Synthetic Citizens Are Coming to Democracy

Democracy runs on a counting problem. We tally votes, signatures, comments, likes, shares, donations and polls — signals that are imperfect but anchored to one assumption: behind every act of political expression sits a person.

Generative AI did not invent fake participation. It transformed its economics. Cost collapses, scale explodes, and the output stops looking like spam and starts looking like ordinary civic speech. The next wave of AI in politics will not stop at deepfakes and automated campaign emails. It will produce synthetic citizens: AI-generated personas that speak, argue, endorse, oppose and simulate the behavior of real communities.

The democratic question is not whether AI can help citizens express themselves. It can. The question is whether democracies can preserve the line between AI-assisted human participation and AI-substituted public will. Call it democratic provenance: synthetic voices may inform political judgment, but they must not be counted as public will.

Machines can help us listen. They cannot be counted as the people.

This argument continues a thread I have been pulling on in AI and European Democracy on the EU's regulatory architecture, in AI and the Information War on synthetic media, and in Spain Is Not a Translation Problem on the legitimate — and the dangerous — uses of synthetic populations.

From Macros to Adaptive Personas

The warning signs are not new. In 2021, the New York Attorney General reported that nearly 18 million of the more than 22 million comments submitted to the FCC's 2017 net neutrality proceeding were fake. A broadband-industry-funded campaign generated more than 8.5 million fake comments impersonating real people. The same investigation identified more than 9 million pro-net-neutrality comments submitted under fabricated identities through automated software.

What 2017 accomplished with macros and stolen identities, 2025 began doing with adaptive personas that vanish into ordinary debate.

Between November 2024 and March 2025, University of Zurich researchers covertly deployed AI-generated comments in r/ChangeMyView, a forum where users award “deltas” when an argument changes their view. According to moderator disclosures and subsequent reporting, the accounts used fabricated identities and tailored arguments to users' posting histories. They posted 1,783 comments and earned more than 10,000 karma before being exposed. Reddit's chief legal officer called the study “deeply wrong on both a moral and legal level,” and Retraction Watch reported that the university's ethics committee issued a formal warning to the principal investigator. The technique evaded detection for the entire duration of the study.

Researchers and campaign-adjacent practitioners are already industrializing political simulation. Out of One, Many proposed “silicon samples” built from sociodemographic backstories drawn from real survey participants. Ipsos used AI personas ahead of the 2024 UK general election in a project called Marginal Moments, simulating undecided voters in marginal English constituencies. Fake Plastic Voters (Novelli, Argota Sánchez-Vaquerizo, Cyr, Formisano, McDougall, Sandri & Floridi, SSRN, April 2026) argues that AI focus groups are faster and cheaper than human ones but cannot replace human interaction where political meanings and identities actually emerge. Other work, including a 2025 German vote-choice study by von der Heyde and colleagues, exposes the limits: models reproduce partisan bias and miss complex individual behavior, especially outside English-language contexts.

Synthetic Research, or Synthetic Democracy?

Synthetic personas have legitimate research uses. A campaign may test whether a speech is comprehensible. A ministry may stress-test whether public information is accessible. A civil society group may flag confusing language before consulting real people.

But the line between research tool and substitute for representation is thin and easy to cross: the campaign that quietly lets simulated voters drive message selection; the ministry that uses synthetic panels to choose policy framings; the public affairs team that A/B-tests amendments against generated constituents. Most of the policy fight will play out there, in dashboards and slide decks where no one admits that simulation has slipped into representation.

One counter-argument deserves a serious answer. Real consultation, the argument runs, is already dominated by well-resourced voices, so synthetic publics could surface the unheard. The remedy for unequal human representation is better human representation, not synthetic substitution. A synthetic respondent purporting to speak for Roma communities, undocumented migrants or diaspora voters is an approximation built from data that may contain very little of those communities to begin with.

The persuasion evidence sharpens the concern, with caveats. In a controlled online debate experiment with 900 participants, GPT-4 equipped with basic sociodemographic information was more persuasive than human opponents 64.4% of the time among debate pairs where AI and humans were not equally persuasive. A separate study found that LLM-generated policy messages produced small but statistically significant attitude shifts — about two to four points on 101-point policy-support scales — and matched the effectiveness of messages written by lay humans.

The danger is not magic persuasion. It is cheap, scalable, personalized influence.

What Europe's Rules Already Cover — and What They Don't

Europe's regulatory architecture covers part of the problem. Article 50 of the EU AI Act requires AI interaction notices, machine-readable marking for generative AI systems, and disclosure for certain deepfakes and AI-generated public-interest publications. Those rules apply from 2 August 2026. Under the Digital Omnibus on AI provisional agreement reached at trilogue on 7 May 2026, providers with generative systems already on the EU market before that date get a transition until 2 December 2026 to bring their machine-readable marking into compliance.

Spain is moving on national implementation in parallel: a March 2025 preliminary draft law classifies failures to correctly label AI-generated images, audio or video constituting deepfakes as serious infringements, with sanctions ranging from €500,000 to €7.5 million or 1% to 2% of global turnover.

These are important steps. But labeling content is not the same as protecting representation. A deepfake label tells people a video was generated. It does not tell them whether a consultation was flooded by synthetic submissions. A chatbot disclosure tells a user they are interacting with AI. It does not tell them whether AI agents are shaping the perceived majority view in their community.

Five Recommendations for Democratic Provenance

A rule of democratic provenance translates into five practical moves.

Closing Thoughts

AI can help people understand complex rules, translate bureaucratic language and draft comments that citizens then review and endorse. It can help institutions process large volumes of public input. AI should widen the channel between people and power — not replace what flows through it with synthetic substitutes.

Democracy can survive disagreement, manipulation attempts and waves of low-quality speech. It cannot survive losing the ability to tell whether the public is speaking at all.