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Bias can be thought of as a malleable and quickly-adapting entity. It is important to anticipate situations which are likely to lead to bias or have led to discrimination in the past, and create systems to eliminate or lessen the likelihood of these behaviors from occurring.

Another aspect of unconscious bias training should include the standardization of company policies, protocol, and procedures.

when unconscious bias training is implemented, it is imperative to have measures in place to assess incremental changes and progress. How will you then learn whether the training was successful if you don’t know what point you started at? Data should be collected at several stages of the training intervention, which can ensure the effectiveness of the training.

Bias can be thought of as a malleable and quickly-adapting entity. It is important to anticipate situations which are likely to lead to bias or have led to discrimination in the past, and create systems to eliminate or lessen the likelihood of these behaviors from occurring.

Another aspect of unconscious bias training should include the standardization of company policies, protocol, and procedures.

when unconscious bias training is implemented, it is imperative to have measures in place to assess incremental changes and progress.

In fact, studies demonstrate bias across nearly every field and for nearly every group of people. If you’re Latino, you’ll get less pain medication than a white patient. If you’re an elderly woman, you’ll receive fewer life-saving interventions than an elderly man. If you are a man being evaluated for a job as a lab manager, you will be given more mentorship, judged as more capable, and offered a higher starting salary than if you were a woman. If you are an obese child, your teacher is more likely to assume you’re less intelligent than if you were slim. If you are a black student, you are more likely to be punished than a white student behaving the same way.

Mike Pence, for instance, bristled during the 2016 vice-presidential debate: “Enough of this seeking every opportunity to demean law enforcement broadly by making the accusation of implicit bias whenever tragedy occurs.” And two days after the first presidential debate, in which Hillary Clinton proclaimed the need to address implicit bias, Donald Trump asserted that she was “essentially suggesting that everyone, including our police, are basically racist and prejudiced.”

Still other people, particularly those who have been the victims of police violence, also reject implicit bias—on the grounds that there’s nothing implicit about it at all.

Bias is woven through culture like a silver cord woven through cloth. In some lights, it’s brightly visible. In others, it’s hard to distinguish. And your position relative to that glinting thread determines whether you see it at all.

All of which is to say that while bias in the world is plainly evident, the exact sequence of mental events that cause it is still a roiling question.  Devine, for her part, told me that she is no longer comfortable even calling this phenomenon “implicit bias.” Instead, she prefers “unintentional bias.” The term implicit bias, she said, “has become so broad that it almost has no meaning.”

Weeks afterwards, students who had participated noticed bias more in others than did students who hadn’t participated, and they were more likely to label the bias they perceived as wrong. Notably, the impact seemed to last: Two years later, students who took part in a public forum on race were more likely to speak out against bias if they had participated in the training.

This hierarchy matters, because the more central a layer is to self-concept, the more resistant it is to change. It’s hard, for instance, to alter whether or not a person values the environment. But if you do manage to shift one of these central layers, Forscher explained, the effect is far-reaching.

And if there’s one thing the Madison workshops do truly shift, it is people’s concern that discrimination is a widespread and serious problem. As people become more concerned, the data show, their awareness of bias in the world grows, too.

Moreover, to the extent that people have unconscious biases, there is no clear-cut way to measure them

The main tool for measuring unconscious bias, the Implicit Association Test (IAT), has been in use for twenty years but is highly contested.

meta-analytic reviews have concluded that IAT scores — in other words, unconscious biases — are very weak predictors of actual behavior.

The vast majority of people labeled “racist” by these tests behave the same as the vast majority of people labelled “non-racist.” Do we really want to tell people who behave in non-racist ways that they are unconsciously racists, or, conversely, tell people who behave in racist ways that they aren’t, deep down, racists at all?

Instead of worrying what people think of something or someone deep down, we should focus on ways to eliminate the toxic or prejudiced behaviors we can see. That alone will drive a great deal of progress.

Scientific evidence suggests that the relationship between attitudes and behaviors is much weaker than one might expect.

Even if we lived in a world in which humans always acted in accordance with their beliefs, there would remain better ways to promote diversity than by policing people’s thoughts.

Organizations should focus less on extinguishing their employees’ unconscious thoughts, and more on nurturing ethical, benevolent, and inclusive behaviors.

This means focusing less on employees’ attitudes, and more on organizational policies and systems, as these play the key role creating the conditions that entice employees (and leaders) to behave in more or less inclusive ways.

This gets to the underlying flaw with unconscious bias trainings: behaviors, not thoughts, should be the target of diversity and inclusion interventions.

