Equality of opportunity in supervised learning
Equality of opportunity in supervised learning Hardt et al., NIPS’16
With thanks to Rob Harrop for highlighting this paper to me.
There is a a lot of concern about discrimination and bias entering our machine learning models. Today’s paper choice introduces two notions of fairness: equalised odds, and equalised opportunity, and shows how to construct predictors that are fair under these criteria. One very appealing feature of the model is that in the case of uncertainty caused by under-representation in the training data, the cost of less accurate decision making in that demographic is moved from the protected class (who might otherwise for example not be offered loans), to the decision maker. I’m going to approach the paper backwards, and start with the case study, as I find a motivating example really helps with the intuition.
Loans, race, and FICO scores
We examine various fairness measures in the context of FICO scores with the protected attribute of race. FICO scores are a proprietary classifier widely used in the United States to predict credit worthiness. Our FICO data is based on a sample of 301,536 TransUnion TransRisk scores from 2003.
We’re interesting in comparing scores, the Continue reading