Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy

Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy by Cathy O’Neil.

math-weapons

From the description at Amazon:

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 shocking 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 race or neighborhood), 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, from college to retirement, O’Neil exposes the black box models that shape our future, both as individuals and as a society. Models that score teachers and students, sort resumes, grant (or deny) loans, evaluate workers, target voters, set parole, and monitor our health—all have pernicious feedback loops. They don’t simply describe reality, as proponents claim, they change reality, by expanding or limiting the opportunities people have. O’Neil calls on modelers to take more responsibility for how their algorithms are being used. 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.

Even if you have qualms about Cathy’s position, you have to admit that is a great book cover!

When I was in law school, I had F. Hodge O’Neal for corporation law. He is the O’Neal in O’Neal and Thompson’s Oppression of Minority Shareholders and LLC Members, Rev. 2d.

The publisher’s blurb is rather generous in saying:

Cited extensively, O’Neal and Thompson’s Oppression of Minority Shareholders and LLC Members shows how to take appropriate steps to protect minority shareholder interests using remedies, tactics, and maneuvers sanctioned by federal law. It clarifies the underlying cause of squeeze-outs and suggests proven arrangements for avoiding them.

You could read Oppression of Minority Shareholders and LLC Members that way but when corporate law is taught with war stories from the antics of the robber barons forward, you get the impression that isn’t why people read it.

Not that I doubt Cathy’s sincerity, on the contrary, I think she is very sincere about her warnings.

Where I disagree with Cathy is in thinking democracy is under greater attack now or that inequality is any greater problem than before.

If you read The Half Has Never Been Told: Slavery and the Making of American Capitalism by Edward E. Baptist:

half-history

carefully, you will leave it with deep uncertainty about the relationship of American government, federal, state and local to any recognizable concept of democracy. Or for that matter to the “equality” of its citizens.

Unlike Cathy as well, I don’t expect that shaming people is going to result in “better” or more “honest” data analysis.

What you can do is arm yourself to do battle on behalf of your “side,” both in terms of exposing data manipulation by others and concealing your own.

Perhaps there is room in the marketplace for a book titled: Suppression of Unfavorable Data. More than hiding data, what data to not collect? How to explain non-collection/loss? How to collect data in the least useful ways?

You would have to write it as a how to avoid these very bad practices but everyone would know what you meant. Could be the next business management best seller.

Comments are closed.