• Employers may be able to improve the diversity and quality of job candidates by using hiring algorithms that value exploration, or that account for candidates' unique backgrounds and work histories, according to an August working paper published by the National Bureau of Economic Research.
• Researchers built three resume screening algorithms for first-round interviews for high-paying jobs in industries often criticized for a lack of diversity, according to an emailed statement from the MIT Sloan School of Management. The third algorithm implemented an "upper confidence bound," or UCB, model that included "exploration bonuses" which account for details such as having an unusual college major, different geographies or unique work histories. These bonuses tend to be higher for groups of candidates who are underrepresented, the statement said.
• Using the third algorithm, researchers more than doubled the share of candidates who were Black or Hispanic, whereas the first two algorithms — which used a typical "standardized learning" approach — actually decreased Black and Hispanic representation. But while all of the algorithms increased the share of applicants who were women compared to human recruiting, the UCB model selected fewer female candidates than the two standardized learning models.

Employers are increasingly automating recruiting processes during the COVID-19 pandemic, according to sources who previously spoke to HR Dive. Virtual skill assessments, screening tools, on-demand interviews and simulations, in particular, have grown in adoption during this time.

Yet it is algorithms, which many adopted prior to the pandemic, that have been a specific source of controversy... [for more, please visit HR Dive]