Tallying the results of Washington, DC’s first ranked-choice election in June took about 10 days—longer than many voters are used to. In an age when artificial intelligence can generate an answer in seconds, waiting days for election results can feel old-fashioned, inefficient, even suspicious. And it is about to matter far more widely. This November, 17 states, cities, and counties will use ranked-choice voting, including Maine and Alaska statewide, in races that could help decide control of Congress. More voters than ever will watch results take days to resolve, and more will be asked to trust a count they cannot see.

But that slowness may be one of its most democratic features.

I come to this question from health informatics, where I study how even AI-generated information that sounds clear, fluent, and helpful still requires careful review before it reaches a patient. The same verification problem applies to elections. The more authoritative a system sounds, the more important it becomes to make sure the output can be checked.

DC’s June 16 primary followed a system that requires more than a simple tally: Voters rank candidates, and if no candidate receives more than 50% of first-choice votes, lower-performing candidates are eliminated, and votes are redistributed according to voters’ next choices. New York City already uses ranked-choice voting in local primary and special elections.

AI may soon be able to produce election results in seconds. That does not mean it should.

There is nothing wrong with ranked-choice voting, but it must be carried out with verifiable results. Because election counting is not just a math problem; it is a trust problem, especially in the US today, where election workers face harassment, routine counting delays are recast as fraud, and many voters already doubt institutions before a single ballot is counted.

In this climate, counting ranked-choice votes in a way that allows for public verification might take longer than people are used to. Every ballot must be tied to a voter-verifiable record. Every round of tabulation must be explainable. Every disputed outcome must be auditable by people who can inspect the evidence themselves.

AI is not yet counting votes, but AI and machine-assisted systems are already touching elections before ballots are counted, including information voters receive, and how their signatures are reviewed. In 2024, X’s Grok chatbot gave users false information about ballot deadlines; after election officials from five states complained, X changed Grok so election-related questions directed users to Vote.gov, the federal government’s official voting information website. In North Carolina, 10 counties piloted automated signature-verification software for absentee-by-mail ballots in 2024. The pilot did not affect whether any ballot was counted, but later reporting found reliability problems: The software failed to match about 11% of signatures, most software-flagged signatures were approved after human review, and technical issues complicated the test. Neither example is the same as artificial intelligence counting votes, but both show how software can shape what voters are told or what happens before a ballot enters the count.

I have seen the problem in my own research. AI systems can cite studies that do not exist, with titles, authors, and journals that look real until you check them. A 2026 Nature analysis⁠ warned that hallucinated citations are........

© Common Dreams