Hamish MacEwan

Some of the skepticism is a reaction against the hype; a lot of it arises from ignorance, and it has the same smell as the rich history of science denial from the tobacco industry (and probably much earlier) onward.

But there’s another thread of data skepticism that’s profoundly important. On her MathBabe blog, Cathy O’Neil has written several articles about lying with data — about intentionally developing models that don’t work because it’s possible to make more money from a bad model than a good one.[] In a slightly different vein, Cathy argues that making machine learning simple for non-experts might not be in our best interests; it’s easy to start believing answers because the computer told you so, without understanding why those answers might not correspond with reality.

I’ve often wondered how big is big enough