Adam S. Forman, Nathaniel M. Glasser, and Matthew Savage Aibel, attorneys in the Employment, Labor & Workforce Management practice, co-authored an article in SHRM.org, titled “Minimize Risks When Using Big Data Analytics in Hiring.”
Following is an excerpt:
Despite the many advantages of “big data” analytics, employers must be ready to manage the potential risks, particularly when hiring.
While the phrase has different meanings depending on the context, “big data” typically refers to data that is so large in volume that computers, rather than traditional methods of analysis, are necessary to understand it. “Big data analytics,” a phrase often used synonymously for the actual data and its computerized analysis, encompasses data’s volume, collection speed, type collected and how best to decipher it. Marketing departments have long used big data analytics to target potential customers with pinpoint accuracy. HR departments increasingly consider whether and how to incorporate big data tools into their hiring processes.
The promise offered by big data analytics includes better outreach to potential applicants, increased efficiency in the hiring process, fewer people hours spent combing through resumes, and the selection of more qualified and better-matched candidates. The market includes a variety of analytical tools for these purposes, such as algorithms that scan resumes to match candidates to jobs by simulating human hiring tendencies, measure candidates on personality traits deemed critical for success in the job and assess the cognitive abilities of each candidate against those of high-performing incumbents. Vendors market their big data tools as predictive algorithms that will allow their clients to hire the right people by using data that maps the applicant’s profile onto the company’s available openings. Ultimately, by hiring the right people, companies will improve productivity, increase retention, and spend fewer resources on employee selection. …
Before adopting big data analytics, however, employers must be aware of the potential risks.
For example, an employer cannot easily “look under the hood” to see precisely how the selection algorithm is operating, partially because vendors consider the algorithm to be proprietary and confidential, and partially because the vendors themselves do not know exactly how the algorithm has changed as a result of machine learning. Without the ability to assess what the selection algorithm is doing, employers may have difficulty determining which factors, if any, are a potential source of bias.