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Volume 589 Issue 7843, 28 January 2021

Exposing bias

Across many economies in the world, women and individuals from minority ethnic groups regularly experience unfavourable outcomes when seeking employment. But the part played by discrimination in these results has been unclear. In this week’s issue, Dominik Hangartner, Daniel Kopp and Michael Siegenthaler present an approach for quantifying hiring discrimination by tracking the search behaviour of recruiters on employment websites and using machine learning to control for the jobseeker characteristics that the recruiters see. Testing their technique on a platform for jobseekers in Switzerland, the researchers found that individuals from immigrant and minority ethnic groups had rates of contact that were 4–19% lower than those for otherwise identical candidates from the majority ethnic group. The team also saw that women had 7% lower contact rates in male-dominated professions, although the opposite pattern was seen for male jobseekers in female-dominated professions. The researchers suggest that their tool could offer a cost-efficient and non-intrusive way to continuously monitor and counter discrimination in recruitment. The cover image is an artistic representation of racial discrimination in recruitment, adapted from Shutterstock/fizkes.

Cover image: Adapted from Shutterstock/fizkes

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