Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy (16 page)

Defenders of the tests note that they feature lots of questions and that no single answer can disqualify an applicant. Certain patterns of answers, however, can and do disqualify them. And we do not know what those patterns are. We’re not told what the tests are looking for. The process is entirely opaque.

What’s worse, after the model is calibrated by technical experts, it receives precious little feedback. Again, sports provide a good contrast here. Most professional basketball teams employ data geeks, who run models that analyze players by a series of
metrics, including foot speed, vertical leap, free-throw percentage, and a host of other variables. When the draft comes, the Los Angeles Lakers might pass on a hotshot point guard from Duke because his assist statistics are low. Point guards have to be good passers. Yet in the following season they’re dismayed to see that the rejected player goes on to win Rookie of the Year for the Utah Jazz and leads the league in assists. In such a case, the Lakers can return to their model to see what they got wrong. Maybe his college team was relying on him to score, which punished his assist numbers. Or perhaps he learned something important about passing in Utah. Whatever the case, they can work to improve their model.

Now imagine that Kyle Behm, after getting red-lighted at Kroger, goes on to land a job at McDonald’s. He turns into a stellar employee. He’s managing the kitchen within four months and the entire franchise a year later. Will anyone at Kroger go back to the personality test and investigate how they could have gotten it so wrong?

Not a chance, I’d say. The difference is this: Basketball teams are managing individuals, each one potentially worth millions of dollars. Their analytics engines are crucial to their competitive advantage, and they are hungry for data. Without constant feedback, their systems grow outdated and dumb. The companies hiring minimum-wage workers, by contrast, are managing herds. They slash expenses by replacing human resources professionals with machines, and those machines filter large populations into more manageable groups. Unless something goes haywire in the workforce—an outbreak of kleptomania, say, or plummeting productivity—the company has little reason to tweak the filtering model. It’s doing its job—even if it misses out on potential stars.

The company may be satisfied with the status quo, but the victims of its automatic systems suffer. And as you might expect,
I consider personality tests in hiring departments to be WMDs. They check all the boxes. First, they are in widespread use and have enormous impact. The Kronos exam, with all of its flaws, is scaled across much of the hiring economy. Under the previous status quo, employers no doubt had biases. But those biases varied from company to company, which might have cracked open a door somewhere for people like Kyle Behm. That’s increasingly untrue. And Kyle was, in some sense, lucky. Job candidates, especially those applying for minimum-wage work, get rejected all the time and rarely find out why. It was just chance that Kyle’s friend happened to hear about the reason for his rejection and told him about it. Even then, the case against the big Kronos users would likely have gone nowhere if Kyle’s father hadn’t been a lawyer, one with enough time and money to mount a broad legal challenge. This is rarely the case for low-level job applicants.
*

Finally, consider the feedback loop that the Kronos personality test engenders. Red-lighting people with certain mental health issues prevents them from having a normal job and leading a normal life, further isolating them. This is exactly what the Americans with Disabilities Act is supposed to prevent.

The majority of job applicants, thankfully, are not blackballed by automatic systems. But they still face the challenge of moving their application to the top of the pile and landing an interview. This has long been a problem for racial and ethnic minorities, as well as women.

In 2001 and 2002, before the expansion of automatic résumé
readers,
researchers from the University of Chicago and MIT sent out five thousand phony résumés for job openings advertised in the
Boston Globe
and the
Chicago Tribune
. The jobs ranged from clerical work to customer service and sales. Each of the résumés was modeled for race. Half featured typically white names like Emily Walsh and Brendan Baker, while the others with similar qualifications carried names like Lakisha Washington and Jamaal Jones, which would sound African American. The researchers found that the white names got 50 percent more callbacks than the black ones. But a secondary finding was perhaps even more striking. The white applicants with strong résumés got much more attention than whites with weaker ones; when it came to white applicants, it seemed, the hiring managers were paying attention. But among blacks, the stronger résumés barely made a difference. The hiring market, clearly, was still poisoned by prejudice.

The ideal way to circumvent such prejudice is to consider applicants blindly. Orchestras, which had long been dominated by men, famously started in the 1970s to hold
auditions with the musician hidden behind a sheet. Connections and reputations suddenly counted for nothing. Nor did the musician’s race or alma mater. The music from behind the sheet spoke for itself. Since then, the percentage of women playing in major orchestras has leapt by a factor of five—though they still make up only a quarter of the musicians.

The trouble is that few professions can engineer such an even-handed tryout for job applicants. Musicians behind the sheet can actually perform the job they’re applying for, whether it’s a Dvorak cello concerto or bossa nova on guitar. In other professions, employers have to hunt through résumés, looking for qualities that might predict success.

