Step one: cleanse the skin?
We’ll be interacting with machines for the rest of our lives, as tech futurist and New York University professor Amy Webb told us recently.
But who are those machines built by? And how does that affect their function?
A recent study by the Georgia Institute of Technology revealed that if you’re a person with dark skin, you may “be more likely than your white friends to get hit by a self-driving car,” according to Vox.
Here’s more from their analysis:
The study’s insights add to a growing body of evidence about how human bias seeps into our automated decision-making systems. It’s called algorithmic bias.
The most famous example came to light in 2015, when Google’s image-recognition system labeled African Americans as “gorillas.” Three years later, Amazon’s Rekognition system drew criticism for matching 28 members of Congress to criminal mugshots. Another study found that three facial-recognition systems — IBM, Microsoft, and China’s Megvii — were more likely to misidentify the gender of dark-skinned people (especially women) than of light-skinned people.
Since algorithmic systems “learn” from the examples they’re fed, if they don’t get enough examples of, say, black women during the learning stage, they’ll have a harder time recognizing them when deployed.
We speak to an expert on algorithms about how they’re developed and whom they affect.
- Kartik Hosanagar Author of “A Human’s Guide to Machine Intelligence: How Algorithms Are Shaping Our Lives and How We Can Stay in Control;” professor of technology and digital business at Wharton
- Safiya Noble, PhD Author of "Algorithms of Oppression: How Search Engines Reinforce Racism"; associate professor, UCLA; visiting faculty, the University of Southern California;
An Excerpt From 'A Human's Guide To Machine Intelligence'
A HUMAN’S GUIDE TO MACHINE INTELLIGENCE by Kartik Hosanagar, to be published on March 12, 2019 by Viking, an imprint of Penguin Publishing Group, a division of Penguin Random House LLC. Copyright © 2019 Kartik Hosanagar.
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