the note intercept, and \(f_u^\top f_n\) is the dot product of a low-dimensional user factor and a note factor. Parameters are fit by regularized least squares over the observed ratings.
The factors \(f_u\) and \(f_n\) end up absorbing the main axis of polarity—roughly, partisanship. Their dot product predicts the disagreement pattern: how user polarity aligns with note polarity. What’s left in \(i_n\) is the helpfulness signal with the polarity component divided out. A note with high \(i_n\) is one that raters across the polarity axis converge on calling helpful. That is the bridging score, and a note is shown publicly only when \(i_n\) clears a threshold.
A few things the math is quietly saying:
- It’s a rank-1 factorization in the deployed version—one dominant axis of disagreement is assumed. If the real disagreement graph has three orthogonal factions, we are regressing out one axis and projecting the others into the intercept. FWIW that might still be an improvement over unweighted averaging, but it is not bridging in the strong sense.
- \(i_n\) is not identifiable without regularization; the regularizer on \(f_u\) and \(f_n\) is doing real work, and its choice affects what counts as “bridging” out in the tail.
- Adversarial robustness emerges because a manipulator has to coordinate raters with divergent \(f_u\) to move \(i_n\), which is costlier than coordinating raters inside one faction.
Polis and the Taiwan experience. Polis solves a related problem with a different pipeline, aimed at structured deliberation rather than ranking fact-checks. Participants submit short statements; everyone votes agree / disagree / pass on the statements of others. The agree/disagree matrix is factored by PCA, giving each participant a 2-D position on an “opinion map”, and \(k\)-means clusters participants into opinion groups (typically two to four). Per-statement consensus is computed across clusters: a group-informed consensus statement is one that substantially every cluster, weighted by cluster size, agrees with.
The Taiwan g0v community and the subsequent vTaiwan process, under Audrey Tang, ran Polis at policy scale—producing consensus recommendations on Uber regulation, fintech licensing, online alcohol sales, and more. Tang’s broader framing, “Plurality,” treats this family of tools as infrastructure for collective intelligence across diversity.
Polis differs from Community Notes along axes worth keeping in mind:
| Community Notes | Polis | |
|---|---|---|
| Output | per-item score (rank) | per-statement consensus + opinion map |
| Time | continuous, per note | session-based |
| Adversarial pressure | high | lower (smaller audience) |
| Downstream use | automatic display | human facilitators curate |
| Rank | 1 polarity axis | 2 PCA components |
The broader family. Aviv Ovadya and Luke Thorburn’s “bridging systems” writeups generalize the design pattern and flag its characteristic failure modes: what if there is no bridge? what if the apparent “bridge” is a false consensus because we have only modelled one axis of disagreement? Related experimental infrastructure lives at the Meaning Alignment Institute and the AI & Democracy Foundation.
For the aggregation problem this sits next to, see social choice (classical preference aggregation), reputation systems (iterative weighting), and epistemic communities on the “whose judgement carries weight” question. TODO: pull Wojcik et al. on Community Notes, plus the Ovadya & Thorburn writeups, into the bibliography.
1.4 Other AI governance tools
- AI Tools for Trust: Community Notes, Rhetoric Detection & More
- AI & Democracy Foundation
- Meaning Alignment Institute / Substack — aligning AI and institutions with what really matters.
- Will AI break democracy or fix it? Yes. (reviews Schneier and Sanders (2025))
2 Participatory civic platforms
Most “participation” in existing democracies is consultation: comment boxes, public submissions, surveys, town halls. The institution asks for input, then decides what to do with it. This is better than nothing, but it doesn’t change the governance structure—the same people make the same decisions, just with more information (which they may or may not use).
The tools below aim at something stronger: participation-as-governance, where the mechanism design of the platform itself determines how input translates into outcomes. Participatory budgeting with binding commitments, consent-based policy revision, structured deliberation with decision rules — these aren’t just input channels, they’re alternative governance architectures. The distinction matters because the failure mode of consultation is captured input (powerful voices dominate the comment box), while the failure mode of governance-by-platform is mechanism failure (the rules produce perverse outcomes). Different failure modes require different defences.
Not all of this is AI-dependent — much of it is about building better infrastructure for human participation.
- Civic Tech Field Guide — a comprehensive directory.
- Participedia — a global crowdsourcing platform for researchers, educators, practitioners, policymakers, activists, and anyone interested in public participation and democratic innovations. Theory, methods, and case studies.
- Democratic Input and Artificial Intelligence
- Plurality: The Future of Collaborative Technology and Democracy
- Plural — Swiss platform for participatory democracy.
- metagov/interop: Supporting interoperable deliberative tools
- Results — Market research of existing civic technologies for participation — gov.scot
2.1 Metagov
Metagov hosts a stable of interesting projects for online community governance. Joshua Tan is head of research; I’m keen to see what the organisation does next. See also Metagov News (Special AI Issue) - Nov 2025.
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Knowledge Organisation Infrastructure (KOI) is an open protocol that allows communities to collaboratively manage knowledge on their own terms while remaining interoperable with others. Developed by BlockScience with contributions from Metagov and the Australian Research Council Centre of Excellence for Automated Decision-Making and Society (ADM+S), KOI is designed for contexts where knowledge needs to be contextual, traceable, and machine-readable without forcing everyone into the same database or governance model.
KOI allows different groups to organise, reference, and share knowledge in a modular, consent-based way. It enables interoperability without centralisation, creating a shared architecture for collective intelligence while preserving local control.
PolicyKit: This is software for online communities to govern themselves. It lets communities create and enforce their own rules and decision-making processes.
Govbase: An open-source, crowd-sourced database of online governance projects, tools, organizations, and concepts.
Collective Voice: A project to integrate Metagov with Open Collective, exploring how collective governance can work with the financial practices of online communities.
