%0 Generic %1 chiappa2019causal %A Chiappa, Silvia %A Isaac, William S. %D 2019 %K causality %R 10.1007/978-3-030-16744-8_1 %T A Causal Bayesian Networks Viewpoint on Fairness %U http://arxiv.org/abs/1907.06430 %X We offer a graphical interpretation of unfairness in a dataset as the presence of an unfair causal path in the causal Bayesian network representing the data-generation mechanism. We use this viewpoint to revisit the recent debate surrounding the COMPAS pretrial risk assessment tool and, more generally, to point out that fairness evaluation on a model requires careful considerations on the patterns of unfairness underlying the training data. We show that causal Bayesian networks provide us with a powerful tool to measure unfairness in a dataset and to design fair models in complex unfairness scenarios.

@misc{chiappa2019causal, abstract = {We offer a graphical interpretation of unfairness in a dataset as the presence of an unfair causal path in the causal Bayesian network representing the data-generation mechanism. We use this viewpoint to revisit the recent debate surrounding the COMPAS pretrial risk assessment tool and, more generally, to point out that fairness evaluation on a model requires careful considerations on the patterns of unfairness underlying the training data. We show that causal Bayesian networks provide us with a powerful tool to measure unfairness in a dataset and to design fair models in complex unfairness scenarios.}, added-at = {2019-10-09T13:03:47.000+0200}, author = {Chiappa, Silvia and Isaac, William S.}, biburl = {https://www.bibsonomy.org/bibtex/230542c2093d7bbf01f2a664539160403/stuart10}, description = {A Causal Bayesian Networks Viewpoint on Fairness}, doi = {10.1007/978-3-030-16744-8_1}, interhash = {fd3888f60059ef38cd31bcb29d9c79ea}, intrahash = {30542c2093d7bbf01f2a664539160403}, keywords = {causality}, note = {cite arxiv:1907.06430}, timestamp = {2019-10-09T13:03:47.000+0200}, title = {A Causal Bayesian Networks Viewpoint on Fairness}, url = {http://arxiv.org/abs/1907.06430}, year = 2019 }