See below for a three-minute, animated video for employers and HR professionals who are interested in learning more about how to minimize the risk and maximize the rewards of using talent selection tools.
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By now, most large employers have become accustomed to using “big data” to help them conduct business. It is little wonder, then, that many employers want to leverage big data when making decisions about what is their most valuable asset: See more +
See below for a three-minute, animated video for employers and HR professionals who are interested in learning more about how to minimize the risk and maximize the rewards of using talent selection tools.
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By now, most large employers have become accustomed to using “big data” to help them conduct business. It is little wonder, then, that many employers want to leverage big data when making decisions about what is their most valuable asset: their employees. Vendors are increasingly selling employment-selection tools, such as résumé scraping software, that purport to predict an applicant’s future job performance. For example, a statistical algorithm may learn from personnel data that employees with English degrees have a high turnover rate, so a tool could be developed to find applicants with those degrees and weed them out. Nilan Johnson Lewis labor and employment attorney Mark Girouard cautions that relying on big data exposes employers to big risks, because these tools may screen out members of one gender or race group at different rates than others. Girouard cautions that even the most cutting-edge technology can’t tell an employer or recruiter as much as they hope. “With enough data points, you can always find statistical correlations,” Girouard notes, “but those correlations do not necessarily tell you something meaningful.” “While big data tools are backed by science, that does not necessarily mean the selection criteria they identify are important to your jobs.” Girouard adds that “like other selection tools—such as cognitive, personality, or physical abilities tests—if a hiring algorithm impacts members of one gender and race group differently than others and is based on factors that the employer cannot prove are important to its jobs, it is unlawful.” Girouard counsels employers to push back on screening technology vendors and not take their findings at face value. Instead, they should ask to “look under the hood” to see what those findings actually mean in the context of their jobs. “While there has not been much litigation on this issue yet, the EEOC has taken a particular interest in big data screening tools, so you can see it coming,” says Girouard. See less -
