Would you like to know when you’re going to die?

A group of Danish and American social scientists announced that with artificial intelligence and a huge data set, they were able to predict the likelihood of a person’s death within the next four years with startling accuracy. “Using Sequences of Life-Events to Predict Human Lives,” published in Nature Computational Science in December, was like a Christmas gift from Blumhouse. The internet exploded with reports of a super-creepy AI “doom calculator.” Overnight, the paper became the most famous Danish rumination on mortality since “To be or not to be.”

Communal jump scares are irresistible, and research that cast AI as the oracle of death was the perfect way to end 2023. The academics knew it, too. “We thought it sounds kind of Minority Report, so maybe it’ll get a little bit of attention for the paper,” says Sune Lehmann, one of the authors and a professor of networks and complexity science at the Technical University of Denmark. “Clearly that was a miscalculation, because it got this insane amount of attention! But really it’s not what the paper is about.”

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It’s true. The marketing was a success, but it did the scholarship a disservice. Because “Using Sequences of Life-Events to Predict Human Lives” isn’t just a gimmick. It’s a fascinating merger of AI and social science that raises way more uncomfortable questions about how we live than about when we’ll die.

The project started from a basic observation about artificial intelligence. In large language models, words are transformed into numeric tokens. Then the tokens are examined in impossibly large combinations to find their relationships. Humans use grammar and logic to sort words, but the sheer size of a neural network allows computers to find patterns and correlations among the tokens that we could never spot.

The researchers wondered what would happen if, instead of text, they tokenized reams of data about everyday life. To exclude recent events such as the pandemic and ensure they could determine, ahem, final outcomes, they relied on data recorded from 2008 to 2015. “If you think about the social sciences, a lot of what they try to process are high-dimensional event sequences,” Lehmann, 47, told me over Zoom from his home in Copenhagen. (Yes, there was a very fast-looking bike in the room, and although he had a mild case of covid-19, he could not have been cheerier.) “So what if we use AI and think of each life as words in a sentence? You’re born, you see the pediatrician, you go to school, you get a job, and so on. What are the correlations that we can learn?”

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To contemplate this kind of project, you need data from people willing to reveal much more about themselves than you’d get from an ordinary census. You also need that data to be as obsessively organized as a chef’s pantry. You need the Danes. “In Denmark, we have this wild thing — we trust the government! We like them,” Lehmann says.

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That trust is the foundation of Statistics Denmark, a well-funded state agency established in 1966 to keep track of everything that might happen to a Dane. The data is available to any researcher affiliated with a Danish university, but it must be anonymized; a single lapse can cause the whole institution to lose access. There are highly specific categories for medical conditions, schooling, income, personality nuances and … duck strikes. “It’s a category for, like, if you’re moving along the landscape, and then you’re struck by a duck,” Lehmann says. “That’s how granular it........

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