When I was in grad school both of my parents developed cancer (!) and thus, doing mostly stats at the time and with all my bio training, I dug into the literature. The numbers were all grim and the role of a lot of screening was surprising! But the biggest issue was the hype /1 https://twitter.com/stochastician/status/1319086942521335808">https://twitter.com/stochasti...

My parents would see a story on TV about "MIT developers treatment X" or "new Stanford research Y" and ask me, day after day, if it could help them. I always had to say no, and explain to them that these were just prototypes, ideas, demonstrations. Clinical impact was far. /2

The reality was often worse, and was something I saw repeatedly even in my own department. Fundamental research, while important (crucial!), was often carelessly spun into a promise of near-term clinical impact. Often this was not deliberate or malicious (although not always) /3

A grad student optimistically expressing to someone in the news office that "someday" it might lead to a treatment. A professor referencing a disease that may have originally spurned interest. I blame the news offices for a lot of the out-of-control hype. /4

Understandably, the public focuses on the clinical potential. My parents care far more about science giving them hope than they do about MAP kinase pathways. I think as scientists we have tremendous responsibility to police our own hype, because people will run with it. /5

I see this happening with ML technologies. I see promises that will obviously never be fulfilled. I see weasel words and self-promoting singulatarians. And it happens a lot at the intersection of ML and biology. /6

We have to do better, because policy decisions are being made on this hype. We have to do better, because as a field we should value humility. And we have to do better, because I& #39;m tired of telling my loved ones that no, there& #39;s no magic bullet, in spite of what they saw on CNN.