Unfair Automated Hiring Systems Are Everywhere
Algorithms can exacerbate employment discrimination
Earlier this month, Lina Khan, chair of the US Federal Trade Commission (FTC), wrote an essay in The New York Times affirming the agency’s commitment to regulating AI. But there was one AI application Khan didn’t mention that the FTC urgently needs to regulate: automated hiring systems. These range in complexity from tools that merely parse resumes and rank them to systems that green-light candidates and trash applicants deemed unfit. Increasingly, working Americans are obligated to use them if they want to get hired.
In my recent book, The Quantified Worker, I argue that the American worker is being reduced to numbers by AI technologies in the workplace, automated hiring systems chief among them. These systems reduce applicants to a score or rank, often ignoring the gestalt of their human experience. Sometimes they even sort people by their race, age, and sex, a practice that’s legally prohibited from being part of the employment decision-making process.
Ironically, many of these systems are marketed as being bias-free or guaranteed to reduce the probability of discriminatory hiring. But because they’re so loosely regulated, such systems have been shown to deny equal employment opportunity on the basis of protected categories such as race, age, sex, and disability. In December 2022, for example, a female truckers union sued Meta, alleging that Facebook “selectively shows job advertisements based on users’ gender and age, with older workers far less likely to see ads and women far less likely to see ads for blue-collar positions, especially in industries that historically exclude women.” This is deceptive. Even more, it is unfair to job applicants and employers alike. Employers purchase automated hiring systems to reduce their liability for employment discrimination, and the vendors of those systems are legally obligated to substantiate their claims of efficacy and fairness.
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