Machine learning systems are implemented by all the big tech companies in everything from ad auctions to photo-tagging, and are supplementing or replacing human decision making in a host of more mundane, but possibly more consequential, areas like loans, bail, policing, and hiring. And we’ve already seen plenty of dangerous failures; from risk assessment tools systematically rating black arrestees as riskier than white ones, to hiring algorithms that learned to reject women. There’s a broad consensus across industry, academe, government, and civil society that there is a problem here, one that presents a deep challenge to core democratic values, but there is much debate over what kind of problem it is and how it might be solved. Taking a sociological approach to the current boom in ethical AI and machine learning initiatives that promise to save us from the machines, this talk explores how this problem becomes a problem, for whom, and with what solutions.