“The whole idea behind Pymetrics is that instead of using a resume, you are looking at people’s cognitive, social, and emotional aptitudes,” Polli says. “It’s also much more future-facing and potential-oriented, rather than backwards-facing and sort of only talking about your past experiences. It’s a much more holistic, hopeful view of someone than, Oh, this is what you’ve done, and this is all you can do.”——Bias, in Polli’s view, is a human problem. True, it can pop up in the algorithms that humans create to sort through job applications. But it’s at least as much of a risk in the people who review resumes and conduct interviews, who are naturally prone to make unfair judgments based on everything from a person’s name (pdf) and gender to their appearance and speech patterns. Algorithms, Polli suggests, are at least more trainable.——“It’s hard to remove bias from algorithms, but it is possible,” she says. “It is not possible to remove bias from humans.”——Those assertions are up for debate. But at least one thing is clear: Companies love the idea of algorithms, which promise to evaluate talent at far greater speed and lower cost than regular flesh-and-blood recruiting and hiring processes allow. Pymetrics’ client list includes big names like Unilever, Nielsen, LinkedIn, Accenture, KraftHeinz, MasterCard, and Boston Consulting Group. Venture capital is betting on Pymetrics, too. The company’s primary backers are General Atlantic, Jazz Venture Partners, Khosla Ventures, Salesforce Ventures, and Workday Ventures, and it has thus far raised $56.6 million in funding. At the same time, the AI-driven hiring tools provided by companies like HireVue are also becoming increasingly widespread.—-And so the salient question at the moment isn’t whether companies should use machine learning to filter job candidates. It’s already happening. More relevant is the matter of whether the talent revolution already underway is a fair one—and what more can be done to ensure that algorithms alleviate, rather than deepen, the longstanding problems in hiring.