by Cathy O'Neil
Following World War II, major companies (as well as the Pentagon) poured enormous resources into OR. The science of logistics radically transformed the way we produce goods and bring them to market.
In the 1960s, Japanese auto companies made another major leap, devising a manufacturing system called Just in Time. The idea was that instead of storing mountains of steering wheels or transmission blocks and retrieving them from vast warehouses, the assembly plant would order parts as they were needed rather than paying for them to sit idle. Toyota and Honda established complex chains of suppliers, each of them constantly bringing in parts on call. It was as if the industry were a single organism, with its own homeostatic control systems.
Just in Time was highly efficient, and it quickly spread across the globe. Companies in many geographies can establish just-in-time supply chains in a snap. These models likewise constitute the mathematical underpinnings of companies like Amazon, Federal Express, and UPS.
Scheduling software can be seen as an extension of the just-in-time economy. But instead of lawn mower blades or cell phone screens showing up right on cue, it’s people, usually people who badly need money. And because they need money so desperately, the companies can bend their lives to the dictates of a mathematical model.
I should add that companies take steps not to make people’s lives too miserable. They all know to the penny how much it costs to replace a frazzled worker who finally quits. Those numbers are in the data, too. And they have other models, as we discussed in the last chapter, to reduce churn, which drains profits and efficiency.
The trouble, from the employees’ perspective, is an oversupply of low-wage labor. People are hungry for work, which is why so many of them cling to jobs that pay barely eight dollars per hour. This oversupply, along with the scarcity of effective unions, leaves workers with practically no bargaining power. This means the big retailers and restaurants can twist the workers’ lives to ever-more-absurd schedules without suffering from excessive churn. They make more money while their workers’ lives grow hellish. And because these optimization programs are everywhere, the workers know all too well that changing jobs isn’t likely to improve their lot. Taken together, these dynamics provide corporations with something close to a captive workforce.
I’m sure it comes as no surprise that I consider scheduling software one of the more appalling WMDs. It’s massive, as we’ve discussed, and it takes advantage of people who are already struggling to make ends meet. What’s more, it is entirely opaque. Workers often don’t have a clue about when they’ll be called to work. They are summoned by an arbitrary program.
Scheduling software also creates a poisonous feedback loop. Consider Jannette Navarro. Her haphazard scheduling made it impossible for her to return to school, which dampened her employment prospects and kept her in the oversupplied pool of low-wage workers. The long and irregular hours also make it hard for workers to organize or to protest for better conditions. Instead, they face heightened anxiety and sleep deprivation, which causes dramatic mood swings and is responsible for an estimated 13 percent of highway deaths. Worse yet, since the software is designed to save companies money, it often limits workers’ hours to fewer than thirty per week, so that they are not eligible for company health insurance. And with their chaotic schedules, most find it impossible to make time for a second job. It’s almost as if the software were designed expressly to punish low-wage workers and to keep them down.
The software also condemns a large percentage of our children to grow up without routines. They experience their mother bleary eyed at breakfast, or hurrying out the door without dinner, or arguing with her mother about who can take care of them on Sunday morning. This chaotic life affects children deeply. According to a study by the Economic Policy Institute, an advocacy group, “Young children and adolescents of parents working unpredictable schedules or outside standard daytime working hours are more likely to have inferior cognition and behavioral outcomes.” The parents might blame themselves for having a child who acts out or fails in school, but in many cases the real culprit is the poverty that leads workers to take jobs with haphazard schedules—and the scheduling models that squeeze struggling families even harder.
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.” This is, of course, the nature of capitalism. For companies, revenue is like oxygen. It keeps them alive. From their perspective, it would be profoundly stupid, even unnatural, to turn away from potential savings. That’s why society needs countervailing forces, such as vigorous press coverage that highlights the abuses of efficiency and shames companies into doing the right thing. And when they come up short, as Starbucks did, it must expose them again and again. It also needs regulators to keep them in line, strong unions to organize workers and amplify their needs and complaints, and politicians willing to pass laws to restrain corporations’ worst excesses. Following the New York Times report in 2014, Democrats in Congress promptly drew up bills to rein in scheduling software. But facing a Republican majority fiercely opposed to government regulations, the chances that their bill would become law were nil. The legislation died.
In 2008, just as the great recession was approaching, a San Francisco company called Cataphora marketed a software system that rated tech workers on a number of metrics, including their generation of ideas. This was no easy task. Software programs, after all, are hard-pressed to distinguish between an idea and a simple string of words. If you think about it, the difference is often just a matter of context. Yesterday’s ideas—that the earth is round, or even that people might like to share photos in social networks—are today’s facts. We humans each have a sense for when an idea becomes an established fact and know when it has been debunked or discarded (though we often disagree). However, that distinction flummoxes even the most sophisticated AI. So Cataphora’s system needed to look to humans themselves for guidance.
