Democracy Needs Friction To Function

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Democracy Needs Friction To Function

AI promises to help us find common ground. But democracy was never meant to resolve our disagreements.

Okazz for Noema Magazine

Credits

HennyGe Wichers is a writer and researcher based in London. Her work traces the friction between technology and human society.

Editor’s note: Noema is transparent about AI use in its pieces. We publish original, human-generated ideas but allow authorized, disclosed use of AI in certain cases. Please see details at the end of this piece.

TOKYO — In the run-up to Japan’s lower house election earlier this year, a new party called Team Mirai — or “future” in Japanese — was doing something different: actively listening. Its AI-powered chatbot walked voters through policy proposals, answering their questions and probing their thinking about what they cared about. In the end, the party fielded more than 38,000 questions and collected over 6,000 suggestions from voters that helped surface one of the central dilemmas in Japanese politics.

Voters were worried about rising prices and wanted immediate relief. But a significant minority were skeptical of the solution other parties were offering — cutting the consumption tax — partly because of potential impacts on social programs funded by the tax. The concern is reasonable given Japan’s debt burden, now over 200% of GDP and the highest in the G7.

Team Mirai chose to represent voters skeptical of the tax cuts other parties were promising. The party won 11 seats on over three million votes (almost 7% of all votes cast) — more than double what it had aimed for in its first national election.

Using AI to listen more effectively helped Team Mirai find constituencies that conventional politics were not representing. But the chatbot didn’t listen to everything. Some conversational boundaries had already been set — whether intentionally or through the constraints of the systems the chatbot relied on.

The Listening Assumption

Democratic innovators, from political scientists to civic technologists, believe that democracy’s problem is, at least in part, a listening problem: if institutions could hear what citizens really think and care about, political disagreement could become more tractable and solutions clearer. The assumption is a hopeful one. But it is also, implicitly, an exercise of power, because someone must decide what gets heard: which voices are included, how opinions are interpreted, what counts as consensus and which problems are important.

After the turn of the century, a wave of innovation aimed at improving listening began. In the 2010s, a Citizens’ Assembly in Ireland helped break political inertia on abortion, producing recommendations that were approved by around 66% of voters. The Assembly consisted of 99 citizens, randomly selected to be broadly representative of Irish society. But that choice embeds assumptions about representation: who counts as a meaningful voice, and what diversity is.

In 2019, France tried democratic listening on a national scale. President Macron launched the Great National Debate after the Yellow Vest crisis, which began over a fuel tax increase and widened into anger over living costs and the distance between governing elites and citizens. The response, as political scientist Hélène Landemore documented, ran over two months: around 10,000 town-hall meetings, almost 19,000 local grievance books — reviving the cahiers de doléances of 1789, where citizens recorded their complaints and proposals on the eve of the Revolution — 21 randomly selected citizen assemblies and 1.9 million online contributions. But scale came with its own problems. While the smaller assemblies moved closer to deliberation, the online platform mostly gathered views. Hearing more people did not mean hearing them better.

Quadratic voting — advanced by Glen Weyl and Eric Posner — tries to capture what assemblies do well but at a lower cost. It gives voters a budget of credits to express how much they care about a particular issue, rather than letting them cast a single equally weighted vote. The cost of casting more votes on the same choice rises quadratically, so that preference intensity influences the outcome. But it can’t hear people who don’t know how much they care, who don’t have the time to figure that out or who express care differently.

Polis, the platform used in Taiwan’s vTaiwan process, largely removes the selection problem: It allows all participants to submit statements and vote on others’ contributions at scale. But the choice of what counts as meaningful hasn’t disappeared — it has moved to the algorithm. Polis maps clusters of opinion and highlights statements that gain support across groups, so less-supported or more-divisive views fade into the background. Persistent disagreement isn’t excluded, but it is — by design — not the center of attention.

Each of these innovations refines the information...