Technical / Methods Track
In this tutorial, we plan to describe a mathematical framework based on causal inference for helping the decision makers to choose a criterion that optimizes fairness in a principled and transparent fashion. We will start by introducing the principles of causal inference that will allow us to give a formal context for the causal fairness analysis problem. One key observation is that the data, that is usually the centerpiece of any fairness analysis, is the output of different causal processes, which despite the inherent lack of observability, are the mechanisms in the real-world that bring about the observed disparities. This implies that fairness analysis itself should not be concerned only with the data, which is a realization of these mechanisms, but also with the mechanisms themselves. In fact, the multiple fairness metrics currently available capture different aspects of these underlying mechanisms. We will discuss the causal fairness metrics for detection of bias and how to measure these in practice.