Tomas Chamorro-Premuzic is chief talent scientist at Manpower Group and a professor at University College London and Columbia University.

None of that is true. This week, Facebook rushed to deny a report in Gizmodo that said the team in charge of its “trending” news list routinely suppressed conservative points of view. Last month, Gizmodo also reported that Facebook employees asked Mark Zuckerberg, the social network’s chief executive, if the company had a responsibility to “help prevent President Trump in 2017.” Facebook denied it would ever try to manipulate elections.

Even if you believe that Facebook isn’t monkeying with the trending list or actively trying to swing the vote, the reports serve as timely reminders of the ever-increasing potential dangers of Facebook’s hold on the news.

The question isn’t whether Facebook has outsize power to shape the world — of course it does, and of course you should worry about that power. If it wanted to, Facebook could try to sway elections, favor certain policies, or just make you feel a certain way about the world, as it once proved it could do in an experiment devised to measure how emotions spread online.

There is no evidence Facebook is doing anything so alarming now. The danger is nevertheless real. The biggest worry is that Facebook doesn’t seem to recognize its own power, and doesn’t think of itself as a news organization with a well-developed sense of institutional ethics and responsibility, or even a potential for bias. Neither does its audience, which might believe that Facebook is immune to bias because it is run by computers.

That myth should die. It’s true that beyond the Trending box, most of the stories Facebook presents to you are selected by its algorithms, but those algorithms are as infused with bias as any other human editorial decision.

“With Facebook, humans are never not involved. Humans are in every step of the process — in terms of what we’re clicking on, who’s shifting the algorithms behind the scenes, what kind of user testing is being done, and the initial training data provided by humans.”Everything you see on Facebook is therefore the product of these people’s expertise and considered judgment, as well as their conscious and unconscious biases apart from possible malfeasance or potential corruption. It’s often hard to know which, because Facebook’s editorial sensibilities are secret. So are its personalities: Most of the engineers, designers and others who decide what people see on Facebook will remain forever unknown to its audience.

Facebook also has an unmistakable corporate ethos and point of view. The company is staffed mostly by wealthy coastal Americans who tend to support Democrats, and it is wholly controlled by a young billionaire who has expressed policy preferences that many people find objectionable.

You could argue that none of this is unusual. Many large media outlets are powerful, somewhat opaque, operated for profit, and controlled by wealthy people who aren’t shy about their policy agendas — Bloomberg News, The Washington Post, Fox News and The New York Times, to name a few.But there are some reasons to be even more wary of Facebook’s bias. One is institutional. Many mainstream outlets have a rigorous set of rules and norms about what’s acceptable and what’s not in the news business.

Those algorithms could have profound implications for society. For instance, one persistent worry about algorithmic-selected news is that it might reinforce people’s previously held points of view. If News Feed shows news that we’re each likely to Like, it could trap us into echo chambers and contribute to rising political polarization. In a study last year, Facebook’s scientists asserted the echo chamber effect was muted.

are Facebook’s engineering decisions subject to ethical review? Nobody knows.

The other reason to be wary of Facebook’s bias has to do with sheer size. Ms. Caplan notes that when studying bias in traditional media, scholars try to make comparisons across different news outlets. To determine if The Times is ignoring a certain story unfairly, look at competitors like The Washington Post and The Wall Street Journal. If those outlets are covering a story and The Times isn’t, there could be something amiss about the Times’s news judgment.Such comparative studies are nearly impossible for Facebook. Facebook is personalized, in that what you see on your News Feed is different from what I see on mine, so the only entity in a position to look for systemic bias across all of Facebook is Facebook itself. Even if you could determine the spread of stories across all of Facebook’s readers, what would you compare it to?

Patrica G. Devine, a social psychologist at the University of Wisconsin who studies unintended bias, argues that there has been little rigorous evaluation of the training strategies deployed to combat it, and as a result we simply don’t know enough about what makes a difference.

“It often has the feeling of being a one-and-done kind of thing: ‘We did it,’

“if people are hostile to the training, it’s like fingers being wagged at you, and if you are not at all open to that, it can fuel negativity to the point of backlash.”

The Covid crisis, in a sense, has provided a test case, and the results have been dispiriting. Between mid-March and early May, of the 125 people arrested for violations of social-distancing rules and other regulations related to the coronavirus, 113 were black or Hispanic

The problem with implicit bias work is that it too often fails to acknowledge the realities of instinctive distaste, the powerful emotions that animate the worst suppositions. It presumes a world better than the one we actually have.

