Isaac Asimov’s satirical short story “Franchise” is highly relevant for today. The story was published in 1955 in the magazine If: World of Science Fiction.
The story depicts the absurdity of algorithmic predictions. A machine decides elections based on one voter to predict the results of the entire election.
Since people were “impatient” about tallying all the votes, “they invented special machines which could look at the first few votes and compare them with the votes from the same places in previous years. That way the machine could compute how the total vote would be and who would be elected.” Later on, “they built Multivac and it can tell from just one voter.”
One year, a man named Norman Muller is chosen as the voter. As is explained to Muller:
One factor isn’t known, though, and won’t be known for a long time. That’s the reaction pattern of the human mind. All Americans are subjected to the molding pressure of what other Americans do and say, to the things that are done to him and the things he does to others. Any American can be brought to Multivac to have the bent of his mind surveyed. From that the bent of all other minds in the country can be estimated. . . . Multivac picked you as the most representative this year. Not the smartest, or the strongest, or the luckiest, but just the most representative. Now we don’t question Multivac, do we?
Muller is reluctant to participate, but his family convinces him. He is “encased in machinery” and the machine will ask him questions. As the officials explain to Muller:
Multivac already has most of the information it needs to decide all the elections, national, state and local. It needs only to check certain imponderable attitudes of mind and it will use you for that. We can’t predict what questions it will ask, but they may not make much sense to you, or even to us.
Later on, we learn some of the questions Muller was asked. Uncannily prescient, the story anticipates a key refrain about the 2024 Presidential election about Trump winning because of the high price of eggs: “The one question Normal could remember at the moment was an incongruously gossipy: ‘What do you think of the price of eggs?’”
Norman is sworn to secrecy about the questions and his responses because the “less is known about the Multivac, the less chance of attempted outside pressures upon the men who services it.”
Too Much Faith in Technology
The humans fail to engage in critical thinking about Multivac; they blindly accept its authority and infallibility.
There’s a key lesson for today, as we still see way too much faith in technology. These days, we’re being gaslighted about AI, told constant tall tales and wild claims of AI being nearly conscious, of singularity being just minutes away.
Problems with AI Predictions
Although the story was satirical at the time, algorithmic predictions of a similar nature are being made today, leaving the main satire as having the prediction turn on the variable of person’s vote. Though modern algorithmic predictions are even made from aggregate data without the drama of gathering final pieces of data from one “representative” person.
Another absurdity is that a matter as important as an election would be left to a machine’s prediction, but currently, many matters are being made via algorithmic prediction, including decisions about hiring, credit worthiness, education, pretrial detention, and criminal sentencing.
The problem with AI predictions is that they are “snake oil” according to computer scientists Arvind Narayanan and Sayash Kapoor, who argue: “Accurately predicting people’s social behavior is not a solvable technology problem, and determining people’s life chances on the basis of inherently faulty predictions will always be morally problematic.”
As Hideyuki Matsumi and I wrote:
The use of algorithmic predictions for matters involving humans is fraught with problems. On the surface, these predictions seem beguiling. They gleam with the promise of higher accuracy and less bias than human predictions. But algorithmic predictions are, in reality, power dressed up with math. They are used not to see the future but to shape it. They draw from a particular construction of the past and aim to give it an iron grip on the future. The entities using algorithmic predictions are not predicting the future to understand it but to control it.
When entities gain control over people’s future, people lose control. Organizations make decisions and choices based on predictions, and people’s own decisions and choices no longer matter.
Unfortunately, AI predictions are spreading, and they are being trusted when they ought not to be. Carissa Véliz argues that predictions” can create a false sense of safety.” They beguile us with a feeling of certainty in a world of chance and contingency, where so much is out of our control and beyond our ability to predict or know for certain.
Consider the case of State v. Loomis, 881 N.W.2d 749 (Wis. 2016). A defendant was sentenced based on an AI prediction he was likely to commit future crimes. He argued he should be able to see how the AI algorithm worked to understand why it gave him a high recidivism risk. COMPAS, the company that created the algorithm, claimed that it was a trade secret. The Wisconsin Supreme Court rejected Loomis’s claim, choosing COMPAS’s trade secret over Loomis’s right to due process.
State v. Loomis enshrined into law an unjustified faith in tech. Far too often, companies, government officials, judges, and policymakers turn to tech to produce magic. They are spellbound by the self-interested hyped-up claims of tech companies. Asimov’s “Franchise” shows how ridiculous this faith is.
For Further Reading
Isaac Asimov, “Franchise” (1955)
Daniel J. Solove, Dangerous Oracles: Minority Report, Philip K. Dick, and AI Predictions, Substack (May 22, 2026)
Hideyuki Matsumi & Daniel J. Solove, The Prediction Society: Algorithms and the Problems of Forecasting the Future, 2025 U. Ill. L. Rev. 1 (2025)
Arvind Narayanan & Sayash Kapoor, AI Snake Oil: What Artificial Intelligence Can Do, What It Can’t, and How to Tell the Difference (2024)
Carissa Véliz, Prophecy: Prediction, Power, and the Fight for the Future, from Ancient Oracles to AI (2026)
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Daniel J. Solove is the Bernard Professor of Intellectual Property and Technology Law at the George Washington University Law School. He is the founder of TeachPrivacy, a company that provides workforce privacy, cybersecurity security, and AI training to companies and organizations around the world. He is the author of 10+ books and 100+ articles.
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