Researchers at the University of Copenhagen and Northeastern University in Boston have developed an algorithm that can predict a person’s life course, including premature death, in much the same way that large language models such as ChatGPT can predict sentences.
The death calculator, dubbed ‘DeathGPT’ by Financial Times, is based on narrative building just like it is in stories. According to scientists, each life story is the chronicle of a death foretold. By using Denmark’s registry data, which contains a wealth of day-to-day information on education, salary, job, working hours, housing and doctor visits, academics have developed an algorithm that can predict a person’s life course, including premature death, in much the same way that large language models (LLMs) such as ChatGPT can predict sentences. The algorithm outperformed other predictive models, including actuarial tables used by the insurance industry.
The fact that our complex existences can be resolved in text is both exhilarating and confusing. Sune Lehmann, from the Technical University of Denmark, who led the research published last month in Nature Computational Science, does not find the idea discombobulating. “I think the similarity between text and lives is deep and multi-faceted,” he told Financial Times. “It makes sense to me that our algorithm can predict the next step in human lives.”
Methodology
For a first step, researchers compiled a “vocabulary” of life events, creating a kind of synthetic language, and used it to construct “sentences”. A sample sentence might be: “During her third year at secondary boarding school, Hermione followed five elective classes.”
Loopholes
While the paper claims that “accurate individual predictions are indeed possible”, the algorithm furnishes a probability of death over a certain period rather than an exact date. There are caveats: what applies in Denmark might not apply elsewhere, and the algorithm encodes biases in the training data. Even so, given its potential to fine-tune risk prediction, the impact on the insurance industry will be worth watching. For their part, the researchers don’t want their work to be used by insurers, and are keeping the algorithm and data under wraps for now.
Outcomes
In existing predictive models, researchers must pre-specify variables that matter, such as age, gender and income. In contrast, this approach swallows all the data and can independently alight on relevant factors (it spotted that income counts positively for survival, for example, and that a mental health diagnosis counts negatively). This could point researchers to previously unexplored influences on health — and may uncover new links between apparently unrelated patterns of behaviour. One of Lehmann’s growing concerns is privacy; he points out that companies such as Google are assembling muscular prediction machines, using an abundance of personal data filtered from the internet.
This is an era of unparalleled predictability in human lives — and an era of unparalleled power for those who can read our stories before we have lived them.