Cataphora’s software burrowed into corporate e-mail and mes saging in its hunt for ideas. Its guiding hypothesis was that the best ideas would tend to spread more widely through the network. If people cut and pasted certain groups of words and shared them, those words were likely ideas, and the software could quantify them.
But there were complications. Ideas were not the only groups of words that were widely shared on social networks. Jokes, for example, were wildly viral and equally befuddling to software systems. Gossip also traveled like a rocket. However, jokes and gossip followed certain patterns, so it was possible to teach the program to filter out at least some of them. With time, the system identified the groups of words most likely to represent ideas. It tracked them through the network, counting the number of times they were copied, measuring their distribution, and identifying their source.
Very soon, the roles of the employees appeared to come into focus. Some people were idea generators, the system concluded. On its chart of employees, Cataphora marked idea generators with circles, which were bigger and darker if they produced lots of ideas. Other people were connectors. Like neurons in a distributed network, they transmitted information. The most effective connectors made snippets of words go viral. The system painted those people in dark colors as well.
Now, whether or not this system effectively measured the flow of ideas, the concept itself was not nefarious. It can make sense to use this type of analysis to identify what people know and to match them with their most promising colleagues and collaborators. IBM and Microsoft use in-house programs to do just this. It’s very similar to a dating algorithm (and often, no doubt, has similarly spotty results). Big Data has also been used to study the productivity of call center workers.
A few years ago, MIT researchers analyzed the behavior of call center employees for Bank of America to find out why some teams were more productive than others. They hung a so-called sociometric badge around each employee’s neck. The electronics in these ba
dges tracked the employees’ location and also measured, every sixteen milliseconds, their tone of voice and gestures. It recorded when people were looking at each other and how much each person talked, listened, and interrupted. Four teams of call center employees—eighty people in total—wore these badges for six weeks.
These employees’ jobs were highly regimented. Talking was discouraged because workers were supposed to spend as many of their minutes as possible on the phone, solving customers’ problems. Coffee breaks were scheduled one by one.
The researchers found, to their surprise, that the fastest and most efficient call center team was also the most social. These employees pooh-poohed the rules and gabbed much more than the others. And when all of the employees were encouraged to socialize more, call center productivity soared.
But data studies that track employees’ behavior can also be used to cull a workforce. As the 2008 recession ripped through the economy, HR officials in the tech sector started to look at those Cataphora charts with a new purpose. They saw that some workers were represented as big dark circles, while others were smaller and dimmer. If they had to lay off workers, and most companies did, it made sense to start with the small and dim ones on the chart.
Were those workers really expendable? Again we come to digital phrenology. If a system designates a worker as a low idea generator or weak connector, that verdict becomes its own truth. That’s her score.
Perhaps someone can come in with countervailing evidence. The worker with the dim circle might generate fabulous ideas but not share them on the network. Or perhaps she proffers price less advice over lunch or breaks up the tension in the office with a joke. Maybe everybody likes her. That has great value in the workplace. But computing systems have trouble finding digital proxies for these kinds of soft skills. The relevant data simply isn’t collected, and anyway it’s hard to put a value on them. They’re usually easier to leave out of a model.
So the system identifies apparent losers. And a good number of them lost their jobs during the recession. That alone is unjust. But what’s worse is that systems like Cataphora’s receive minimal feedback data. Someone identified as a loser, and subsequently fired, may have found another job and generated a fistful of patents. That data usually isn’t collected. The system has no inkling that it got one person, or even a thousand people, entirely wrong.
That’s a problem, because scientists need this error feedback—in this case the presence of false negatives—to delve into forensic analysis and figure out what went wrong, what was misread, what data was ignored. It’s how systems learn and get smarter. Yet as we’ve seen, loads of WMDs, from recidivism models to teacher scores, blithely generate their own reality. Managers assume that the scores are true enough to be useful, and the algorithm makes tough decisions easy. They can fire employees and cut costs and blame their decisions on an objective number, whether it’s accurate or not.
Cataphora remained small, and its worker evaluation model was a sideline—much more of its work was in identifying patterns of fraud or insider trading within companies. The company went out of business in 2012, and its software was sold to a start-up, Chenope. But systems like Cataphora’s have the potential to become true WMDs. They can misinterpret people, and punish them, without any proof that their scores correlate to the quality of their work.
This type of software signals the rise of WMDs in a new realm. For a few decades, it may have seemed that industrial workers and service workers were the only ones who could be modeled and optimized, while those who trafficked in ideas, from lawyers to chemical engineers, could steer clear of WMDs, at least at work. Cataphora was an early warning that this will not be the case. Indeed, throughout the tech industry, many companies are busy trying to optimize their white-collar workers by looking at the patterns of their communications. The tech giants, including Google, Facebook, Amazon, IBM, and many others, are hot on this trail.
