Research Roundup: Cornell Study Shows Companies Prefer to Keep Hiring Algorithms a Black Box

To combat time constraints and attempt to eliminate human bias, many companies have taken to entrusting at least part of their hiring processes to outside companies that use machine-learning algorithms to weed out applicants. However, with little known about how these algorithms work, they, too, may be perpetuating bias. New research from a Cornell University Computing and Information Science team found companies prefer obscurity over transparency when it comes to this emerging technology.

Mitigating Bias in Algorithmic Hiring: Evaluating Claims and Practices by Manish Raghavan, Solon Barocas, Jon Kleinberg and Karen Levy found that tech companies have been able to define, and therefore address, algorithmic bias subjectively. For starters, terms like “bias” and “fairness,” as they relate to these algorithms, have not been universally defined. Therefore, tech companies can be vague about how they handle these issues.

As part of the study, the researchers looked into 19 companies that create algorithmic pre-employment screenings. These screenings typically include video interviews, questions and games. The researchers looked at company sites to find information on how these algorithms work, scouring websites for webinars, pages or other documents that lay out practices and logistics surrounding the algorithms.

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