by Muhammad Aurangzeb Ahmad

In an earlier piece about teaching machines to predict death, I opened with the old idea that a person often meets their destiny on the road they take to avoid it. Stories around this themes are often read as parables of trying to evade prophecy where the effort to escape a fate becomes the mechanism that delivers it. Lately, I’ve been thinking that there’s another way to read these stories and parables. Each of them is, before it is anything else, a prophecy made about someone who has not yet been born, or has only just arrived, and cannot yet speak on his own behalf. In the story of Krishna, Kamsa is told that the eighth child of his sister will be his undoing. He starts killing her children as they come. The seventh is miraculously transferred to another womb, and the eighth is Krishna. In the story of Oedipus, Laius hears what his son will do and drives a pin through the infant’s feet and leaves him on a mountainside. The common theme here is that the prophecy comes first and the child arrives into a world that has already decided what he is.

I have spent the better part of a decade on problems and converns around end of life. This essay, and the one that will follow it, are about the mirror image of those concern: They are about the beginning of life, which I have realized are an equally interesting case. Consider this, at the end of life, we may entertain the possibility of an algorithm being able to speak for a person who once had a voice. We can at least ask whether it gets that person right. We can even ask whether the preferences it reconstructs are ones the patient would have recognized. At the beginning of life, the algorithm speaks about a person who has never had a voice at all. And in a growing number of clinics it does more than speak about that person. It helps decide whether that person will exist or not! Consider what already happens in a fertility clinic. A cycle of in vitro fertilization typically produces more embryos than a family will use, and someone has to decide which one to transfer first. For decades that decision rested on an embryologist looking through a microscope and grading each embryo by its shape and its rate of division, a practice that is skilled, subjective, and inconsistent between one laboratory and the next. Into that gap has come a class of deep learning systems that watch time-lapse footage of a developing embryo and return a single number meant to express its chance of implanting. One such system is iDAScore which produces a score between 1.0 and 9.9 which moves an embryo toward the front of the queue. Read more »