The Formula_How Algorithms Solve All Our Problems... and Create More

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by Luke Dormehl


  Chicago’s Mattersight Corporation does exactly that. Based on custom algorithms, Mattersight calls its business “predictive behavioral routing.” By dividing both callers and agents into different personality types, it can make business both faster and more satisfactory to all involved. “Each individual customer has different expectations and behaviors,” Mattersight notes in promotional materials. “Similarly, each individual employee has different strengths and weaknesses handling different types of calls. As a result, the success of a given customer interaction is often determined by which employee handles that interaction and how well their competencies and behavioral characteristics align with each specific customer’s needs.”

  The man behind Mattersight’s behavioral models is a clinical psychologist named Dr. Taibi Kahler. Kahler is the creator of a type of psychological behavioral profiling called Process Communication. Back in the early 1970s, Kahler interned at a private psychiatric hospital. While he was there, he created something called a “Miniscript” based on his observations about patients in distress. The work wound up winning him the 1977 Eric Berne Memorial Scientific Award. What Kahler noticed was that certain predictable signs precede particular incidents of distress, and that these distress signs are linked to specific speech patterns. These, in turn, led to him developing profiles on the six different personality types he saw recurring. The personality types are as follows:

  Personality type

  Personality traits

  How common?

  “Thinkers”

  Thinkers view the world through data. Their primary way of dealing with situations is based upon logical analysis of a situation. They have the potential to become humorless and controlling.

  1 in 4 people

  “Rebels”

  Rebels interact with the world based on reactions. They either love things or hate them. Many innovators come from this group. Under pressure they can be negative and blameful.

  1 in 5 people

  “Persisters”

  Persisters filter everything through their opinions. Everything is measured up against their worldview. This describes the majority of politicians.

  1 in 10 people

  “Harmonizers”

  Harmonizers deal with everything in terms of emotions and relationships. Tight situations make this group overreactive.

  3 in 10 people

  “Promoters”

  Promoters view everything through action. These are the salesmen of the world, always looking to close a deal. They can be irrational and impulsive.

  1 in 20 people

  “Imaginers”

  Imaginers deal in unfocused thought and reflection. These people operate in vivid internal worlds and are likely to spot patterns where others cannot.

  1 in 10 people

  Although everyone has all six personality types to a greater or lesser degree, people will respond best to individuals who reflect their own primary personality type. If people’s communication needs are not met by being given the kind of positive “feedback” they require (a feelings-oriented person being asked cold hard facts, for example) they go into distress, which can be diffused only if the person on the other end of the conversation is able to adequately pick up on the warning signals and respond appropriately.

  In a call-center environment this knowledge results in an extraordinary qualitative change, according to Mattersight. A person patched through to an individual with a similar personality type to their own will have an average conversation length of five minutes, with a 92 percent problem-resolution rate. A caller paired up to a conflicting personality type, on the other hand, will see their call length double to ten minutes—while the problem-resolution rate tumbles to 47 percent.

  Process Communication isn’t only being used by Mattersight, however. In the past, Kahler has helped NASA develop algorithms to aid with the selection of its astronauts, since his model can accurately predict the personality types that won’t crack under the high-pressure atmosphere of space travel. (“Persisters”—who strive for perfection and encourage others to reach their peak performance—prove to be the best personality fit.) Kahler’s company, Kahler Communications, also has a number of ongoing projects designed to help organizations come up with data-driven and algorithmic solutions to questions related to personality.

  “From our perspective this is the key to diversity,” says Robert Wert, a former attorney who was employed as the COO of Kahler Communications when I had the opportunity to speak with him. “If all cultures are made up of the same building blocks, all of whom have the same type of interactions both positive and negative, then the real diversity is in personality type. It’s not in ethnicity, it’s not in gender, it’s not in anything else. I see this as the great equalizer. If you can walk into a room and immediately start speaking to someone who’s of a different background to you, and you can identify the same traits in them that you’ve dealt with for the rest of your life, that person is no longer the Other.”

  The Lake Wobegon Strategy

  Founded in 2011, Gild is a recruitment company that serves some of the tech industry’s biggest and best-known players. Currently focused on automating the discovery of talented programmers, Gild’s mission statement is to apply The Formula to the notoriously unreliable hiring process. To do this, the company uses algorithms to analyze individuals on tens of thousands (soon hundreds of thousands) of different metrics and data points—mining them for insights in what Gild refers to as “broad predictive modeling.”

  The success stories the company trots out are impressive. A typical one tells of 26-year-old college dropout Jade Dominguez, who lived off an increasing line of credit-card debt in South Pasadena, California, while teaching himself computer programming.14 After being “discovered” by Gild’s algorithm, he now works as a programmer at the company that found him. His story is hardly unique, either. “These are people whose CVs you wouldn’t look twice at, but who our algorithm predicts would be perfect for the job,” says Vivienne Ming, Gild’s chief scientist. “For some of our customers, that is exactly what they’re looking for. These are companies that are flooded with résumés. They don’t need us to find people; they need us to find different people.”

