10 Machine Learning Ethics Mini-Essays

Appendix: Data Science Ethics

As a follow-up to the 10 questions about Machine Learning ethics, I tacked on these thoughts about the “Data Science Hippocratic Oath”

addressing the Professor at the end of the document:

I don’t know if you remember, but in the first lecture I made a point about asking about one of the items in the “Data Science Hippocratic Oath”, that said I should not “be overly impressed by Mathematics”. My inner Physicist, who is usually the first to think out loud about Mathematics, was, at first, baffled by the notion. Without Mathematics, Physics would be pretty lame. Same with Actuarial Science. Most Science I know of, wouldn’t even be Science without Math. I’m pretty sure the same is true of Data Science. What’s not to be overly impressed by? Though, you cited some good examples in the lecture that I think have helped me to come to an understanding of what it means. I think, it’s not about being impressed by the possibilities that a toolkit like Mathematics opens up. I think it’s more about one’s impression of individual acts of Mathematics. If something is wrong, or deceptive, or dangerous, or misleading, or seductive, or foolish, or half-baked, or ill-conceived, or malicious, or nonsense, or simply doesn’t lead to insights or solve a problem, it doesn’t get any extra points just because it happens to be Math, too.

My inner Physicist is already pretty comfortable with this idea. Euler’s Identity, for example, is a gobsmackingly impressive act of Mathematics, but only because you can watch, live, in real time, every day, as it helps humans to explain and predict real-world phenomena with blistering precision and reproduce-ability. The Math that predicted that BitCoin would be worth $650,000 each by now? Well, sure, it’s Math. May even be impressively well-conceived and well-executed Math. But it’s clearly nothing to be impressed by. It may be complete, and consistent, and perfect, but if it does not line up with the real world, it’s just noise. Science is the filter through which the impressiveness of an act of Mathematics can be determined, in my book. Here’s one of my heroes, Dr. Richard Feynman, speaking further on this: . So I have this as my take-away, and I’d be interested in any feedback you may have — I can be almost alarmingly impressed by Math or Science. But I am pretty much entirely unimpressed by Science that does not stand up under Math, or by Math that does not stand up under Science.