Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy, Cathy O'Neil (100%)

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Many of the WMDs I’ll be discussing in this book, including the Washington school district’s value-added model, behave like that. They define their own reality and use it to justify their results. This type of model is self-perpetuating, highly destructive—and very common.
The privileged, we’ll see time and again, are processed more by people, the masses by machines.
First, teacher evaluation algorithms are a powerful tool for behavioral modification. That’s their purpose, and in the Washington schools they featured both a stick and a carrot.
The human victims of WMDs, we’ll see time and again, are held to a far higher standard of evidence than the algorithms themselves.
What does a single national diet have to do with WMDs? Scale. A formula, whether it’s a diet or a tax code, might be perfectly innocuous in theory. But if it grows to become a national or global standard, it creates its own distorted and dystopian economy. This is what has happened in higher education.
However, when you create a model from proxies, it is far simpler for people to game it. This is because proxies are easier to manipulate than the complicated reality they represent.
The point of Bayesian analysis is to rank the variables with the most impact on the desired outcome.
Workforce Ready HR, that promised to eliminate “the guesswork” in hiring, according to its web page: “We can help you screen, hire, and onboard candidates most likely to be productive—the best-fit employees who will perform better and stay on the job longer.
we’ve seen time and again that mathematical models can sift through data to locate people who are likely to face great challenges, whether from crime, poverty, or education. It’s up to society whether to use that intelligence to reject and punish them—or to reach out to them with the resources they need.
The root of the trouble, as with so many other WMDs, is the modelers’ choice of objectives. The model is optimized for efficiency and profitability, not for justice or the good of the “team.”
In statistics, this phenomenon is known as Simpson’s Paradox: when a whole body of data displays one trend, yet when broken into subgroups, the opposite trend comes into view for each of those subgroups.
There are a couple of problems. First, if the system attributes risk to geography, poor drivers lose out. They are more likely to drive in what insurers deem risky neighborhoods. Many also have long and irregular commutes, which translates into higher risk.
This is a far cry from insurance’s original purpose, which is to help society balance its risk. In a targeted world, we no longer pay the average. Instead, we’re saddled with anticipated costs. Instead of smoothing out life’s bumps, insurance companies will demand payment for those bumps in advance.
These automatic programs will increasingly determine how we are treated by the other machines, the ones that choose the ads we see, set prices for us, line us up for a dermatologist appointment, or map our routes. They will be highly efficient, seemingly arbitrary, and utterly unaccountable. No one will understand their logic or be able to explain it.
“That’s a useful concept,” writes Keith Devlin, the mathematician and science author. “But if you try to apply it to any one person, you come up with the absurdity of a person with 2.4 children. Averages measure entire populations and often don’t apply to individuals.”
But if the candidate knows these voters are angry about rent, how about using the same technology to identify the ones who will most benefit from affordable housing and then help them find it?
Change that objective from leeching off people to helping them, and a WMD is disarmed—and
Big Data processes codify the past. They do not invent the future. Doing that requires moral imagination, and that’s something only humans can provide. We have to explicitly embed better values into our algorithms, creating Big Data models that follow our ethical lead. Sometimes that will mean putting fairness ahead of profit.
And, as we’ve seen repeatedly throughout the book, the resulting pain is not distributed equally, but is rather borne by society’s most vulnerable citizens.

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Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy, Cathy O'Neil (100%) #46