Because of my experience in Occupy, instead of asking the question, "Who will benefit from this system I'm implementing with the data?" I started to ask the question, "What will happen to the most vulnerable?" Or "Who is going to lose under this system? How will this affect the worst-off person?" Which is a very different question from "How does this improve certain people's lives?"

I wanted to prevent people from giving them too much power. I see that as a pattern. I wanted that to come to an end as soon as possible.

I know how models are built, because I build them myself, so I know that I'm embedding my values into every single algorithm I create and I am projecting my agenda onto those algorithms.

The national conversation around white entitlement, around institutionalized racism, the Black Lives Matter movement, I think, came about in large part because of the widening and broadening of our understanding of inequality. That conversation was begun by Occupy.

When people are not given an option by some secret scoring system, it's very hard to complain, so they often don't even know that they've been victimized.

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

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.