Weapons of Math Destruction

Math! And social justice! Two of my favorite things! What's not to like?

Unfortunately, kind of a lot. Look: people who read math books for fun are math nerds. Dumbing down math concepts with cutesy terms is not needed. It will not make people who would not otherwise read math for fun read your book and it will piss off the rest of us. Also, it's lazy. And it's bad math – O'Neil uses the term “weapon of math destruction” (over and over) very vaguely, so that she doesn't have to define exactly what she's talking about. Oh, she claims that she has a clear definition, but then she calls things like Racial Profiling a WMD (cringe). Racial Profiling isn't an algorithm; it's a cognitive heuristic and it doesn't relay on Big Data.

More problematically, I think she uses this term to obscure that a lot of her points are actually about cognitive biases, racial inequality and socioeconomic inequality, rather than the data science used to enforce these. She herself acknowledges that some things (like, e.g. racial profiling) have happened to exactly the current degree long before data science was available.

Overall, I found her approach really shallow. She's a former tenured ivy league math professor! I wanted her to write a book that only she could write – full of nuance and equations I needed a scratchpad to struggle through.

Nonetheless, I think some of her points were good: that machine-learning algorithms are dense and require supervision and critical thinking as to their results rather than blind trust. It's an important book for the math-phobic.