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

People felt like they were friends with Google, and they believed in the "Do No Evil" thing that Google said. They trusted Google more than they trusted the government, and I never understood that.

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.

We can't just throw something out there and assume it works just because it has math in it.

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.

An insurance company might say, "Tell us more about yourself so your premiums can go down." When they say that, they're addressing the winners, not the losers.

There might never be that moment when everyone says, "Oh my God, big data is awful."