Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy
Rate it:
Open Preview
24%
Flag icon
nefarious
24%
Flag icon
The playbook for predatory advertisers is similar, but they carry it out at massive scale, targeting millions of people every day. The customers’ ignorance, of course, is a crucial piece of the puzzle.
25%
Flag icon
unwittingly
25%
Flag icon
To optimize recruiting—and revenue—they need to know whom their messages reached and, if possible, what impact they had. Only with this data can they go on to optimize the operation.
25%
Flag icon
That’s why much of the advertising money at for-profit universities goes to Google and Facebook. Each of these platforms allows advertisers to segment their target populations in meticulous detail.
25%
Flag icon
Once these campaigns move online, the learning accelerates. The Internet provides advertisers with the greatest laboratory ever for consumer research and lead generation. Feedback from each promotion arrives within seconds—a lot faster than the mail.
26%
Flag icon
throbbing
26%
Flag icon
teem
26%
Flag icon
underwhelmed
26%
Flag icon
An advertising program might start out with the usual demographic and geographic details. But over the course of weeks and months it begins to learn the patterns of the people it’s targeting and to make predictions about their next moves. It gets to know them. And if the program is predatory, it gauges their weaknesses and vulnerabilities and pursues the most efficient path to exploit them.
26%
Flag icon
beavering away.
27%
Flag icon
scrape together
27%
Flag icon
They found that diplomas from for-profit colleges were worth less in the workplace than those from community colleges and about the same as a high school diploma. And yet these colleges cost on average 20 percent more than flagship public universities.
27%
Flag icon
What these people need is money. And the key to earning more money, they hear again and again, is education.
28%
Flag icon
languished.
28%
Flag icon
all the rage
28%
Flag icon
Like those in the rest of the Big Data industry, the developers of crime prediction software are hurrying to incorporate any information that can boost the accuracy of their models.
29%
Flag icon
straying
30%
Flag icon
chiseling
30%
Flag icon
frisk.
31%
Flag icon
What we found, to no great surprise, was that an overwhelming majority of these encounters—about 85 percent—involved young African American or Latino men.
31%
Flag icon
ensnared
31%
Flag icon
The Constitution’s implicit judgment is that freeing someone who may well have committed a crime, for lack of evidence, poses less of a danger to our society than jailing or executing an innocent person.
32%
Flag icon
racketeering.
32%
Flag icon
In this system, the poor and nonwhite are punished more for being who they are and living where they live.
33%
Flag icon
The goal, if data were used constructively, would be to optimize prisons—much the way companies like Amazon optimize websites or supply chains—for the benefit of both the prisoners and society at large.
33%
Flag icon
upend
33%
Flag icon
Concern for privacy, on that occasion, trumped efficiency. But this won’t always be the case.
34%
Flag icon
From a mathematical point of view, however, trust is hard to quantify. That’s a challenge for people building models. Sadly, it’s far simpler to keep counting arrests, to build models that assume we’re birds of a feather and treat us as such.
35%
Flag icon
vouch
35%
Flag icon
bewildered.
35%
Flag icon
burgeoning
35%
Flag icon
Naturally, these hiring programs can’t incorporate information about how the candidate would actually perform at the company. That’s in the future, and therefore unknown. So like many other Big Data programs, they settle for proxies.
36%
Flag icon
galling
36%
Flag icon
cinch.
36%
Flag icon
And we do not know what those patterns are. We’re not told what the tests are looking for. The process is entirely opaque.
38%
Flag icon
So in this sense, the unequal paths to opportunity are nothing new. They have simply returned in a new incarnation, this time to guide society’s winners past electronic gatekeepers.
38%
Flag icon
The job was to teach the computerized system how to replicate the same procedures that human beings had been following. As I’m sure you can guess, these inputs were the problem. The computer learned from the humans how to discriminate, and it carried out this work with breathtaking efficiency.
39%
Flag icon
echelons
40%
Flag icon
cogs
40%
Flag icon
grist
40%
Flag icon
clerks
40%
Flag icon
muttering
40%
Flag icon
tow.
41%
Flag icon
harried
41%
Flag icon
scrambling
41%
Flag icon
hinges
41%
Flag icon
write-up.
42%
Flag icon
And because they need money so desperately, the companies can bend their lives to the dictates of a mathematical model.
42%
Flag icon
frazzled