Occupy provided me a lens through which to see systemic discrimination.

Obviously the more transparency we have as auditors, the more we can get, but the main goal is to understand important characteristics about a black box algorithm without necessarily having to understand every single granular detail of the algorithm.

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.

When I think about whether I want to take a job, I don't just think about whether it's technically interesting, although I do consider that. I also consider the question of whether it's good for the world.

There's less of a connection for a lot of people between the technical decisions we make and the ethical ramifications we are responsible for.

So much of our society as a whole is gearing us to maximize our salary or bonus. Basically, we just think in terms of money. Or, if not money, then, if you're in academia, it's prestige. It's a different kind of currency. And there's this unmeasured dimension of all jobs, which is whether it's improving the world.