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If you believe that the fundamental fact of the world is its mistreatment of women, you usually discover that each new thing you investigate—quelle surprise!—mistreats women. If you begin with an overwhelming presupposition of racism, you often find racism down at the root of everything. And if you start with the long history of oppressing the poor, you almost always find, at the end of the day, that the poor are being oppressed.
The trouble is not that the results of such investigations are necessarily wrong. Tautologies tend to be true, after all. But the tautological also tends to tell us nothing new. The failure of circularity comes in part from the skepticism it engenders in us when we encounter it—tempting us to dismiss a book by indulging our own fallacy to say, in essence, Of course that author finds that result. The more important failing, however, comes from the fact that circularity rarely marks an advance. If the reason for some social problem is a structural flaw in the entire history of the world, then it has no discrete solution. Short of the end times, short of the divine resolution of everything human, there exists no human way to fix it.
We’ve had any number of books in recent months that look at current technology through such predetermined lenses. Andrew Guthrie Ferguson’s The Rise of Big Data Policing: Surveillance, Race, and the Future of Law Enforcement, for example. Safiya Umoja Noble’s Algorithms of Oppression: How Search Engines Reinforce Racism. The paperback edition of Cathy O’Neil’s Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy.
Each of these does some interesting reporting on the uses and abuses of the latest advances in the computer revolution. It’s true none of them, not even Cathy O’Neil’s Weapons of Math Destruction, really has strong objections to the whole idea of computerizing culture (at least, not when compared with the almost pyrrhonian skepticism about social statistics displayed in Jerry Z. Muller’s latest volume, The Tyranny of Metrics). Still, they are all rightly suspicious of the current effects of that computerizing. And they each close their argumentative circle like a gate slamming shut, leaving us not one step further on. We end mostly with just an unhelpful truism: If an economic technique or governmental instrument is oppressive, that oppression is usually going to fall more heavily on the poor and the vulnerable. The answer we need is how to halt oppression, in itself, not a conclusion that we need to spread the oppression around more fairly.
And then we have Virginia Eubanks’s Automating Inequality: How High-Tech Tools Profile, Police, and Punish the Poor. In many ways, this book is a model of how to think about the current effects of the strange expectation that computers will solve the problems of our social programs. Not in every way. Eubanks feels compelled to signal her political virtuousness at times, reminding readers that she is a standard-issue lefty, opposed to those evil Republicans. And she stumbles at times into the dead circle, falling to the temptation to blame everything on the culture’s endemic prejudices about race and economic class, which leaves readers just to shrug about the computer revolution: More of the same, innit? Ah, well. Move along.
But if we read past the occasional politics and occasional dead ends of tautology, conservatives and liberals alike will find that Automating Inequality is the best book we have thus far about the ways in which governments at nearly every level of authority are using computer algorithms as essentially magic: easy technological substitutes for the difficult balance of sympathy and intelligence needed to govern the messy thing that is human society.
Automating Inequality looks, for example, at the 2006 welfare reform launched under Governor Mitch Daniels in Indiana. We could argue about whether Indiana’s welfare systems needed reform. Republicans tended to think it did, while Democrats tended to think it did not. But that’s just politics, the kind of dispute elections are designed to resolve. In Automating Inequality, Eubanks clearly sides with the virtuous Democrats against the vicious Republicans, the same old dull masquerading of political partisanship as logical argument. But the better and far more interesting analysis she performs is of the means that Indiana used to implement its welfare reform.
Daniels had insisted that new computerization would “clean up welfare waste” by reducing the number and, especially, the authority of welfare caseworkers (some of whom, he reasonably noted, were caught in corrupt collusion with the people in their casework). IBM had a new set of hardware and software—a new algorithm—that would provide an objective and incorruptible substitute for all those faulty human beings.
Indiana quickly…