Hiring the right people is one of the most important decisions a business can make. Regardless of industry, most companies require the following attributes of future hires: necessary hard skills, soft skills and experience to fill a given position. Hard skills and experience are, for the most part, quantifiable. Soft skills are more difficult to capture. Research suggests businesses historically hire based on knowledge and experience and then fire based on soft skills. As a result of this missing success indicator, businesses are turning to pre-hire assessments to weed out some of the candidates and create a better job fit, including neurological based-tools such as artificial intelligence.
The use of pre-hire soft skill assessments is growing, as companies attempt to be more predictive in their hiring process. According to the Talent Board’s 2017 Candidate Experience Research report, 89 percent of companies are using some form of pre-employment assessment tool — up from 82 percent in 2016. Using pre-hire assessments is estimated to be an $800-million market.
With this need to identify soft skills, businesses still tend to look for a quick-fix approach to hiring. Everyone wants some Holy Grail to rank candidates and offer up only the best. They want the latest and greatest technology to assess body language, facial expressions, voice inflection and word choices to reveal job-related soft skills. They want to feed all the resumes from top producers into a computer algorithm so a black box will indicate how to find the perfect fit.
No matter what selection criteria are selected, no tool should be solely used for the decision-making process. Most researchers agree that best practice hiring requires a combination of several data-gathering approaches to capture the value of a potential employee. Such approaches may include a résumé review, face-to-face structured interviews, and pre-hire assessments that help articulate specific job-related soft skills.
Employers should be cautious when selecting an assessment tool in the hiring process by keeping in mind that the goal is to put in place a predictability model that maintains defensible reliability and validity.
For example, before using any pre-hire assessment, a business may want to consider more basic concerns, especially in light of some of the newer approaches such as the use of artificial intelligence.
Does the assessment company provide evidence that the tool does not introduce adverse impact such as discrimination in some form?
A Business Insider article published in October revealed how Amazon fed 10 years of good-hire résumés into a system until the big data created a benchmark for performance. The problem with this is, the company forgot to tell the computer that gender hires had changed over that decade. As such, the algorithm was systematically rejecting women. This process applied an old paradigm that all we need to do is clone our best rather than benchmarking an actual job. Building for the future by hiring based on the past is not the best approach.
Does the assessment provide statistical documentation that the assessment measures job-related performance?
A September Wall Street Journal article explored two companies using data-science or artificial intelligence to determine who gets hired. While the process may in time prove to be both reliable and predictive, at present there is very little data to support that micro-facial expressions, voice inflection and positive word choices reveal job-related soft skills. The key in this concern is on job-related skills. Human resources professionals are aware of the need to collect only interviews and application material that are specifically job-related.
Technological advancements are occurring at an ever-increasing rate. Neurological discoveries and associated investigative tools are leading to new understandings of human behavior never before explored. While the future is bright, science requires time for peer review and refinement of new technologies.
Ron Bonnstetter, Ph.D. is senior vice president of research and development and director of the TTI SI Center for Applied Cognitive Research at TTI Success Insights in Scottsdale, Arizona.