It's a standard thing you hear from startup people - that their product is somehow improving the world. And if you follow the reasoning, you will get somewhere, and I'll tell you where you get: You'll get to the description of what happens to the winners under the system that they're building.

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.

The training one receives when one becomes a technician, like a data scientist - we get trained in mathematics or computer science or statistics - is entirely separated from a discussion of ethics.

It's a standard thing you hear from startup people - that their product is somehow improving the world. And if you follow the reasoning, you will get somewhere, and I'll tell you where you get: You'll get to the description of what happens to the winners under the system that they're building.

The most important goal I had in mind was to convince people to stop blindly trusting algorithms and assuming that they are inherently fair and objective.

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.

Every system using data separates humanity into winners and losers.