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?"

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

I would argue that one of the major problems with our blind trust in algorithms is that we can propagate discriminatory patterns without acknowledging any kind of intent.

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.

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.

I don't think anybody's ever notified that they were sentenced to an extra two years because their recidivism score had been high, or notified that this beat cop happened to be in their neighborhood checking people's pockets for pot because of a predictive policing algorithm. That's just not how it works.

I think what's happened is that the general public has become much more aware of the destructive power of Wall Street.