As you might expect, human resources departments rely on
automatic systems to winnow down piles of résumés. In fact, some 72
percent of résumés are never seen by human eyes. Computer programs flip through them, pulling out the skills and experiences that the employer is looking for. Then they score each résumé as a match for the job opening. It’s up to the people in the human resources department to decide where the cutoff is, but the more candidates they can eliminate with this first screening, the fewer human-hours they’ll have to spend processing the top matches.

So job applicants must craft their résumés with that automatic reader in mind. It’s important, for example, to sprinkle the résumé liberally with
words the specific job opening is looking for. This could include positions (sales manager, chief financial officer, software architect), languages (Mandarin, Java), or honors (summa cum laude, Eagle Scout).

Those with the latest information learn what machines appreciate and what tangles them up. Images, for example, are useless. Most résumé scanners don’t yet process them. And fancy fonts do nothing but confuse the machines, says Mona Abdel-Halim. She’s the cofounder of Resunate.com, a job application tool. The safe ones, she says, are plain vanilla fonts, like Ariel and Courier. And forget about symbols such as arrows. They only confuse things, preventing the automatic systems from correctly parsing the information.

The result of these programs, much as with college admissions, is that those with the money and resources to prepare their résumés come out on top. Those who don’t take these steps may never know that they’re sending their résumés into a black hole. It’s one more example in which the wealthy and informed get the edge and the poor are more likely to lose out.

To be fair, the résumé business has always had one sort of bias or another. In previous generations, those in the know were careful to organize the résumé items clearly and consistently, type them
on a quality computer, like an IBM Selectric, and print them on paper with a high rag content. Such résumés were more likely to make it past human screeners. More times than not, handwritten résumés, or ones with smudges from mimeograph machines, ended up in the circular file. 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.

The unequal treatment at the hands of these gatekeepers extends far beyond résumés. Our livelihoods increasingly depend on our ability to make our case to machines. The clearest example of this is Google. For businesses, whether it’s a bed-and-breakfast or an auto repair shop, success hinges on showing up on the first page of search results. Now individuals face similar challenges, whether trying to get a foot in the door of a company, to climb the ranks—or even to survive waves of layoffs. The key is to learn what the machines are looking for. But here too, in a digital universe touted to be fair, scientific, and democratic, the insiders find a way to gain a crucial edge.

In the 1970s, the admissions office at
St. George’s Hospital Medical School, in the South London district of Tooting, saw an opportunity. They received more than twelve applications for each of their 150 openings each year. Combing through all those applications was a lot of work, requiring multiple screeners. And since each of those screeners had different ideas and predilections, the process was somewhat capricious. Would it be possible to program a computer to sort through the applications and reduce the field to a more manageable number?

Big organizations, like the Pentagon and IBM, were already using computers for such work. But for a medical school to come
up with its own automated assessment program in the late ’70s, just as Apple was releasing its first personal computer, represented a bold experiment.

It turned out, however, to be an utter failure. St. George was not only precocious in its use of mathematical modeling, it seemed, but also an unwitting pioneer in WMDs.

As with so many WMDs, the problem began at the get-go, when the administrators established the model’s twin objectives. The first was to boost efficiency, letting the machine handle much of the grunt work. It would automatically cull down the two thousand applications to five hundred, at which point humans would take over with a lengthy interviewing process. The second objective was fairness. The computer would remain unswayed by administrators’ moods or prejudices, or by urgent entreaties from lords or cabinet ministers. In this first automatic screening, each applicant would be judged by the same criteria.

And what would those criteria be? That looked like the easy part. St. George’s already had voluminous records of screenings from the previous years. 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.

In fairness to the administrators at St. George’s, not all of the discrimination in the training data was overtly racist. A good number of the applications with foreign names, or from foreign addresses, came from people who clearly had not mastered the English language. Instead of considering the possibility that great doctors could learn English, which is obvious today, the tendency was simply to reject them. (After all, the school had to discard
three-quarters of the applications, and that seemed like an easy place to start.)

Now, while the human beings at St. George’s had long tossed out applications littered with grammatical mistakes and misspellings, the computer—illiterate itself—could hardly follow suit. But it could correlate the rejected applications of the past with birthplaces and, to a lesser degree, surnames. So people from certain places, like Africa, Pakistan, and immigrant neighborhoods of the United Kingdom, received lower overall scores and were not invited to interviews. An outsized proportion of these people were nonwhite. The human beings had also rejected female applicants, with the all-too-common justification that their careers would likely be interrupted by the duties of motherhood. The machine, naturally, did the same.

In 1988, the British government’s Commission for Racial Equality found the medical school guilty of racial and gender discrimination in its admissions policy. As many as sixty of the two thousand applicants every year, according to the commission, may have been refused an interview purely because of their race, ethnicity, or gender.