A Stanford study has revealed that Artificial Intelligence tools led to an increase in alleged ‘racial bias’ during the recruitment process, even as candidates increasingly depend on GenAI to apply for jobs online.

The study by Stanford HAI (Human Centered AI) surveyed 3.4 million people who submitted four million job applications for 1,700 job postings across 150 employers and 11 industry sectors and each job application was assessed by an AI hiring tool built by a single third-party vendor.

The study claims it found substantial evidence of racial disparities in AI-based candidate screening. 26% of Black applicants and 15% of Asian applicants applied to positions where the AI system discriminated against their racial group. 

The study disclosed that people who submit multiple applications to positions screened by the same algorithmic hiring vendor are more likely to be rejected from almost every position to which they apply. Ten percent of applicants who submit four applications face rejections. 

“To measure adverse impact, we apply the US Equal Employment Opportunity Commission EEOC’s “four-fifths rule,” which flags a position when one group is recommended at less than 80% of the rate of the most-recommended group — the relevant U.S. employment law (Title VII), “ the study mentioned.

According to the study ninety percent of U.S. employers use AI screening tools to sort and rank job seekers, with most relying on the same few third-party vendors.  

“Entry-level hiring has slowed.  At the same time, AI tools have made it easier than ever for job seekers to fire off applications. Together, fewer jobs and more applications mean companies are now seeing nearly three times as many applications for entry-level positions as in 2022,” the study highlighted. 

As per the study the impact of AI hiring tools impact remains unclear as it is rapidly evolving with new tools being built using language models and agents.  The study stressed on the need for independent research into algorithmic hiring as Without independent research, it will be difficult to pursue evidence-based AI policy to govern AI’s impact on individual job prospects and overall workforce composition. 

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