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When Computers Stand in the Schoolhouse Door


Suresh Venkatasubramanian, University of Utah

Suresh Venkatasubramanian of the University of Utah presented a method for finding disparate impact in algorithms last year at the ACM Conference on Knowledge Discovery and Data Mining.

Credit: Vincent Horiuchi / University of Utah

If you have ever searched for hotel rooms online, you have probably had this experience: surf over to another website to read a news story and the page fills up with ads for travel sites, offering deals on hotel rooms in the city you plan to visit. Buy something on Amazon, and ads for similar products will follow you around the Web. The practice of profiling people online means companies get more value from their advertising dollars and users are more likely to see ads that interest them.

The practice has a downside, though, when the profiling is based on sensitive attributes, such as race, sex, or sexual orientation. Algorithms that sort people by such categories risk introducing discrimination, and if they negatively affect a protected group's access to jobs, housing, or credit, they may run afoul of antidiscrimination laws. That is a growing concern as computer programs are increasingly used to help make decisions about who gets a credit card, which résumés lead to job interviews, or whether someone gets into a particular college. Even when the programs do not lead to illegal discrimination, they may still create or reinforce biases.


 

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