A Causal Bayesian Networks Viewpoint on Fairness
Silvia Chiappa, William S. Isaac
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.