Ms. Cooper’s transgression was not a mistaken perception or an insensitive statement.

The language — “African-American” — she seemed to have down. It was the deeper impulse for retaliation that she couldn’t suppress.

By the end of World War II, however, American corporations were facing severe talent shortages. Their senior executives were growing old, and a dearth of hiring from the Depression through the war had resulted in a shortfall of able, well-trained managers. Finding people who had the potential to rise quickly through the ranks became an overriding preoccupation of American businesses. They began to devise a formal hiring-and-management system based in part on new studies of human behavior, and in part on military techniques developed during both world wars, when huge mobilization efforts and mass casualties created the need to get the right people into the right roles as efficiently as possible. By the 1950s, it was not unusual for companies to spend days with young applicants for professional jobs, conducting a battery of tests, all with an eye toward corner-office potential.

But companies abandoned their hard-edged practices for another important reason: many of their methods of evaluation turned out not to be very scientific.

this regime, so widespread in corporate America at mid-century, had almost disappeared by 1990. “I think an HR person from the late 1970s would be stunned to see how casually companies hire now,”

Many factors explain the change, he said, and then he ticked off a number of them: Increased job-switching has made it less important and less economical for companies to test so thoroughly. A heightened focus on short-term financial results has led to deep cuts in corporate functions that bear fruit only in the long term. The Civil Rights Act of 1964, which exposed companies to legal liability for discriminatory hiring practices, has made HR departments wary of any broadly applied and clearly scored test that might later be shown to be systematically biased.

about a quarter of the country’s corporations were using similar tests to evaluate managers and junior executives, usually to assess whether they were ready for bigger roles.

He has encouraged the company’s HR executives to think about applying the games to the recruitment and evaluation of all professional workers.

Knack makes app-based video games, among them Dungeon Scrawl, a quest game requiring the player to navigate a maze and solve puzzles, and Wasabi Waiter, which involves delivering the right sushi to the right customer at an increasingly crowded happy hour. These games aren’t just for play: they’ve been designed by a team of neuroscientists, psychologists, and data scientists to suss out human potential. Play one of them for just 20 minutes, says Guy Halfteck, Knack’s founder, and you’ll generate several megabytes of data, exponentially more than what’s collected by the SAT or a personality test. How long you hesitate before taking every action, the sequence of actions you take, how you solve problems—all of these factors and many more are logged as you play, and then are used to analyze your creativity, your persistence, your capacity to learn quickly from mistakes, your ability to prioritize, and even your social intelligence and personality. The end result, Halfteck says, is a high-resolution portrait of your psyche and intellect, and an assessment of your potential as a leader or an innovator.

When the results came back, Haringa recalled, his heart began to beat a little faster. Without ever seeing the ideas, without meeting or interviewing the people who’d proposed them, without knowing their title or background or academic pedigree, Knack’s algorithm had identified the people whose ideas had panned out. The top 10 percent of the idea generators as predicted by Knack were in fact those who’d gone furthest in the process.

What Knack is doing, Haringa told me, “is almost like a paradigm shift.” It offers a way for his GameChanger unit to avoid wasting time on the 80 people out of 100—nearly all of whom look smart, well-trained, and plausible on paper—whose ideas just aren’t likely to work out.

Aptitude, skills, personal history, psychological stability, discretion, loyalty—companies at the time felt they had a need (and the right) to look into them all. That ambit is expanding once again, and this is undeniably unsettling. Should the ideas of scientists be dismissed because of the way they play a game? Should job candidates be ranked by what their Web habits say about them? Should the “data signature” of natural leaders play a role in promotion? These are all live questions today, and they prompt heavy concerns: that we will cede one of the most subtle and human of skills, the evaluation of the gifts and promise of other people, to machines; that the models will get it wrong; that some people will never get a shot in the new workforce.

scoring distance from work could violate equal-employment-opportunity standards. Marital status? Motherhood? Church membership? “Stuff like that,” Meyerle said, “we just don’t touch”—at least not in the U.S., where the legal environment is strict. Meyerle told me that Evolv has looked into these sorts of factors in its work for clients abroad, and that some of them produce “startling results.”

consider the alternative. A mountain of scholarly literature has shown that the intuitive way we now judge professional potential is rife with snap judgments and hidden biases, rooted in our upbringing or in deep neurological connections that doubtless served us well on the savanna but would seem to have less bearing on the world of work.

