by Muhammad Aurangzeb Ahmad

In the first part of this series I described the data footprint that now surrounds a child before birth. It could take the form of an implantation score attached to an embryo, a polygenic risk estimate read from a few of its cells, a sequenced genome, growth curves etc. These may seem harmless or even mundane but the The technology is moving toward something much more ambitious i.e., towards a prediction of the life itself.

Consider the following cautionary tale: an AI model called life2vec was developed by researchers working with Danish national registry data. The data is a summary of recorded lives of roughly six million people. It includes things like Births, jobs, illnesses, relocations, incomes and diagnoses become events in a sequence. The model reads them much as a language model reads words in a sentence and learns what tends to come next. The analogy being that a human life can be represented as a sequence and, having read enough of that sequence, a machine can try to read forward. Once trained, life2vec could predict several kinds of outcomes, including personality characteristics and early mortality. In the tasks reported by its developers it outperformed specialized baselines. The researchers have also been careful about what this means. The model does not tell someone the date on which they will die. It was not released as a public “death calculator,” and its creators objected when it was described that way.

Now let’s get back to our main topic, the embryo grade and neonatal probability I discussed earlier were judgments made at one point in time. The systems now being developed can potentially go way beyond that. Foundation models trained on long stretches of electronic health records can absorb a person’s accumulating clinical history and repeatedly revise their estimates. A risk calculated in the neonatal unit could therefore become the first entry in a forecast that follows the patient for years. Genetic predictions are even easier to carry forward. Older forms of prophecy rarely had this institutional reach. A natal horoscope did not really set an insurance premium. A machine-readable risk score might do that if appropriate safeguards are not added to the system. Read more »