I think there's inherently an issue that models will literally never be able to handle, which is that when somebody comes along with a new way of doing something that's really excellent, the models will not recognize it. They only know how to recognize excellence when they can measure it somehow.

People are starting to be very skeptical of the Facebook algorithm and all kinds of data surveillance.

Micro-targeting is the ability for a campaign to profile you, to know much more about you than you know about it, and then to choose exactly what to show you.

We've learned our lesson with finance because they made a huge goddamn explosion that almost shut down the world. But the thing I realized is that there might never be an explosion on the scale of the financial crisis happening with big data.

With recidivism algorithms, for example, I worry about racist outcomes. With personality tests [for hiring], I worry about filtering out people with mental health problems from jobs. And with a teacher value-added model algorithm [used in New York City to score teachers], I worry literally that it's not meaningful. That it's almost a random number generator.

Especially from my experience as a quant in a hedge fund - I naively went in there thinking that I would be making the market more efficient and then was like, oh my God, I'm part of this terrible system that is blowing up the world's economy, and I don't want to be a part of that.

You'll never be able to really measure anything, right? Including teachers.