  The first time I spoke with Ming, it was May 2013, and she was sitting in the back of a taxicab on her way to San Francisco International Airport. A tall, striking woman with silver-blue eyes and strawberry-blond hair, Ming is a theoretical neuroscientist with a Carnegie Mellon University pedigree. Effortlessly assured, her geeky engineering side is evidenced by the fact that she wears a prerelease Google Glass headset. In addition to her neuroscience background, Ming’s Twitter profile describes her as an “intrepid entrepreneur, undesirable superhero [and] very sleepy mother.”

  Ming is deeply invested in Gild’s utopian vision of turning the workplace into the kind of meritocracy she believes it should be. “This is the way things ought to work, right?” she says, rhetorically. “The person making the hiring decisions really should have an accurate picture of who I am—not just a snap judgment made because I look a certain way. But believe me, the way that people look is a huge influence on hiring.”

  If there is a reason why the idea of people being misjudged on first appraisal hits home particularly hard with Ming it may have something to do with her background. Born Evan Campbell Smith, Ming underwent gender reassignment surgery in 2008, having “ghosted [her] way through life” up until that point. “I was not a classically good student,” she explains. “I frequently failed my classes,
I was constantly in trouble at school. I was not engaged, but I deeply cared about the learning experience. The most trouble I ever got in was lying to stay in an honors chemistry course. I loved being there. I loved learning.” After Ming underwent the operation to become a woman, she noticed that she was treated differently: being asked fewer questions about math than she had as a man, and not being invited to so many social events by male colleagues and business connections.

  To Ming, there exist two main problems with classic hiring strategies. The first is that they are inherently biased. While the majority of people appreciate the value of recruiting people with a different background from themselves, they are often not exposed to these individuals in social settings. Why, she asks, is a typical start-up composed of similar-looking individuals of approximately the same age, with the same scruffy engineering look? Because they hired people they knew. The person who is good friends with a white, male, upper-middle-class, hardworking engineer is statistically more likely to be a hardworking engineer themselves. They are also likely to be white, male and upper middle class. As data-driven algorithmic culture has taken over, these casual assumptions have in many cases become codified. To get a job at Facebook, one of the initial tests used in the weeding-out process is to find a person already working for Facebook who knows you. This is the same idea as LinkedIn, whose algorithms search for connections between an individual and the person they are trying to meet. Although the idea is certainly neat on one level, it can also have the unfortunate effect of excluding a significant number of people from diverse regional, social and cultural backgrounds.

  The other problem that Ming explains (and to a scientist this is almost certainly worse) is that previous hiring strategies have proven inaccurate when it comes to forecasting who will succeed in a workplace role. In a place like Silicon Valley, where the supposed objectivity of data-driven hiring is prized above all else, this is particularly unforgivable. Google, for example, employs what it calls the Lake Wobegon Strategy for hiring—named after American humorist Garrison Keillor’s claim that he grew up on the fictitious Lake Wobegon, where “all the women are strong, all the men are good-looking, and all the children are above average.” According to Google’s Lake Wobegon Strategy, to maintain a high level of skill in an organization that is doubling in size each year, new employees should be above the mean skill level of current Googlers in order to be hired. To measure something as unquantifiable as “skill,” Google traditionally placed a strong emphasis on academic results. A person’s GPA and university were considered a strong predictor of workplace success, since they showed past evidence of rigor, stickability and the ability to meet deadlines. An individual who studied computer science at MIT might not be the best computer scientist in the world, but it is surely safe to assume that they are at least “good enough” to have gotten into the course in the first place. Pick a random person who didn’t go to MIT, on the other hand, and while there is still the chance that they will be brilliant, the likelihood that they will be terrible is far higher. In a risk-averse industry where people are rarely given a bonus for betting on the long shot that pays off—but could very easily lose their job for hiring a person deemed unsuitable for a particular role—it is no wonder that many high-tech companies would choose to play it safe.

  As datasets piled up, however, and algorithms began scouring that information for patterns, Google realized that the metrics they were using to predict job performance (including school grades, exam results, previous job experience and even face-to-face interviews) offered very little in the way of accuracy when forecasting who it was that was likely to excel in a particular position. This in itself was not an entirely new revelation. In the 1960s, telecommunications giant AT&T conducted IQ tests on low-level managers and then followed them for the next 20 years of their career to see how each employee progressed within the organization. In line with popular wisdom, AT&T’s assumption was that those individuals with a higher IQ would rise to the highest level, while those with lower IQs would settle lower down in the company, like water that finds its own level. Instead, what was discovered was that IQ scores explained less than 15 percent of the variance between managers in terms of career achievement. The rest was an unmeasurable combination of personality traits, emotional attributes, sociability and a number of other characteristics that can determine success.

