Cathy O’Neil’s important new book Weapons of Math Destruction, is out today, and if you’re at all interested in the social significance of how mathematics is now being used, you should go out and get a copy. She has been blogging for quite a while at Mathbabe, which you should be following, and is a good place to start if your attention span is too short for the book.
Cathy has had an interesting career path, including a period as my colleague here in the math department at Columbia. She left here to pursue excitement and fortune at a top hedge fund, D.E. Shaw, where she had a front-row seat at the 2008 financial industry collapse. A large factor in that collapse was the role played by mathematical models, and her book explains some of that story (for another take on this, there’s Models.Behaving.Badly from another Columbia colleague, Emanuel Derman). As far as I’ve ever been able to figure out, the role of mathematical modeling in the mortgage backed securities debacle was as a straightforward accessory to fraud. Dubious and fraudulent lending was packaged using mathematics into something that could be marketed as a relatively safe investment, with one main role of the model that of making it hard for others to figure out what was going on. This worked quite well for those selling these things, with the models successfully doing their job of obscuring the fraud and keeping most everyone out of jail.
While this part of the story is now an old and well-worn one, what’s new and important about Weapons of Math Destruction is its examination of the much wider role that mathematical modeling now plays in our society. Cathy went on from the job at D.E. Shaw to work first in risk management and later as a data scientist at an internet media start-up. There she saw some of the same processes at work:
In fact, I saw all kinds of parallels between finance and Big Data. Both industries gobble up the same pool of talent, much of it from elite universities like MIT, Princeton and Stanford. These new hires are ravenous for success and have been focused on external metrics – like SAT scores and college admissions – their entire lives. Whether in finance or tech, the message they’ve received is that they will be rich, they they will run the world…
In both of these industries, the real world, with all its messiness, sits apart. The inclination is to replace people with data trails turning them into more effective shoppers, voters, or workers to optimize some objective… More and more I worried about the separation between technical models and real people, and about the moral repercussions of that separation. If fact, I saw the same pattern emerging that I’d witnessed in finance: a false sense of security was leading to widespread use of imperfect models, self-serving definitions of success, and growing feedback loops. Those who objected were regarded as nostalgic Luddites.
I wondered what the analogue to the credit crisis might be in Big Data. Instead of a bust, I saw a growing dystopia, with inequality rising. The algorithms would make sure that those deemed losers would remain that way. A lucky minority would gain ever more control over the data economy, taking in outrageous fortunes and convincing themselves that they deserved it.
The book then goes on to examine various examples of how Big Data and complex algorithms are working out in practice. Some of these include:
- The effect of the US News and World Report algorithm for college ranking, as colleges try and game the algorithm, while at the same time well-off families are at work gaming the complexities of elite college admissions systems.
- The effects of targeted advertising, especially the way it allows predatory advertisers (some for profit educational institutions, payday lenders, etc.) to very efficiently go after those most vulnerable to the scam.
- The effects of predictive policing, with equality before the law replaced by an algorithm that sends different degrees of law enforcement into different communities.
- The effects of automated algorithms sorting and rejecting job applications, with indirect consequences of discrimination against classes of people.
- The effects of poorly thought-out algorithms for evaluating teachers, sometimes driving excellent teachers from their jobs .
- The effects of algorithms that score credit, determine access to mortgages and to insurance, often with the effect of making sure that those deemed losers stay that way.
Finally, there’s a chapter on Facebook and the way political interests are taking advantage of the detailed information it provides to target their messages, to the detriment of democracy.
To me, Facebook is perhaps the most worrisome of all the Big Data concerns of the book. It now exercises an incredible amount of influence over what information people see, with this influence sometimes being sold to the highest bidder. Together with Amazon, Google and Apple, our economy and society have become controlled by monopolies to an unparalleled degree, monopolies that monitor our every move. In the context of government surveillance, Edward Snowden remarked that we are now “tagged animals, the primary difference being that we paid for the tags and they’re in our pockets.” A very small number of huge extremely wealthy corporations have even greater access to those tags than the government does, recording every movement, communication with others, and even every train of thought as we interact with the web.
These organizations are just starting to explore how to optimize their use of our tags, and thus of us. Of the students starting classes here today in the math department, our training will allow many of them to go on to careers working for these companies. As they go off to work on the algorithms that will govern the lives of all of us, I hope they’ll start by reading this book and thinking about the issues it raises.
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