{"id":124753,"date":"2020-12-02T21:06:11","date_gmt":"2020-12-02T18:06:11","guid":{"rendered":"https:\/\/en.buradabiliyorum.com\/study-shows-how-ai-exacerbates-recruitment-bias-against-women\/"},"modified":"2020-12-02T21:06:11","modified_gmt":"2020-12-02T18:06:11","slug":"study-shows-how-ai-exacerbates-recruitment-bias-against-women","status":"publish","type":"post","link":"https:\/\/buradabiliyorum.com\/en\/study-shows-how-ai-exacerbates-recruitment-bias-against-women\/","title":{"rendered":"#Study shows how AI exacerbates recruitment bias against women"},"content":{"rendered":"

“#Study shows how AI exacerbates recruitment bias against women<\/strong>”<\/p>\n

\n A new study<\/a> from the University of Melbourne has demonstrated how hiring algorithms\u00a0can amplify human gender biases against women.<\/p>\n

Researchers from the University of Melbourne gave 40 recruiters real-life\u00a0resum\u00e9s for jobs at UniBank, which funded the study. The resum\u00e9s were for roles as a data analyst, finance officer, and recruitment officer,\u00a0which Australian Bureau of Statistics data shows are respectively male-dominated, gender-balanced, and female-dominated positions.<\/p>\n

Half of the recruitment panel was given resum\u00e9s with the candidate\u2019s stated gender. The other half was given the exact same resum\u00e9s, but with traditionally female names\u00a0and male ones interchanged. For instance, they might switch \u201cMark\u201d to \u201cSarah\u201d and \u201cRachel\u201d to \u201cJohn.\u201d<\/p>\n

The panelists were then instructed to rank each candidate and collectively pick the top and bottom three\u00a0resum\u00e9s for each role. The researchers then reviewed their decisions.<\/p>\n

[Read: How to build a search engine for criminal data]<\/em><\/p>\n

They found that the recruiters consistently preferred\u00a0resum\u00e9s from the app<\/a>arently male candidates<\/span>\u00a0\u2014 even though they had the same qualifications and experience as the women. Both male and female panelists were more likely to give men\u2019s\u00a0resum\u00e9s a higher rank.<\/span><\/p>\n

\"\"
Credit: The University of Melbourne<\/figcaption>
<\/a>Data suggest 70% of data analysts in Australia are men. If an algorithm is trained to rank candidates based on these statistics, it could assume that a male name is a desirable quality for the position.<\/figcaption><\/figure>\n

The researchers then used the data to create a hiring algorithm that would rank each candidate in-line with the panel\u2019s preferences \u2014 and found that it reflected their biases.<\/p>\n

Read:\u00a0Amazon\u2019s sexist hiring algorithm could still be better than a human<\/p>\n

\u201cEven when the names of the candidates were removed, AI assessed resum\u00e9s based on historic hiring patterns where preferences leaned towards male candidates,\u201d said study\u00a0co-author Dr Marc Cheong in a statement<\/a>.<\/span><\/p>\n

\u201cFor example, giving advantage to candidates with years of continuous service would automatically disadvantage women who\u2019ve taken time off work for caring responsibilities.\u201d<\/p>\n

The study relied on a small sample of data, but these types of gender biases have also been documented in large companies. Amazon, for example, had to shut down a hiring algorithm<\/a> tool after discovering it was discriminating against female applicants, because the models were predominantly trained on resumes submitted by men.<\/p>\n

\u201cAlso, in the case of more advanced AIs that operate within a \u2018black box\u2019 without transparency or human oversight, there is a danger that any amount of initial bias will be amplified,\u201d added Dr\u00a0Cheong.<\/span><\/p>\n

The researchers believe the risks can be reduced by making hiring algorithms more transparent. But we also need to address our inherent human\u00a0biases \u2014 before they\u2019re baked into the machines.<\/p>\n

\n Published December 2, 2020 \u2014 18:06 UTC\n <\/p>\n<\/p><\/div>\n