Alexey Miroshnikov, Discover Financial Services, Wasserstein-based fairness interpretability framework for machine learning models
SAS 4201The objective of this talk is to introduce a fairness interpretability framework for measuring and explaining the bias in classification and regression models at the level of a regressor distribution. In our work, we measure the model bias across sub-population distributions in the model output using the Wasserstein metric. To properly quantify the contributions of…