On October 13, 2016, the U.S. Equal Employment Opportunity Commission empaneled a group of Big Data experts, including Littler Shareholder Marko Mrkonich, to discuss the use of data analytics in hiring, performance management, retention and other employment decisions. All on the panel agreed that Big Data is already being used in employment, and its use and its scope are expected to continue to expand. Much of the discussion between the Commissioners and the panelists centered on how the use of data analytics may interact with equal employment opportunity laws and whether algorithm-based human resources decisions may ultimately improve fairness and efficiency and minimize bias.
"Big Data has the potential to drive innovations that reduce bias in employment decisions and help employers make better decisions in hiring, performance evaluations, and promotions," Commission Chair Jenny R. Yang said. "At the same time," Yang added, "it is critical that these tools are designed to promote fairness and opportunity, so that reliance on these expanding sources of data does not create new barriers to opportunity."
Commissioners heard from the panel on the many different uses of Big Data—or using artificial intelligence, algorithms, "data scraping" of publicly available information, and other means of evaluating tens of thousands of pieces of information about applicants and employees—in human resources decision-making.
"It's an exciting era because this capability is going to give a fair shot to millions of job applicants who wouldn't have been considered previously," said Dr. Michael Housman, a workforce scientist at hiQ Labs. He added that while in the traditional interview process, there is a "'like me' bias that leads recruiters to hire applicants like themselves," Big Data can be a tool to eliminate this bias because "algorithms are designed to make assessment decisions based on the factors that actually matter and have been correlated statistically with on-the-job performance and outcomes."
Dr. Michal Kosinski, a professor of Organizational Behavior at Stanford Graduate School of Business, agreed that Big Data can be used to improve equal employment opportunity. "If used properly, Big Data—coupled with modern computational techniques—can improve person-job fit, increase our ability to identify talent, raise equality in access to jobs and careers, and help overcome implicit and explicit prejudice in the workplace," Kosinski testified.
Dr. Kathleen Lundquist, an organizational psychologist, conceded that Big Data is here to stay, but was more concerned about its implementation: "Big Data, predictive analytics or talent analytics . . . is the inevitable future of HR. It presents a future that is both promising and scary." She cautioned that, "algorithms may be trained to predict outcomes which are themselves the result of previous discrimination."
Lundquist said that, for example, if the hiring algorithm was based on mining data from a group of successful employees, "the high-performing group may be non-diverse and hence the characteristics of that group may more reflect their demographics than the skills or abilities needed to perform the job." She concluded that in her example, the algorithm would be "matching people characteristics, rather than job requirements."
Dr. Kelly Trindel, Chief Analyst in EEOC's Office of Information, Research, and Planning, also identified potential areas of concern pertaining to Big Data algorithms. "If the training phase for a Big Data algorithm happened to identify a greater pattern of absences for a group of people with disabilities, it might cluster the relevant people together to create a 'high absenteeism risk' profile. The profile need not be tagged as 'disability'-rather it might appear to be based on some group of financial, consumer, or social media behaviors."
Housman responded to these fears of algorithms leading to an increase in bias in decision-making by noting that employers can work with data analysts to build algorithms that are aimed at widening opportunity and improving diversity.
Littler Shareholder Marko Mrkonich testified that "it is already clear that Big Data, used correctly, can be a powerful tool to eliminate overt and implicit bias from an employee selection process, and a misplaced, rigid adherence to outdated legal tests and standards cannot prevent this progress from taking place." Mrkonich added that "the challenge for employers is to embrace the strengths of Big Data without losing sight of their own business goals and culture amidst potential legal risks [while the] challenge for the legal system is to permit those engaged in the responsible development of Big Data methodologies in the employment sector to move forward and explore their possibilities without interference from guidelines and standards based on assumptions that no longer apply or that become obsolete the next year."
The Commission did not indicate that it currently planned to issue guidance, or otherwise take any action pertaining to how employers are using Big Data in the workplace.
Commissioner Victoria A. Lipnic, who helped organize the meeting, said, "It can be a challenge to determine whether, when, and how laws may apply in our increasingly technology-driven workplaces. But [at the core of our responsibilities is]: Ensuring that our understanding of today's workplaces and our interpretation and administration of the law, are as current and fully-informed as possible. It's for that reason that holding meetings like today is so crucial to our work."
Littler's Workplace Policy Institute will continue to engage with policy makers on this important issue in the months and years ahead.
The EEOC will hold the October 13, 2016 Commission meeting record open for 15 days. Members of the public may submit written comments on any issues or matters discussed at the meeting. Public comments may be mailed to Commission Meeting, EEOC Executive Officer, 131 M Street, N.E., Washington, D.C. 20507, or Commissionmeetingcomments@eeoc.gov.
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