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A study released in November 2018 examined how algorithms are used to decide loan approval, a task that can be laden with biases. Companies that leverage algorithms can’t turn a blind eye to the results their software provides; instead they should understand how the algorithm works, what data it pulls from and monitor its results, a Big Law attorney said.

The “Fairness Under Unawareness: Assessing Disparity When Protected Class Is Unobserved” paper penned by Cornell University professors, a Ph.D student and Capital One staffers, found potential pitfalls when algorithms are used when protected classes, such as gender or race, aren’t given by applicants applying for a loan.

Nathan Kallus, a Cornell Tech professor and co-writer of the paper, said that when an applicant doesn’t include their protected class, regulators may be overestimating disparities by guessing race by zip code or other factors. For example, when an applicant doesn’t list their race in a loan application, institutions use “proxy variables” such as zip codes or surnames listed on the application to predict what race the applicant is, Kallus said.

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Victoria Hudgins

I am a reporter for Legaltech News where I cover national and international cyber regulations and legal tech innovations and developments.

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