AI Is Coming for Hiring, and It Might Not Be That Bad
Artificial intelligence promises to make hiring an unbiased utopia, according to Bloomberg.
Employee referrals, a process that tends to leave underrepresented groups out, still make up a bulk of companies’ hires. Recruiters and hiring managers also bring their own biases to the process, studies have found, often choosing people with the “right-sounding” names and educational background.
Across the pipeline, companies lack racial and gender diversity, with the ranks of underrepresented people thinning at the highest levels of the corporate ladder. Fewer than 5 percent of chief executive officers at Fortune 500 companies are women. Racial diversity among Fortune 500 boards is almost as dismal, as four of the five new appointees to boards in 2016 were white.
AI advocates argue the technology can eliminate some of these biases. Instead of relying on people’s feelings to make hiring decisions, companies such as Entelo and Stella.ai use machine learning to detect the skills needed for certain jobs. The AI then matches candidates who have those skills with open positions. The companies claim not only to find better candidates, but also to pinpoint those who may have previously gone unrecognized in the traditional process.
Stella’s algorithm only assesses candidates based on skills, for example, said founder Rich Joffe. “The algorithm is only allowed to match based on the data we tell it to look at. It’s only allowed to look at skills, it’s only allowed to look at industries, it’s only allowed to look at tiers of companies.” That limits bias, he said.
Entelo released Unbiased Sourcing Mode, a tool that further anonymizes hiring. The software allows recruiters to hide names, photos, school, employment gaps and markers of someone’s age, as well as to replace gender-specific pronouns, all in the service of reducing various forms of discrimination.
Not all algorithms are created equal, and there’s disagreement among the AI community about which algorithms have the potential to make the hiring process more fair. One type of machine learning relies on programmers to decide which qualities should be prioritized when looking at candidates. These “supervised” algorithms can be directed to scan for individuals who went to Ivy League universities or who exhibit certain qualities, such as extroversion.
“Unsupervised” algorithms determine on their own which data to prioritize. The machine makes its own inferences based on existing employees’ qualities and skills to determine those needed by future employees. If that sample only includes a homogeneous group of people, it won’t learn how to hire different types of individuals, even if they might do well in the job.
Companies can take measures to mitigate these forms of programmed bias. Pymetrics, an AI hiring startup, has programmers audit its algorithm to see if its giving preference to any gender or ethnic group. Software that heavily considers ZIP code, which strongly correlates with race, will likely have a bias against black candidates, for example. An audit can catch these prejudices and allow programmers to correct them.