When Algorithms Are Racist: How to Protect Against Biased Algorithms
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.
March 07, 2019 at 12:00 PM
4 minute read
The original version of this story was published on Legal Tech News
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.
NOT FOR REPRINT
© 2024 ALM Global, LLC, All Rights Reserved. Request academic re-use from www.copyright.com. All other uses, submit a request to [email protected]. For more information visit Asset & Logo Licensing.
Trending Stories
Featured Firms
Law Offices of Gary Martin Hays & Associates, P.C.
(470) 294-1674
Law Offices of Mark E. Salomone
(857) 444-6468
Smith & Hassler
(713) 739-1250