Delayed impact of fair machine learning
Delayed impact of fair machine learning Liu et al., ICML’18
“Delayed impact of fair machine learning” won a best paper award at ICML this year. It’s not an easy read (at least it wasn’t for me), but fortunately it’s possible to appreciate the main results without following all of the proof details. The central question is how to ensure fair treatment across demographic groups in a population when using a score-based machine learning model to decide who gets an opportunity (e.g. is offered a loan) and who doesn’t. Most recently we looked at the equal opportunity and equalized odds models.
The underlying assumption of course for studied fairness models is that the fairness criteria promote the long-term well-being of those groups they aim to protect. The big result in this paper is that you can easily up end ‘killing them with kindness’ instead. The potential for this to happen exists when there is a feedback loop in place in the overall system. By overall system here, I mean the human system of which the machine learning model is just a small part. Using the loan/no-loan decision that is a popular study vehicle in fairness papers, we need to Continue reading