Machines Shouldn?t Have to Spy On Us to Learn

Diffie and Hellman didn’t just render the dead drop obsolete; they made the internet as we know it possible.Now consider another, more modern phenomenon that needs to be made obsolete—a data transaction that is very different from a dead drop but also terribly flawed.Today, if you’re a company that benefits from machine learning, chances are that, somewhere along the line, you’ve engaged in a bunch of nightmarish surveillance. The technology’s success is simultaneously a privacy-­violating disaster for society.We need a new breakthrough in encryption, one that fundamentally changes the rotten trade-off we now make between privacy and AI.At the same time, other pitfalls with today’s machine learning hold it back from being useful where it might really help. For instance, it’s a challenge right now for responsible actors to use machine learning in scenarios where it’s not legally possible or ethically desirable to share underlying data. When faced with new regulatory barriers, companies and researchers will pour effort into developing new compliant ways to have their cake.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.