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study has revealed that subconscious gender biases from a range of
sources leads to unfavourable outcomes for female job applicants, observes Jessie Tu, journalist with Women's Agenda.
A new study from the University of Melbourne has revealed that subconscious gender biases from a range of sources leads to unfavourable outcomes for female job applicants. 
Job
Photo: Women's Agenda
The study was commissioned by UniBank with
an aim to deepen understanding of how artificial intelligence (AI)
influences the likelihood of women being hired in finance industry
roles.
The findings revealed
that gender bias enters the recruitment procedure at a number of
different points — the main causes of bias include gender-skewed
datasets, correlational bias judgements in algorithms and human
decision-making...
How did the study work?
40 Masters, PhD and post-graduate degree students who have had experience in employment-hiring acted as recruiters. They were given real-life CVs from job-applicants applying for jobs at a bank.
The job-applicants were applying for three different roles — data analyst, which is male dominated, finance, which is gender-balanced and recruitment officer, which is female-dominated...
Professor Ruppanner added that algorithms must be used carefully to avoid skewed results, and that these problems were exacerbated when large recruiting websites fail to broadcast how their algorithms function.
“The algorithm isn’t thinking about experience, it’s just finding associations,” she said. “You have to say to it, ‘Don’t penalise women for parental leave.’ It has to be coded in.”
Source: Women's Agenda