Interop1: An initiative that aims to create a more interoperable ecosystem for online deliberation and funds open-source tools for deliberation and digital governance.
[…]
3 Blockchain and decentralized governance
Zuzalu introduced me to Zuzalu.city and, in turn, to Make Ethereum Cypherpunk Again:
Many of these values are shared not just by many in the Ethereum community, but also by other blockchain communities, and even non-blockchain decentralization communities, though each community has its own unique combination of these values and how much each one is emphasized.
- Open global participation: anyone in the world should be able to participate as a user, observer or developer, on a maximally equal footing. Participation should be permissionless.
- Decentralization: minimize the dependence of an application on any one single actor. In particular, an application should continue working even if its core developers disappear forever.
- Censorship resistance: centralized actors should not have the power to interfere with any given user’s or application’s ability to operate. Concerns around bad actors should be addressed at higher layers of the stack.
- Auditability: anyone should be able to validate an application’s logic and its ongoing operation (eg. by running a full node) to make sure that it is operating according to the rules that its developers claim it is.
- Credible neutrality: base-layer infrastructure should be neutral, and in such a way that anyone can see that it is neutral even if they do not already trust the developers.
- Building tools, not empires. Empires try to capture and trap the user inside a walled garden; tools do their task but otherwise interoperate with a wider open ecosystem.
- Cooperative mindset: even while competing, projects within the ecosystem cooperate on shared software libraries, research, security, community building and other areas that are commonly valuable to them. Projects try to be positive-sum, both with each other and with the wider world.
4 Adjacent concerns
These related notebooks explore specific facets of the technology–governance intersection:
- Platform democracy and kinder social media — redesigning online public spaces.
- Delegated agent economies — what happens when AI agents act on our behalf in markets and governance.
- Political economy of cognition — the foundational theory of decision-making in the age of AI.
- AI disempowerment — the dark mirror of this notebook.
- Democratizing AI — not AI for governance, but governance for AI.
5 As an epistemic problem
Governance is, at bottom, an epistemic problem: how does a collective discover which policies will actually produce good outcomes, given that no individual knows enough? (For the broader context of how communities form and maintain shared knowledge, see epistemic communities.)
Social choice theory frames this as preference aggregation—how to combine what people want. But much of governance isn’t about preferences at all; it’s about beliefs. People don’t disagree about climate policy because they want different temperatures. They disagree because they hold different models of how the economy, the atmosphere, and political institutions interact. The preference-aggregation framing (voting, polls, referenda) is the wrong tool for the belief-aggregation job.
Several mechanisms in the utopian governance notebook attack this directly: prediction markets aggregate beliefs by rewarding accuracy—but they tell you what people think will happen, not what would happen if you intervened (the causal validity problem). Futarchy tries to bridge the gap by conditioning markets on policy choices, but inherits the causal difficulties. Reputation systems are a softer version of the same idea: weight opinions by track record rather than by majority.
AI-mediated deliberation (the Habermas Machine, Team Mirai, and similar tools discussed above) offers a different angle. Rather than aggregating existing beliefs, it generates new statements that bridge between positions—finding common ground that individuals didn’t articulate. This is closer to what Habermas meant by the ideal speech situation: not a vote, but a process that produces justified consensus through structured dialogue. The question is whether AI mediation actually gets us closer to truth, or just closer to statements that feel agreeable. See also community governance for how deliberation works at smaller scales without AI.
These approaches aren’t mutually exclusive—markets for factual questions, deliberation for value-laden ones, reputation for weighting expertise—and the interesting design question is how to combine them. This topic probably deserves its own notebook when the literature matures; for now, this section stakes out the territory.
TODO: connect to Tetlock’s superforecasting literature, epistemic institutions more broadly, and the question of whether AI systems can serve as epistemic infrastructure rather than just deliberative infrastructure (i.e. not just mediating discussion but actively modelling policy consequences).
6 Incoming
Aviv Ovadya—primary focus on ensuring AI governance can keep up with AI advances, building on deliberative democracy.
Aviv’s primary focus is on ensuring that the governance of AI can keep up with the rate of AI advances, building on lessons from applied deliberative democracy to enable effective transnational governance and alignment. This involves framing (e.g., “Platform Democracy”), theory (e.g., “Generative CI”), and applied work: accelerating efforts to build out and pilot the organizational and technical infrastructure for deliberative governance (formally or informally advising efforts at Meta, Twitter, and OpenAI).
Reimagining Democracy for AI (in the “Journal of Democracy”)
Saiph Savage, Director Northeastern Civic A.I. Lab—tools for gig economy workers and sousveillance.
Inside Audrey Tang’s Plan to Align Technology with Democracy | TIME
Exploring a new social product for local communities
Our vision is a world where being the “steward” of the local online space is an important job in every community—not only paid and well-supported, but as celebrated as local librarians and teachers. We’re well past the days where just because something’s online doesn’t mean it’s not real or important.
Government as Software—the claim: government is nothing more than an information-processing machine.
wh-ai | Foundation—a local startup pitching big:
wh-ai [/ˈwaɪ/] is building superintelligent knowledge. Our platform implements a peer-to-peer dialectic process on a decentralized trustless network where questions are answered and verified by the fittest and most advanced AI agents, no need to have multiple AI agents on retainer, nor trust them, instantly guaranteeing highest quality and alignment at lowest cost. We enable a future where we can thrive in novel and abundant synthetic data, expertly verified, with primitives that ground the next discovery on a immutable bedrock.
Apenwarr — “Everyone seems to have an increasingly horrifically misguided idea of how distributed systems work.”
Jeremy Yuille, Cognitive Infra: The Real Work Surfaces — isomorphous.design
AI and Collective Governance: Points of Intervention - Google Docs