We may like to think that society has become more enlightened since those days, and in many ways it has, but our biases are mostly unconscious, and they can run surprisingly deep. Consider race. For a 2004 study called “Are Emily and Greg More Employable Than Lakisha and Jamal?,” the economists Sendhil Mullainathan and Marianne Bertrand put white-sounding names (Emily Walsh, Greg Baker) or black-sounding names (Lakisha Washington, Jamal Jones) on similar fictitious résumés, which they then sent out to a variety of companies in Boston and Chicago. To get the same number of callbacks, they learned, they needed to either send out half again as many résumés with black names as those with white names, or add eight extra years of relevant work experience to the résumés with black names.

a sociologist at Northwestern, spent parts of the three years from 2006 to 2008 interviewing professionals from elite investment banks, consultancies, and law firms about how they recruited, interviewed, and evaluated candidates, and concluded that among the most important factors driving their hiring recommendations were—wait for it—shared leisure interests.

Lacking “reliable predictors of future performance,” Rivera writes, “assessors purposefully used their own experiences as models of merit.” Former college athletes “typically prized participation in varsity sports above all other types of involvement.” People who’d majored in engineering gave engineers a leg up, believing they were better prepared.

the prevailing system of hiring and management in this country involves a level of dysfunction that should be inconceivable in an economy as sophisticated as ours. Recent survey data collected by the Corporate Executive Board, for example, indicate that nearly a quarter of all new hires leave their company within a year of their start date, and that hiring managers wish they’d never extended an offer to one out of every five members on their team

In the late 1990s, as these assessments shifted from paper to digital formats and proliferated, data scientists started doing massive tests of what makes for a successful customer-support technician or salesperson. This has unquestionably improved the quality of the workers at many firms.

In 2010, however, Xerox switched to an online evaluation that incorporates personality testing, cognitive-skill assessment, and multiple-choice questions about how the applicant would handle specific scenarios that he or she might encounter on the job. An algorithm behind the evaluation analyzes the responses, along with factual information gleaned from the candidate’s application, and spits out a color-coded rating: red (poor candidate), yellow (middling), or green (hire away). Those candidates who score best, I learned, tend to exhibit a creative but not overly inquisitive personality, and participate in at least one but not more than four social networks, among many other factors. (Previous experience, one of the few criteria that Xerox had explicitly screened for in the past, turns out to have no bearing on either productivity or retention

When Xerox started using the score in its hiring decisions, the quality of its hires immediately improved. The rate of attrition fell by 20 percent in the initial pilot period, and over time, the number of promotions rose. Xerox still interviews all candidates in person before deciding to hire them, Morse told me, but, she added, “We’re getting to the point where some of our hiring managers don’t even want to interview anymore”

Gone are the days, Ostberg told me, when, say, a small survey of college students would be used to predict the statistical validity of an evaluation tool. “We’ve got a data set of 347,000 actual employees who have gone through these different types of assessments or tools,” he told me, “and now we have performance-outcome data, and we can split those and slice and dice by industry and location.”

Evolv’s tests allow companies to capture data about everybody who applies for work, and everybody who gets hired—a complete data set from which sample bias, long a major vexation for industrial-organization psychologists, simply disappears. The sheer number of observations that this approach makes possible allows Evolv to say with precision which attributes matter more to the success of retail-sales workers (decisiveness, spatial orientation, persuasiveness) or customer-service personnel at call centers (rapport-building)

There are some data that Evolv simply won’t use, out of a concern that the information might lead to systematic bias against whole classes of people

the idea that hiring was a science fell out of favor. But now it’s coming back, thanks to new technologies and methods of analysis that are cheaper, faster, and much-wider-ranging than what we had before

what most excites him are the possibilities that arise from monitoring the entire life cycle of a worker at any given company.

Now the two companies are working together to marry pre-hire assessments to an increasing array of post-hire data: about not only performance and duration of service but also who trained the employees; who has managed them; whether they were promoted to a supervisory role, and how quickly; how they performed in that role; and why they eventually left.

What begins with an online screening test for entry-level workers ends with the transformation of nearly every aspect of hiring, performance assessment, and management.

