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“Data analytics, data access, and data quality have all improved to the point that we may be getting close to a moment when computers really will be able to make many hiring decisions better than a human…”

For decades now, science fiction writers have imagined computers that could make foolproof estimates of people’s natural abilities and then hire them based on those results. In these fictional scenarios, complications generally ensue as society’s less fortunates discover the hopelessness of their situation, and rebel against it. 

The HBO TV show Westworld is a good example. Set in the near future, the company Incite Inc. has full access to everyone’s personal data and with it charts their lives, including employment. Mayhem ensues after a data leak, when it is made clear to much of the population that they will never be selected for any position of note; that the outcome of their lives – that was previously only predicated – is now determined.

Today, such scenarios are becoming less far-fetched all the time. Data analytics, data access, and data quality have all improved to the point that we may be getting close to the moment when computers really will be able to make many hiring decisions better than a human, based on a data-driven forecast that can predict how well-suited candidates are for a job, how much they are likely to enjoy it, how good they will be at it, and how long they will stick with it.

Changes are already happening quickly: the 2020 Deloitte Global Human Capital Trends report found that 38 per cent of companies were incorporating artificial intelligence (AI) into their talent acquisition process, up from 7 per cent the year before. Whether they are getting much for their money yet is debatable. Still, this increasing usage suggests that a substantial percentage of businesses are now convinced that automation will play a meaningful role in the recruitment practices of the future.

What happens next? So far, concerns raised seemed to have been limited to privacy advocates and psychologists specialized in applicant selection speaking to niche audiences, but we think there is a real risk that just as in those old sci-fi stories, things may not go as smoothly as many HR leaders expect.

Dr. C.I.S.G. (Colin) Lee
Assistant Professor
Rotterdam School of Management (RSM)
Erasmus University Rotterdam
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Colin Lee

Prof. dr. P. (Piers) Steel

Professor of Organisational Behaviour and Human Resource Management

Department of Human Resources and Organizational Dynamics

Haskayne School of Business, University of Calgary

People writing on paper

Theoretically, some scholars have long believed that if you had a large enough data set and a powerful enough computer, it would be possible to create an algorithm that could digest enough historical data to make highly educated guesses about the performance of a given job candidate. But synthetic validity, as this idea was dubbed in 1957, became feasible only recently, as the amount of available data expanded and the power of computers to digest information grew. Now, there is very little holding back the prospect of personnel matchmaking on a global scale, as we have massive amounts of both occupational and personal data available. LinkedIn has the occupational histories of more than one billion people, while other social media platforms know vast amounts about people’s personal lives and preferences. 

Such information can reveal a surprising amount about an individual. By digesting social media posts and other digital traces, researchers have shown they can generate personality profiles as astute as those that could be given by close friends. They have also turned up unexpected results. (In 2020, for instance, one team of scholars discovered a correlation on Facebook between intelligence and liking curly fries.) 

By putting together profiles that combine professional and social details, occupational matchmakers (labour market intermediaries – LMIs) could have many positive effects on the labour markets. They could help encourage people to train for work they would be good at, give potential employers assurances that they were getting the right person for a job, and reduce discrimination against people who don’t fit a profession’s stereotype but do have the right skills and temperament. They could make it easier for employers to find people with the right talent and temperament who live near where the job needs to be. 

This could be very good for businesses and for the broader economy. Over the past five years, we have developed a detailed simulation focused on the United States, essentially reconstructing the entire labour market to analyse the effects of improved job matching using techniques that are immediately available. Findings in our working manuscript, not yet published, underscore the critical role of human capital. Depending on the model's assumptions and the job-matching system employed, we observed a GDP increase ranging from 3.7 per cent to 9.5 per cent. This translates to gains between 517 and 1,319 billion US dollars, attributable to increased productivity. Worldwide, the effect would be even larger.

“…there is very little holding back the prospect of personnel matchmaking on a global scale, as we have massive amounts of both occupational and personal data available.”

A dark side

However, as in many good sci-fi stories, the helpful LMIs could have a dark side. To begin with, the bulk of workers may not benefit economically from that growth in productivity. Increased international competition could push down wages in higher-wage regions because employers may be less willing to pay for employees who live in places with a high cost of living. LMIs might also encourage more commodification of the workforce, leading to an even bigger gig economy, with a greater risk of political destabilisation, and disenfranchisement. 

Sandra Roest, Manager HR Data & Analytics for the Dutch mail service PostNL and an alumna of Erasmus University Rotterdam, argues that such matchmaking could also miss the mark in important ways, such as by overlooking the varied motivations people have for taking jobs. The LMI would need to ask not just whether candidates would be good at a job but whether the opportunity fits in with the other current priorities of the candidate’s life.

Roest notes that there are a variety of reasons a job might be a great match for someone. The appeal of a job might not even be in the nature of the work itself. Take mail delivery: it could be incredibly rewarding to you because you like the outdoors and prefer a dynamic workday away from a desk. But you might also be eager to do this job because you like engaging with your community or because the job aligns well with your personal life and allows you to pick up your kids from school.  

Moreover, she says, it’s not a static equation. As people progress through different stages of life, their needs and interests in the workplace can shift, leading to changes in what they consider an ideal job. A position that is compatible with family life might be important at one point in time, but less so at another.

But the most serious societal issue will be what to do with people for whom the LMI finds there isn’t any work that matches their personality, capacity, and skills. What might it mean to have populations who feel they have been permanently swiped left by employers everywhere?

Waiting for the LMI

The LMIs are coming soon – the opportunity for improvement of HR management is too big and their economic potential too vast to hold off much longer – but before they arrive, we hope regulators, HR professionals, and managers reflect on the many ways in which they might change labour, and what needs to be done to make sure most of those changes are positive. You’ll be glad they did, Worker 60609.

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