In this talk I will give an overview of the role of causality in ethical ML, and in particular, in fair and explainable ML. In particular, I will first detail how to use causal reasoning to study fairness and interpretability problems in algorithmic decision making, stressing the main limitations that we encounter when aiming to address these problems in practice. Then, I will introduce some technical solution to address these limitations in realistic scenarios where the causal knowledge is only limited.
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