I turned to Sandy Pentland, the director of the Human Dynamics Laboratory at MIT. In recent years, Pentland has pioneered the use of specialized electronic “badges” that transmit data about employees’ interactions as they go about their days. The badges capture all sorts of information about formal and informal conversations: their length; the tone of voice and gestures of the people involved; how much those people talk, listen, and interrupt; the degree to which they demonstrate empathy and extroversion; and more. Each badge generates about 100 data points a minute.

he tried the badges out on about 2,500 people, in 21 different organizations, and learned a number of interesting lessons. About a third of team performance, he discovered, can usually be predicted merely by the number of face-to-face exchanges among team members. (Too many is as much of a problem as too few.) Using data gathered by the badges, he was able to predict which teams would win a business-plan contest, and which workers would (rightly) say they’d had a “productive” or “creative” day. Not only that, but he claimed that his researchers had discovered the “data signature” of natural leaders, whom he called “charismatic connectors” and all of whom, he reported, circulate actively, give their time democratically to others, engage in brief but energetic conversations, and listen at least as much as they talk.

His group is developing apps to allow team members to view their own metrics more or less in real time, so that they can see, relative to the benchmarks of highly successful employees, whether they’re getting out of their offices enough, or listening enough, or spending enough time with people outside their own team.

Torrents of data are routinely collected by American companies and now sit on corporate servers, or in the cloud, awaiting analysis. Bloomberg reportedly logs every keystroke of every employee, along with their comings and goings in the office. The Las Vegas casino Harrah’s tracks the smiles of the card dealers and waitstaff on the floor (its analytics team has quantified the impact of smiling on customer satisfaction). E‑mail, of course, presents an especially rich vein to be mined for insights about our productivity, our treatment of co-workers, our willingness to collaborate or lend a hand, our patterns of written language, and what those patterns reveal about our intelligence, social skills, and behavior.

people analytics will ultimately have a vastly larger impact on the economy than the algorithms that now trade on Wall Street or figure out which ads to show us. He reminded me that we’ve witnessed this kind of transformation before in the history of management science. Near the turn of the 20th century, both Frederick Taylor and Henry Ford famously paced the factory floor with stopwatches, to improve worker efficiency.

“The quantities of data that those earlier generations were working with,” he said, “were infinitesimal compared to what’s available now. There’s been a real sea change in the past five years, where the quantities have just grown so large—petabytes, exabytes, zetta—that you start to be able to do things you never could before.”

People analytics will unquestionably provide many workers with more options and more power. Gild, for example, helps companies find undervalued software programmers, working indirectly to raise those people’s pay. Other companies are doing similar work. One called Entelo, for instance, specializes in using algorithms to identify potentially unhappy programmers who might be receptive to a phone cal

He sees it not only as a boon to a business’s productivity and overall health but also as an important new tool that individual employees can use for self-improvement: a sort of radically expanded The 7 Habits of Highly Effective People, custom-written for each of us, or at least each type of job, in the workforce.

the most exotic development in people analytics today is the creation of algorithms to assess the potential of all workers, across all companies, all the time.

The way Gild arrives at these scores is not simple. The company’s algorithms begin by scouring the Web for any and all open-source code, and for the coders who wrote it. They evaluate the code for its simplicity, elegance, documentation, and several other factors, including the frequency with which it’s been adopted by other programmers. For code that was written for paid projects, they look at completion times and other measures of productivity. Then they look at questions and answers on social forums such as Stack Overflow, a popular destination for programmers seeking advice on challenging projects. They consider how popular a given coder’s advice is, and how widely that advice ranges.

The algorithms go further still. They assess the way coders use language on social networks from LinkedIn to Twitter; the company has determined that certain phrases and words used in association with one another can distinguish expert programmers from less skilled ones. Gild knows these phrases and words are associated with good coding because it can correlate them with its evaluation of open-source code, and with the language and online behavior of programmers in good positions at prestigious companies.

having made those correlations, Gild can then score programmers who haven’t written open-source code at all, by analyzing the host of clues embedded in their online histories. They’re not all obvious, or easy to explain. Vivienne Ming, Gild’s chief scientist, told me that one solid predictor of strong coding is an affinity for a particular Japanese manga site.

Gild’s CEO, Sheeroy Desai, told me he believes his company’s approach can be applied to any occupation characterized by large, active online communities, where people post and cite individual work, ask and answer professional questions, and get feedback on projects. Graphic design is one field that the company is now looking at, and many scientific, technical, and engineering roles might also fit the bill. Regardless of their occupation, most people leave “data exhaust” in their wake, a kind of digital aura that can reveal a lot about a potential hire.

professionally relevant personality traits can be judged effectively merely by scanning Facebook feeds and photos. LinkedIn, of course, captures an enormous amount of professional data and network information, across just about every profession. A controversial start-up called Klout has made its mission the measurement and public scoring of people’s online social influence.

