An article in the Australian that is loosely related to XF1

Data science can’t fix hiring (yet)

• By PETER CAPPELLI
• 12:00AM JUNE 21, 2019
• NO COMMENTS

Recruiting managers desperately need new tools because the existing ones — unstructured interviews, personality tests, personal referrals — aren’t very effective. The newest development in hiring is the rise of data science — driven algorithms to find and assess job candidates. Unfortunately, data science — still in its infancy when it comes to recruiting and hiring — is not yet the panacea employers need.

Vendors of these new tools promise they will help reduce the role of social bias in hiring. And the algorithms can indeed help identify good job candidates who would previously have been screened out for lack of a certain educational or social pedigree. But these tools may also identify and promote the use of predictive variables that are (or should be) troubling.

Because most data scientists seem to know so little about the context of employment, their tools are often worse than nothing. For instance, an astonishing percentage build their models by simply looking at attributes of the “best performers” in workplaces and then identifying which candidates have the same attributes. They use anything that’s easy to measure: facial expressions, word choice, comments on social media, and so forth. But a failure to check for any real difference between high-performing and low-performing employees on these attributes limits their usefulness. Furthermore, scooping up data from social media or the websites people have visited raises important questions about privacy.

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• SIMON BENSON

Another problem with machine learning is that few employers collect the large volumes of data that the algorithms require to make accurate predictions. Vendors can theoretically overcome that by aggregating data from many employers, but they don’t know whether individual company contexts are so distinct that predictions based on data from the many are inaccurate for the one.

Yet another issue is that all analytic approaches to picking candidates are backward looking, being based on outcomes that have already happened. As Amazon learned, the past may be very different from the future you seek. It discovered that the hiring algorithm it had been working on since 2014 gave lower scores to women because historically its best performers had disproportionately been men. So the algorithm looked for people just like them. Amazon stopped using the algorithm in 2017. Nonetheless, many others are pressing ahead.

The underlying challenge for data scientists is that hiring is simply not like trying to predict, say, when a ball bearing will fail. Hiring is so consequential that it is governed not just by legal frameworks but by fundamental notions of fairness. Take a variable that data scientists have found to have predictive value: commuting distance to the job. According to the data, people with longer commutes suffer higher rates of attrition. However, commuting distance is governed by where you live — which is governed by housing prices, relates to income and also relates to race. Picking whom to hire on the basis of where they live most likely has an adverse impact on protected groups.

In the end, the drawback to algorithms is that we’re trying to use them on the cheap: building them by looking only at best performers rather than all performers, using only measures that are easy to gather, and relying on vendors’ claims that the algorithms work. Not only is there no free lunch here, but you might be better off skipping the cheap meal altogether.