Here Come the Synthetic Voters

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The midterm election cycle is heating up in the United States, and there are important elections set to take place soon in Brazil, Israel, Sweden, and beyond. AI will be a factor in these elections, offering a new set of tools and techniques for campaigns to try to reach voters.

Laura Karpas spoke to one researcher who has considered the question of when and how such simulated evidence can be used in campaign research, and another who wonders what the implications may be for the complex interaction between political leaders and public opinion.

What follows is a lightly edited transcript of the podcast episode.

Laura Karpas:

From fireside chats to TV ads to Facebook posts that know your zip code, your age, your anxieties, for 100 years, political campaigns have chased one thing: a way inside voters' heads. Now, what if they could get in there without ever talking to a single voter? That's the promise of AI simulated focus groups where hundreds, maybe thousands of synthetic voters can be built with different ages, ethnicities, regional identities, sexualities, religions, all to provide campaigns a chance to put them under a microscope. But will these synthetic personas provide campaigns a clear picture of real voters or simply an illusion?

Claudio Novelli:

Is simulating focus groups through AI really a microscope? Is it a way to enhance research? This was the question.

Laura Karpas:

That's Claudio Novelli. He's a researcher at Yale's Digital Ethics Lab. He and his team went out to answer this question, and the research was recently published in the 2025 pre-print paper, “Fake Plastic Voters: When Can Political Parties Use AI Simulated Focus Groups?”

Claudio Novelli:

And very soon we realized that the answer was not a binary. The answer was not that simple.

Laura Karpas:

So Novelli's team built something like a map, a decision matrix, essentially rules for when a campaign could use a synthetic focus group and when it really, really shouldn't.

Claudio Novelli:

We know the literature about how risky it is for a political party to over-rely in general on focus groups, building a strategy on a false assumption, on a false background belief. Imagine how challenging and how problematic it could be to base your entire strategy on a synthetic one.

Laura Karpas:

So rule one is ask what the focus group is actually for. Is it to determine how people feel about a certain topic, how they make sense of their political reality? According to these guidelines, AI simulated focus groups should not be used for this purpose. It's too risky.

Claudio Novelli:

In the past, we kind of ruled out the idea of not just using the technology at all, but I think it's becoming clearer and clearer that sometimes you just have to unplug.

Laura Karpas:

And that's because, according to Novelli, these synthetic voters fail to capture the real magic of a focus group, the body language, the silence, the embarrassment, all of the meaning making that happens in real time. But there's a second use for a focus group, not understanding what voters think, but deciding what a political party should do. Testing a message, testing a stance. And so here's where the guidelines open the door, but only if a campaign can answer two questions. One, how risky is this decision? Is it something small, reversible, kicked around in an internal meeting, or is it something that you cannot take back?

Claudio Novelli:

If you are using synthetic focus groups just for internal brainstorming, there's a difference between these, let's say, zero risk scenario and a scenario where the use of a focus group has strong consequences. So for instance, a high-risk scenario is when a political party is using a synthetic focus group to decide whether or not to enter into an alliance. That's very risky and it should not be based on synthetic simulated focus group.

Laura Karpas:

So risk, that's step one. Question two is maybe more complicated. How close is this simulated voter compared to a real one? Picture a spectrum. On one end, the flimsiest option. Just asking a traditional LLM like Claude or ChatGPT to invent a persona and utilize it.

Claudio Novelli:

There are many, many problems with using LLMs in this way. One of the most studied one is the so-called average persona effect. So language models are very good in replicating, emulating an average behavior, but they're not very good in reproducing outliers. And outliers, we know that in the political context, outliers are very important in, let's say, influencing other people and also shifting the political opinion.

Laura Karpas:

Outliers. The opinionated uncle at Thanksgiving, the woman at the dinner who changes everyone's mind. AI is seemingly bad at them. And so on the other end of the spectrum is the most accurate simulation possible. It's called a digital twin.

Claudio Novelli:

Because you're taking all my information, you're trying to replicate me as an individual.

Laura Karpas:

A synthetic version of a real individual person. And maybe while accurate, it's also the most legally and computationally fraught. So the best use case scenario, according to Novelli, is somewhere in the middle.

Claudio Novelli:

You can have synthetic populations where you are not trying to reproduce specific individuals, but you are trying just to reproduce the statistical distribution of some qualities. In that case, it is probably okay to use AI-based personas, but you have to factor the three variables to understand what is the best course of action for political parties.

Laura Karpas:

And maybe campaigns get good at managing this risk, but there's an entirely separate risk that this matrix isn't designed to measure: the risk to the shared reality we live in.

Jennifer Stromer-Galley:

If elites are now going to AI to get a sense of what the public's thinking and then reflect that back to the public, then my concern is that that then creates our reality that it has at its core artificiality.

Laura Karpas:

That's Jennifer Stromer-Galley, a professor of information studies at Syracuse University. She's been tracking how campaigns use technology since 1996. She wrote a book on it titled Presidential Campaigning in the Internet Age, which studies how politicians have used digital tools to interact with voters, to strategize and to persuade.

Jennifer Stromer-Galley:

The reason I study political elites is that I believe that they help drive the stories, the agendas that the public tends to think about. That reality setting is something that elites get to do. And for campaigns, the challenge of figuring out that message means that there is an eternal quest for the technology that will help them really finally figure that out and unlock that magic of the right message to the right audience at the right time.

Laura Karpas:

So now, 25 years later, after websites and social media, artificial intelligence is that next big tool. But here's the thing that Stromer-Galley will tell you. Even the old tools never really captured the public's opinions perfectly. Focus groups, the human ones, they were always a simulation. Trying, but not always succeeding at capturing reality.

Jennifer Stromer-Galley:

The ability to actually get good representative members of the public to talk to is really, really hard. It excluded the poor, it excluded the rich, it over-sampled white people, it over-sampled middle-class educated people. So even focus groups don't really help candidates really understand what the public thinks. So we've never had access to true public opinion.

Laura Karpas:

Which begs the question, who do these synthetic voters represent? Frontier models offer political parties the chance to put hard-to-reach demographics under the microscope. But what data actually informs these personas?

Jennifer Stromer-Galley:

The data that informs AI is not every perspective, every contingency. It's a small subset that was hoovered up from Flickr and Twitter and Reddit. All of these perspectives are a little bit to the extreme or an average of the extreme and still yet generic. It's even more removed from the public than the panels that currently exist. All of it which says to me that our ability to really measure public opinion, if we ever were actually able to measure public opinion, it's smoke and mirrors. It's a simulacrum. And that simulacrum risks warping how the public thinks on a set of policy matters, on how we think about ourselves relative to each other.

Laura Karpas:

And whether or not this tool pays off for political campaigns and the public, that's something we'll find out.

Jennifer Stromer-Galley:

I wouldn't be surprised at all if in these midterms for a couple of the key Senate campaigns to do that kind of experimentation, but I would expect in the 2028 presidential election, this will be the next cutting edge technology to try to unlock and move the needle.

Laura Karpas:

So after 100 years of chasing a way inside voters' heads, looking for the Holy Grail of the right message at the right time, the question now is whether AI simulated focus groups will provide campaigns a microscope or a room full of mirrors.

I'm Laura Karpas reporting for Tech Policy Press.