Mullainathan expressed amazement at how little most creative and professional workers (himself included) know about what makes them effective or ineffective in the office. Most of us can’t even say with any certainty how long we’ve spent gathering information for a given project, or our pattern of information-gathering, never mind know which parts of the pattern should be reinforced, and which jettisoned. As Mullainathan put it, we don’t know our own “production function.”

Over time, better job-matching technologies are likely to begin serving people directly, helping them see more clearly which jobs might suit them and which companies could use their skills. In the future, Gild plans to let programmers see their own profiles and take skills challenges to try to improve their scores. It intends to show them its estimates of their market value, too, and to recommend coursework that might allow them to raise their scores even more. Not least, it plans to make accessible the scores of typical hires at specific companies, so that software engineers can better see the profile they’d need to land a particular job

Knack, for its part, is making some of its video games available to anyone with a smartphone, so people can get a better sense of their strengths, and of the fields in which their strengths would be most valued. (Palo Alto High School recently adopted the games to help students assess careers.) Ultimately, the company hopes to act as matchmaker between a large network of people who play its games (or have ever played its games) and a widening roster of corporate clients, each with its own specific profile for any given type of job.

When I began my reporting for this story, I was worried that people analytics, if it worked at all, would only widen the divergent arcs of our professional lives, further gilding the path of the meritocratic elite from cradle to grave, and shutting out some workers more definitively. But I now believe the opposite is likely to happen, and that we’re headed toward a labor market that’s fairer to people at every stage of their careers

For decades, as we’ve assessed people’s potential in the professional workforce, the most important piece of data—the one that launches careers or keeps them grounded—has been educational background: typically, whether and where people went to college, and how they did there. Over the past couple of generations, colleges and universities have become the gatekeepers to a prosperous life. A degree has become a signal of intelligence and conscientiousness, one that grows stronger the more selective the school and the higher a student’s GPA, that is easily understood by employers, and that, until the advent of people analytics, was probably unrivaled in its predictive powers.

the limitations of that signal—the way it degrades with age, its overall imprecision, its many inherent biases, its extraordinary cost—are obvious. “Academic environments are artificial environments,” Laszlo Bock, Google’s senior vice president of people operations, told The New York Times in June. “People who succeed there are sort of finely trained, they’re conditioned to succeed in that environment,” which is often quite different from the workplace.

because one’s college history is such a crucial signal in our labor market, perfectly able people who simply couldn’t sit still in a classroom at the age of 16, or who didn’t have their act together at 18, or who chose not to go to graduate school at 22, routinely get left behind for good. That such early factors so profoundly affect career arcs and hiring decisions made two or three decades later is, on its face, absurd.

I spoke with managers at a lot of companies who are using advanced analytics to reevaluate and reshape their hiring, and nearly all of them told me that their research is leading them toward pools of candidates who didn’t attend college—for tech jobs, for high-end sales positions, for some managerial roles. In some limited cases, this is because their analytics revealed no benefit whatsoever to hiring people with college degrees; in other cases, and more often, it’s because they revealed signals that function far better than college history,

Google, too, is hiring a growing number of nongraduates. Many of the people I talked with reported that when it comes to high-paying and fast-track jobs, they’re reducing their preference for Ivy Leaguers and graduates of other highly selective schools.

This process is just beginning. Online courses are proliferating, and so are online markets that involve crowd-sourcing. Both arenas offer new opportunities for workers to build skills and showcase competence. Neither produces the kind of instantly recognizable signals of potential that a degree from a selective college, or a first job at a prestigious firm, might. That’s a problem for traditional hiring managers, because sifting through lots of small signals is so difficult and time-consuming.

all of these new developments raise philosophical questions. As professional performance becomes easier to measure and see, will we become slaves to our own status and potential, ever-focused on the metrics that tell us how and whether we are measuring up? Will too much knowledge about our limitations hinder achievement and stifle our dreams? All I can offer in response to these questions, ironically, is my own gut sense, which leads me to feel cautiously optimistic.

Google’s understanding of the promise of analytics is probably better than anybody else’s, and the company has been changing its hiring and management practices as a result of its ongoing analyses. (Brainteasers are no longer used in interviews, because they do not correlate with job success; GPA is not considered for anyone more than two years out of school, for the same reason—the list goes on.) But for all of Google’s technological enthusiasm, these same practices are still deeply human. A real, live person looks at every résumé the company receives. Hiring decisions are made by committee and are based in no small part on opinions formed during structured interviews.