For now, at least, this diversity is welcome. It holds out the hope, at least, that workers rejected by one model might be appreciated by another. But eventually, an industry standard will emerge, and then we’ll all be in trouble.
In 1983, the Reagan administration issued a lurid alarm about the state of America’s schools. In a report called A Nation at Risk, a presidential panel warned that a “rising tide of mediocrity” in the schools threatened “our very future as a Nation and a people.” The report added that if “an unfriendly foreign power” had attempted to impose these bad schools on us, “we might well have viewed it as an act of war.”
The most noteworthy signal of failure was what appeared to be plummeting scores on the SATs. Between 1963 and 1980, verbal scores had fallen by 50 points, and math scores were down 40 points. Our ability to compete in a global economy hinged on our skills, and they seemed to be worsening.
Who was to blame for this sorry state of affairs? The report left no doubt about that. Teachers. The Nation at Risk report called for action, which meant testing the students—and using the results to zero in on the underperforming teachers. As we saw in the Introduction, this practice can cost teachers their jobs. Sarah Wysocki, the teacher in Washington who was fired after her class posted surprisingly low scores, was the victim of such a test. My point in telling that story was to show a WMD in action, how it can be arbitrary, unfair, and deaf to appeals.
But along with being educators and caretakers of children, teachers are obviously workers, and here I want to delve a bit deeper into the models that score their performance, because they might spread to other parts of the workforce. Consider the case of Tim Clifford. He’s a middle school English teacher in New York City, with twenty-six years of experience. A few years ago, Clifford learned that he had bombed on a teacher evaluation, a so-called value-added model, similar to the one that led to Sarah Wysocki’s firing. Clifford’s score was an abysmal 6 out of 100.
He was devastated. “I didn’t see how it was possible that I could have worked so hard and gotten such poor results,” he later told me. “To be honest, when I first learned my low score, I felt ashamed and didn’t tell anyone for a day or so. However, I learned that there were actually two other teachers who scored below me in my school. That emboldened me to share my results, because I wanted those teachers to know it wasn’t only them.”
If Clifford hadn’t had tenure, he could have been dismissed that year, he said. “Even with tenure,” he said, “scoring low in consecutive years is bound to put a target on a teacher’s back to some degree.” What’s more, when tenured teachers register low scores, it emboldens school reformers, who make the case that job security protects incompetent educators. Clifford approached the following year with trepidation.
The value-added model had given him a failing grade but no advice on how to improve it. So Clifford went on teaching the way he always had and hoped for the best. The following year, his score was a 96.
“You’d think I’d have been elated, but I wasn’t,” he said. “I knew that my low score was bogus, so I could hardly rejoice at getting a high score using the same flawed formula. The 90 percent difference in scores only made me realize how ridiculous the entire value-added model is when it comes to education.”
Bogus is the word for it. In fact, misinterpreted statistics run through the history of teacher evaluation. The problem started with a momentous statistical boo-boo in the analysis of the original Nation at Risk report. It turned out that the very researchers who were decrying a national catastrophe were basing their judgment on a fundamental error, something an undergrad should have caught. In fact, if they wanted to serve up an example of America’s educational shortcomings, their own misreading of statistics could serve as exhibit A.
Seven years after A Nation at Risk was published with such fanfare, researchers at Sandia National Laboratories took a second look at the data gathered for the report. These people were no amateurs when it came to statistics—they build and maintain nuclear weapons—and they quickly found the error. Yes, it was true that SAT
scores had gone down on average. However, the number of students taking the test had ballooned over the course of those seventeen years. Universities were opening their doors to more poor students and minorities. Opportunities were expanding. This signaled social success. But naturally, this influx of newcomers dragged down the average scores. However, when statisticians broke down the population into income groups, scores for every single group were rising, from the poor to the rich.
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. The damning conclusion in the Nation at Risk report, the one that spurred the entire teacher evaluation movement, was drawn from a grievous misinterpretation of the data.
Tim Clifford’s diverging scores are the result of yet another case of botched statistics, this one all too common. The teacher scores derived from the tests measured nothing. This may sound like hyperbole. After all, kids took tests, and those scores contributed to Clifford’s. That much is true. But Clifford’s scores, both his humiliating 6 and his chest-thumping 96, were based almost entirely on approximations that were so weak they were essentially random.
The problem was that the administrators lost track of accuracy in their quest to be fair. They understood that it wasn’t right for teachers in rich schools to get too much credit when the sons and daughters of doctors and lawyers marched off toward elite universities. Nor should teachers in poor districts be held to the same standards of achievement. We cannot expect them to perform miracles.
