In her most recent column in Wired, UNC School of Information and Library Science (SILS) Associate Professor Zeynep Tufekci discussed the need to stop excessive data collection practices for machine learning (ML), and to develop privacy-preserving ML methods.
Tufekci talked about the trade-offs society faces through innovations. With ML expansion and privacy violations, putting effective regulations in place will not only stop abusive practices, but also speed up innovation.
“When faced with new regulatory barriers, companies and researchers will pour effort into developing new compliant ways to have their cake,” she said. “With any luck, their breakthroughs will make it newly possible for, say, medical researchers to use machine learning on sensitive, private data sets—and for the rest of us to enjoy the perks of ubiquitous AI without having our privacy savaged.”