  Once Google realized the need to open up the parameters of what it looked for in a new employee, the cultural space was cleared for Gild. Instead of hiring from a population of tens of thousands each year, high-tech companies now have a population consisting of almost everybody to choose from. After all, it is never knowable where the diamond in the rough might pop up. To hedge bets, a prospective employee being looked over by Gild is measured on practically every scrap of information in existence about them. Vivienne Ming likens the difference between this approach and that of a human recruiter to a human chess player competing against Deep Blue—the supercomputer that famously defeated grand master Garry Kasparov in what Newsweek described as “The Brain’s Last Stand.” “The computer is plotting every possible move and choosing the optimal move,” she says. “Chess experts are not. They have implicitly discarded the vast majority of possible moves and are only considering two, three, four possibilities. They just happen to be great ones.” That’s also true of people making hiring decisions, Ming suggests—only that the metrics considered in this case don’t happen to be so great. By looking at as many data points as possible about a person, anomalous factors like whether a person being interviewed was having an off day are bypassed. Gild additionally looks at where individuals spend time online, since this has been shown to be a strong predictor of workplace skills. “If you spend a lot of time blogging it suggests that you’re not quite as good a programmer as someone who spends their time on Quora,” Ming says, referring to the question-and-answer website founded by two former Facebook employees. Even Twitter feeds are mined for their insights, using semantic and sentiment analysis. At the end, factors are combined to give prospective employees a “Gild Score” out of 100.

  “It’s very cool if you’re geeky about algorithms, but the really important take-away is that what we end up with is truly independent dimensions for describing people out in the world,” she says. “We’re talking about algorithms whose entire intent and purpose is to aggregate across your entire life to build up a very accurate representation of who you are.”

  Quantifying Human Potential

  Gild is not the only interested party looking to open up the number of metrics individuals are judged on in the workplace. In 2012, three universities carried out a study as to whether or not Facebook profiles can be used to predict how successful a person is likely to be at their job. By analyzing photos, wall posts, comments and profiles, researchers argued that questions such as “Is this person dependable?” and “How emotionally stable is this person?” can be answered with a high level of certainty. Favorable evaluations were given to those students who had traveled, had more friends and demonstrated a wide range of hobbies and interests. Partying photos didn’t necessarily count as negative either, since people depicted as partiers were characterized as extroverted and friendly, both viewed as ideal qualities for workplace success. Six months after making their initial predictions, the study’s authors followed up with the employers of their 56 test subjects and found a strong correlation between job performance and the Facebook scores that had been awarded for traits such as conscientiousness, agreeability and intellectual curiosity. Their conclusion was that Facebook profiles are strong predictors since candidates will have a harder time “faking . . . their personalities” on a social network than they would in a conventional job interview.15

  This attitude toward quantification and statistical analysis is one that is heard regularly from exponents of The Formula. It owes its biggest debt to the Belfast-born mathematical physicist and engineer Lord Kelvin, who suggested that the thing that cannot be me
asured cannot possibly be improved. In the second half of the 19th century, a cousin of Charles Darwin named Francis Galton seized upon Kelvin’s suggestion as the basis for a number of unusual studies designed to measure the unmeasurable. For example, infuriated by the vagueness associated with a term like “beauty,” Galton set out to create a “beauty map” of the British Isles, whereby each woman he came into contact with was classified as either “attractive, indifferent, or repellent.” London, he claimed, had the highest concentration of beautiful women, while Aberdeen was mathematically proven to be home to the ugliest.16 Another study saw him measure listlessness by constructing a unified “Measure of Fidget,” as Galton felt the “mutiny of constraint” epitomized in a fidget lent “numerical expression to the amount of boredom expressed by [an] audience.” The more fidgeting, the higher the levels of boredom.17 Even God wasn’t safe from quantification, since Galton saw no reason that the “efficacy of prayer” (the rate at which prayers were answered versus ignored) should not be a “perfectly appropriate and legitimate subject of scientific inquiry.”18

  It is into this quantified space that Silicon Valley start-up Knack enters the picture. Founded by Israeli entrepreneur Guy Halfteck, Knack has a deceptively simple aim: to use a combination of gaming technology, machine-learning algorithms and the latest findings from behavioral science to come up with universal measures for terms like “quick-thinking,” “perceptiveness,” “empathy,” “insightfulness,” “spontaneity” and “creativity.” By doing this, Halfteck says that he hopes to trigger a “fundamental change in the human capital space” that will seek to unlock an individual’s previously untapped potential.

 

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