New solutions aim to remedy the difficult problem of improving workforce diversity and inclusiveness.
According to global consulting firm McKinsey & Co, companies that exhibit gender and ethnic diversity are 15 and 35 percent more likely to outperform those that don’t, and companies in the top quartile of executive-board diversity had returns on equity that were 53 percent higher than those in the bottom quartile.
Although most organizations now recognize the value of diversity in their workplaces, even sustained efforts to achieve it are often thwarted by the fact that those workplaces are made up of human beings. And human beings who evaluate candidates and employees are likely to fall prey to what is known as unconscious bias, or automatic attitudes about gender, age, race, etc. that we are unaware we have and act upon. Now that artificial intelligence has come along, however, organizations have the power to make real change in this space. Here is some of the technology helping them do it.
According to an article on the SHRM blog, GapJumpers tackles implicit bias—the prejudice we don’t realize we have—through an online blind-audition process. Applicants are given a job-related assignment—for example, web developers are asked to create a web page—and then hiring managers assess the completed task without seeing any personal identifiers, including name, gender, work experience or educational background. GapJumpers and its clients have seen a 60 percent jump in applicants from traditionally underrepresented groups who make it through to interviews compared to resume-based screening.
Also featured on the SHRM blog was Textio, which has invented an AI system that examines 40 million job listings and considers the outcomes: how many people applied, how long the job stayed open, the demographic groups the description did or didn’t attract. Based on the data, a predictive engine provides feedback on how likely a job description is to draw diverse candidates along with suggestions for how to phrase descriptions using more neutral language.
Per a panel discussion at the recent HR Tech Conference recapped by HRE Daily’s Michelle Rafter, Success Factors identified nine key decision points where unconscious bias can affect a manager’s thinking about hiring, promotions and other key points in the talent lifecycle. The company used AI and machine learning to build decision-interruption nudges into its technology to make managers more aware of actions they’re taking.
On the HR Tech panel, ADP discussed Pay Equity Explore, a service that gives employers tools to analyze employee compensation data and identify inequities. With more companies interested in supporting gender identity and LGBTQ support networks, ADP is testing a separate service on its own employees that identifies sexual orientation in advance of offering it to customers.
Many studies have shown that we have biases based in names. Resumes and application materials labeled with typically male names like James are rated more highly than those with typically female names like Jane. Research has also uncovered significant discrimination against African-American sounding names: white sounding names receive 50 percent more callbacks for interviews. Unbias.io, which is a Google Chrome extension, removes names and photos on LinkedIn, in both standard and recruiter account searches and profile views, to help reduce the effects of unconscious bias.
Similarly, Blendoor captures candidate data from a company’s existing applicant tracking systems and/or online job boards. Candidate profiles are ‘blendorized’ – or displayed without name, photo, or dates. Blendoor also collects EEO demographic data to enable talent pipeline analytics based on race, gender, LGBTQ, veteran, and disabled identities and uncover bias as it’s happening in real-time so that leaders can be held accountable.
Posted in Digital Transformation, Process Improvement | Tagged applicant tracking, artificial intelligence, bias, Decision Making, diversity, gender, hiring, inclusion, job descriptions, process improvement, resumes, screening, SHRM, technology, unconscious bias