What will a robot make of your résumé? The bias problem with using AI in job recruitment
Published: June 9, 2024 4:01pm EDT

Magnifying human bias
Although one of the reasons for using AI in recruitment is that it is meant to be to be more objective and consistent, multiple studies have found the technology is, in fact, very likely to be biased. This happens because AI learns from the datasets used to train it. If the data is flawed, the AI will be too.

Biases in data can be made worse by the human-created algorithms supporting AI, which often contain human biases in their design.

In interviews with 22 HR professionals, we identified two common biases in hiring: “stereotype bias” and “similar-to-me bias”.

Stereotype bias occurs when decisions are influenced by stereotypes about certain groups, such as preferring candidates of the same gender, leading to gender inequality.

“Similar-to-me” bias happens when recruiters favour candidates who share similar backgrounds or interests to them.

These biases, which can significantly affect the fairness of the hiring process, are embedded in the historical hiring data which are then used to train the AI systems. This leads to biased AI.

So, if past hiring practices favoured certain demographics, the AI will continue to do so. Mitigating these biases is challenging because algorithms can infer personal information based on hidden data from other correlated information.

Tracy Walsh
Economy + Business Editor

What will a robot make of your résumé? The bias problem with using AI in job recruitment