Weapons of Math Destruction
How Big Data Increases Inequality and Threatens Democracy
Ratings71
Average rating3.7
A former Wall Street quant sounds an alarm on the mathematical models that pervade modern life — and threaten to rip apart our social fabric
We live in the age of the algorithm. Increasingly, the decisions that affect our lives—where we go to school, whether we get a car loan, how much we pay for health insurance—are being made not by humans, but by mathematical models. In theory, this should lead to greater fairness: Everyone is judged according to the same rules, and bias is eliminated.
But as Cathy O’Neil reveals in this urgent and necessary book, the opposite is true. The models being used today are opaque, unregulated, and uncontestable, even when they’re wrong. Most troubling, they reinforce discrimination: If a poor student can’t get a loan because a lending model deems him too risky (by virtue of his zip code), he’s then cut off from the kind of education that could pull him out of poverty, and a vicious spiral ensues. Models are propping up the lucky and punishing the downtrodden, creating a “toxic cocktail for democracy.” Welcome to the dark side of Big Data.
Tracing the arc of a person’s life, O’Neil exposes the black box models that shape our future, both as individuals and as a society. These “weapons of math destruction” score teachers and students, sort résumés, grant (or deny) loans, evaluate workers, target voters, set parole, and monitor our health.
O’Neil calls on modelers to take more responsibility for their algorithms and on policy makers to regulate their use. But in the end, it’s up to us to become more savvy about the models that govern our lives. This important book empowers us to ask the tough questions, uncover the truth, and demand change.
— Longlist for National Book Award (Non-Fiction)
— Goodreads, semi-finalist for the 2016 Goodreads Choice Awards (Science and Technology)
— Kirkus, Best Books of 2016
— New York Times, 100 Notable Books of 2016 (Non-Fiction)
— The Guardian, Best Books of 2016
— WBUR’s “On Point,” Best Books of 2016: Staff Picks
— Boston Globe, Best Books of 2016, Non-Fiction
Reviews
Popular Reviews
Reviews with the most likes.
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.
If you're looking for hard data or a deep exploration into mathematical algorithms, this book will disappoint. It is, however, an eye-opening, bird's-eye view of a field that is quietly taking over quite a few parts of our lives. I applaud the author for expressing such a high level of empathy for people whose plights she does not share, and for providing such a well-written overview that even the layperson can understand.
For those that are being introduced to this topic, I highly recommend this book (my only criticism is the term Weapons of Math Destruction - or WMD - itself, and how often it is overused within the book). If you are interested in learning more about the specific ways in which machine learning and mathematical algorithms are wreaking havoc in different parts of society, other books are better poised to teach on the details of those topics, such as The New Jim Crow and Automating Inequality.
