It was never going to be an easy process, the research hi-lights some of the challenges of AI tools to filter job applications.

>The researchers interviewed more than 2,250 executives across the United States, United Kingdom and Germany. They found more than 90 per cent of companies were using tools like ATS to initially filter and rank candidates.
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>But they often weren't using it well. Sometimes, candidates were scored against bloated job descriptions filled with unnecessary and inflexible criteria, which left some qualified candidates "hidden" below others the software deemed a more perfect fit.

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>"I advise HR practitioners they have to look into and have open conversations with their vendors: 'OK, so what's in your system? What's the algorithm like? … What is it tracking? What is it allowing me to do?" said Pamela Lirio, an associate professor of international human resources management at the Université de Montréal.
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>Lirio, who specializes in new technologies, says it's also important to question who built the AI and whose data it was trained on, pointing to the example of Amazon, which in 2018 scrapped its internal recruiting AI tool after discovering it was biased against female job applicants.
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>The system had been trained on the resumes of past applicants — who were, overwhelmingly, men — so the AI taught itself to down-rank applicants whose resumes mentioned competing in women's sports leagues or graduating from women's colleges.