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HOW TO REVERT BACK TO BIAS AMP 1 HOW TO
Using group characteristics to make decisions about whether and how to provide services to individual consumers may seem logical in some cases and may even be profitable in the short term. When women hear that their female friends and colleagues have been passed over for jobs at a particular company, they are less likely to apply, even if they know nothing about why these other candidates were rejected. People learn about opportunities from members of their social circles, who often have race, age, gender, and other demographic characteristics in common. Not only are employers potentially missing out on valuable candidates, but over time these losses will compound through word of mouth. Based on past patterns, the algorithm learned to downgrade resumes that mentioned certain women-only colleges or women’s sports or clubs.Īmazon dropped that tool once these biases were discovered, but companies still widely use algorithms for recruiting and hiring. Although the algorithm was not programmed to look at the gender of the job applicants, it was trained using data from the company’s previous decade of hiring decisions, and the applications in that period mainly came from men.
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In 2014, Amazon launched an internal tool to evaluate resumes.
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To understand how this can happen, consider one tech giant’s failed attempts at algorithmic design. It can occur even if the inner workings and biases of an algorithm remain invisible to the public. This damage won’t just hit a few unlucky companies that find themselves embroiled in public controversy around algorithmic discrimination. My research shows that over time, word of mouth about algorithmic bias among customers will hurt demand and sales and cut into profits. Algorithmic bias is not only a pressing ethical and societal concern - it’s also bad for business. It’s one that might make businesses more amenable to regulation or even preclude the need for it by motivating them to act on their own. An important part of the story has been missing, however.