Aequitas – An open source bias audit toolkit for machine learning

dsapp.uchicago.edu

46 points·lainon··5 comments

I find the premise that different groups should expect the same percentage of interventions highly suspect. Imagine we have a program that distributes seeing eye dogs. This toolkit would discover that sighted persons have a 0% chance of getting a dog, while blind persons have a 50% chance. Oh the injustice!

FairML https://github.com/adebayoj/fairml and algofairness https://github.com/algofairness/BlackBoxAuditing are some similar, earlier projects in the same space.

If the goal is equity of outcome above-all-else, ignoring for any differences derived from the data, then why are we bothered investing so much time, money and effort into this area?

See https://www.propublica.org/article/machine-bias-risk-assessm... for a well-known example in this space.

This article: https://www.nature.com/articles/d41586-018-05469-3 also highlights the issues pretty well IMO.

It's not just about letting the data speak. The data is gathered by someone, containing historical bias we might nowadays disagree with, models are chosen by people, using parameters set by people, using evaluations set by people. It's about making sure models are both predictive, usable, and fair.

Predictive, usable and fair. Pick any two!

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