O’Neil deserves some credit right off the bat for not waiting until her retirement from the hedge fund where she worked to tell us the secrets of how corporations use big data (our data). Underlying the collection and use of big data is an attempt to utilize efficiencies in the market place for goods, money, and talent. Big data ostensibly can also “set us free” from time constraints and uneven knowledge dispersal. Conversely the opposite is often true. We are at the mercy of how our own data is shredded and packaged, and errors in the model can mean mutually assured destruction—for the school, corporation, family.
The book starts with examples any readers who actually picked up this book to read might recognize: the chances of getting into a major university. O’Neil doesn’t go into the actual algorithms but just explains the variables chosen to populate the algorithms. Just when I was wondering who this book is targeted at, since after all, we kind of know how to get into university already, she comes up with examples of big data messing with aspirations that are still (hopefully not) in our futures.
She addresses the real pain-in-the-ass nature of minimum wage jobs where the inadequate part-time hours are constantly changing to maximize profits for owners and to screw with employees ability to plan their life, their children’s lives, and the children’s caretaker’s lives. O’Neil addressed the situation in 2009 when Amex decided to reduce the risk of credit card nonpayment by reducing the credit ceilings on users who shopped at certain stores, like Walmart. She shows us the way micro-targeting ends up using data to perpetuate inequities in opportunity and “social capital.”
The hardest part of reading this book (there is no actual math), was keeping my mind on what O’Neil was saying. Every time she'd mention another example of the ways big data was screwing us over, my mind would wander to experiences of my own, or ones I’d heard from friends, family, or others. This is real stuff, and just when I thought that it would be an excellent book for those with skills and interest in social justice to take to an interview with Google, Amazon, or a big bank, in she comes with another example of how the “fixes” are almost worse than the disease (Facebook’s method of who your friends are determining your credit risk).
But O’Neil reminds us big data, mathematics, algorithms, etc. aren’t going to go away.
"Data is not going away. Nor are computers—much less mathematics. Predictive models are, increasingly, the tools we will be relying on to run our institutions, deploy our resources, and manage our lives. But as I’ve tried to show throughout this book, these models are constructed not just from data but from the choices we make about which data to pay attention to—and which to leave out. Those choices are not just about logistics, profits, and efficiency. They are fundamentally moral."Exactly. We still have to use our brains, not just our computers. It is critical that we inject morality into the process or it will always be fundamentally unfair in some way or another, especially if the intent is to increase profits for one entity at the expense of another. One simply can’t include enough variables or specifics. Some universities have begun to audit the algorithms—like Princeton’s Transparency and Accountability Project—by masquerading as people of differing backgrounds and seeing what kind of treatment these individuals receive from online marketers.
O’Neil suggests that sometimes data might be used to good effect by targeting frustrated online commenters with solutions to their issues: i.e., affordable housing info, or by searching out possible areas of workplace or child abuse and targeting that area with resources. She wades into national election data and notes that only swing states get candidates attention, suggesting, by the way, that the electoral college has outlived its usefulness to the citizenry. Algorithms are not going to administer justice or democracy unless we find a way to use them as a tool to root out inequities and try to find ways to deliver needed services where they are deficient.
When I look at the totality of what O’Neil has discussed, I am inclined to think this book is best targeted to thoughtful high schoolers and college-aged students who are thinking about planning their careers, who have a penchant for mathematical and computer modeling, and who think their dream job might be with an online giant. I’d be happy to be disabused of this notion if someone wants to challenge my thought that much of this information is known to many of us who have been out of school for awhile and who have been paying attention to our online experiences and junk mail solicitations. But it is always interesting to read someone as coherent and on the side of social justice as Ms. O’Neil.
It might be noted that Jaron Lanier in Who Owns the Future? (2013) also talks about the use of big data to steer our thinking and makes a preliminary suggestion that individuals should be paid for their data—for data that is collected about them, for profit. It is an interesting discussion as well. Love these intersections of technology and humanity.
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