Those Pesky Correlations

Last week, the ABA held its Section of Labor and Employment Law National Symposium on Technology in Labor and Employment Law.  This is one of my favorite conferences, because I get to geek out with some of the most forward-thinking employment lawyers in the country.  This year was no different.

This year, we had two separate sessions on people analytics.  Analytics has been a hot topic for HR for the last five years (at least), and many employment attorneys are trying to play catch-up.  Using algorithms, artificial intelligence, and at times machine learning, analytics crunches data (employer, employee, social media, and/or public data) to find correlations that assist employers in making decisions.  These decisions can range from finding the best candidate to unmasking the employee stealing trade secrets.  Analytics are really, really cool!  But beware, problems exist.

A big one is discrimination.  We’ve known that analytics can be discriminatory for a while now.  We’ve seen discriminatory results in analytics in the justice system, advertising, and many, many others.  Because we’ve seen discrimination elsewhere, it could happen in people analytics too.  If it does, how will the law handle it?  Will a judge review an analytics case like she would a neutral policy that had a discriminatory result when used?  Or will a judge review an employment decision on an individual basis?  As people analytics develops, employment lawyers are finding themselves divided on how the law will deal with analytics that result in discrimination.

On one side, there are data scientists and a few management-side attorneys.  They think that when the analytics draws a correlation, that correlation is statistically strong, meaning the correlation has a strong relationship to the job or job duties.  For example, coders who visit certain manga sites are better coders than those that don’t.  The statistics show this, so the logic is that you should only hire coders who visit manga sites.  But what if black coders don’t go to manga sites, and now your analytic tool is weeding them out.  This certainly looks like discrimination.  But the logic of data scientists that the statistics support the idea that good coding and manga sites are linked and therefore job-related and a business necessity under Title VII.  People analytic vendors love this.

On the other side are some attorneys (and some industrial psychologists) who believe that the statistics alone will not be sufficient to prevail under the law – an employer must show more than just the statistics to overcome Title VII’s job related and business necessity requirements. Professor Pauline Kim of the University of Washington St. Louis School of Law argues that the correlations need to be both statistically valid and “substantively meaningful.”  She argues (and I agree) that there needs to be some connection to the job that’s more than just math.  If a coder is coding for a manga site, then the criteria that the coder visit manga sites make sense – it’s substantively meaningful.  If coding for a workplace software company, it wouldn’t.  This adds a “smell test” to the statistics that a jury can understand and hold on to.  People analytics vendors don’t love this as much because it means they would have to validate their tools using more than just statistics.

The debate at the conference was lively.  We just don’t know what will happen and what theory will prevail.  The EEOC is certainly paying close attention to people analytics.  Last October, the agency held a public meeting on the subject and heard from many different stakeholders on the subject.  Acting Chair Victoria Lipnic is very interested in where analytics is headed.  So am I.

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