Liars, Outliers, and Algorithmic Fairness
This past week, I was at a pair of workshops on Workshop on Fairness, Accountability, and Transparency in Machine Learning and Data and Algorithm Transparency. They were both great workshops.
For obvious reasons, the election hung as something of a cloud over the meetings. It wasn’t constantly discussed, but we kept returning to it from time to time. It’s pretty sad, in my opinion, when ‘what does this work like if rule of law collapses?’ is a live question. Regulation is a key outcome of fairness research, and representatives from a number of regulatory agencies were in attendance. There’s a very real concern that regulation and policy will not be available levers for the next several years.
Some of the discussion, therefore, was about ways to supplement or compensate for lack of regulatory mechanisms. Far more questions were raised than answered, I think, but it was discussed both in the panels and in hallway discussions.
As we were talking about this, I couldn’t help but think of Bruce Schneier’s Liars and Outliers. I think this book provides a very helpful framework and language for reasoning about what, exactly, we might be trying to do as we promote fairness and nondiscrimination.