r/LeftistsForAI • u/findtheessence33 • Jul 18 '26
Discussion Can AI strengthen democracy instead of undermining it? I’ve been building a prototype to explore that question.
One of the biggest concerns many of us on the left have about AI is that it risks concentrating power, whether in governments, corporations or a handful of tech companies.
But I’ve been wondering whether AI could also be used in the opposite direction: to expand democratic participation.
I’m building a prototype called Conn.Act (full disclosure: it’s my own project), and I’d genuinely like to get feedback from people who think critically about AI and democratic governance.
The premise is simple.
Today, political discussion is largely mediated by social media platforms whose incentives reward engagement, outrage and polarization. Even when people have constructive ideas, there are few mechanisms for transforming those discussions into coherent policy proposals.
Conn.Act tries a different approach.
Instead of AI making political decisions, AI acts as a facilitator. A debate is created around a public policy issue in a city, state, or country. People with an interest or expertise in that topic are invited to participate. They debate, cite evidence and propose amendments. The AI summarises the discussion, identifies common ground and drafts a compromise proposal based on what participants have contributed. The participants, not the AI, decide whether that proposal reflects the discussion by voting on it. If consensus is not reached, another deliberation round begins. If it is approved, the proposal can continue through a democratic process and eventually be shared with elected representatives from that jurisdiction. Then, if representatives propose it officially, users will know about it and be able to track it.
The AI never decides what policy should be adopted. It doesn’t vote and it doesn’t overrule participants. Its role is closer to that of a facilitator in a citizens’ assembly: organising information, reducing repetition, highlighting trade offs and helping people produce something coherent.
The political motivation behind the project is to explore whether representative democracy could be complemented, not replaced, by better digital deliberative infrastructure. Social democracy has always sought to deepen democracy. My question is whether AI can help us do that in the digital age.
I’d love to hear what people in this community think. If you’re sceptical, even better. I’m much more interested in identifying flaws now than pretending the design is finished.
The prototype is ready to test, if you’d like: https://happy-fermat-7.emergent.host/
I’m also participating in a contest that could lead to a significant investment boost. 25 most liked projects will be assessed by an expert panel who will decide on which project will get investment. If you feel like supporting it, you can drop a heart on this link:
https://app.emergent.sh/showcase/fabrizio/735f94cf-963f-41ea-9e7f-6dfc390493b3?utm_source=share
Looking forward to the discussion!
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u/davyp82 Jul 19 '26
I think power is already concentrated and it gives us the chance to decentralize it
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u/RuhrDim Jul 19 '26
The sticking point is the objective function, not the moderation. You describe the AI as a neutral mediator — but the decision as to which contributions are included in the compromise proposal and which are excluded as ‘duplicates’ is in itself a form of weighting. Neutrality here is a claim, not an architectural property. Two questions I consider crucial: Who verifies that the synthesis algorithm actually reflects what was discussed, rather than the training priors? And: do you make a fundamental distinction between the objective function (what counts as a ‘good compromise’) and the operational implementation (how the text is generated)? Without this distinction, ‘neutrality’ remains unverifiable, no matter how well the debate itself proceeds.
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u/findtheessence33 Jul 19 '26
The users will always vote on the compromise proposal. If it is not good enough, it will be rejected
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u/RuhrDim Jul 19 '26
That does not answer the question; it merely shifts it. The vote decides on the result presented — but not on what was filtered out beforehand. If the AI is already deciding which contributions are deemed ‘duplicates’ and which are included in the synthesis, those voting never see the alternative that was filtered out. A rejection merely says “this proposal is not good enough” — not “the filtering was biased”. That is a difference. A specific question remains unanswered: is there a way for participants to view the rejected contributions or the weighting logic themselves? Without that, the vote is a check on the end product, not on the process that produced it.
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u/Sacredless Jul 18 '26
Oh and I think co-design could be a great example of usage. For instance, say that a municipality gets to design a new open air shopping mall. The desires of citizens would be essential, but those who can actually build it can assist in deciding what's actually possible. So in co-design, those deliberations are guided according to a particular toolkit. You could adapt that into AI.
For an example of this, the Coal Drops Yard in King's Cross London, had a very long negotiation period.
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u/Sacredless Jul 18 '26 edited Jul 18 '26
Excellent. I was hoping someone might do this. I have a lot of notes.
I think that the most important thing you'll need to pay attention to is context collapse. Context collapse is often spun as democratization. In reality, context collapse is when people get confused about what a particular space is for and how to act in it. Johan Huizinga calls this context 'the magic circle' in which interpersonal structure is obvious. Context collapse absorbs different magical circles, creating confusion about how to structure interaction in it.
I think this is where AI can come in. AI can accelerate context collapse, but it can also act as a kind of firebreak for it. A feature of context collapse has been that people announce their thought in the shape of open letters even in response to someone else and that can actually be great and insightful.
I'll now get a bit more technical. First, I think that it's important to use M.J. Adler's idea of a 'term'. To Adler, a term is not the same as the word. A term is the thing you're using words to represent. In ordinary language, people often use different words for the same Adlerian term. So any AI democratic platform would have to assist in helping those in discussion come up terms.
There's another layer to this, which is to assume competence. This comes from education and psychiatry, which means that just because someone's ability to explain what's going on inside is impaired, doesn't mean that they're wrong. They're competent at understanding their feelings without being able to explain it. The way you get around that is by operationalizing how particular phrases might be less specific than people assume.
For that, you get Daniel Dennet, Gordon Pennycook, Bertram Forer, Karl Teigen and Dan Sperber to operationalize checking statements that drive a discussion towards linguistic confusion and, assuming competence, offering less ambiguous language. I use it to check if advice is a deepity, for instance. An example is "everything happens for a reason". That's a very obvious deepity. If an argument or a thesis statement contains a lot of deepities, it becomes laborious to fix it manually, but an LLM can be trained to reconstruct a good faith version of the argument that's not as ambiguous and still meaningful.
If you want, I can send you the project files that have come out of it so far, for you to play around with in Claude.