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Causal Discovery Under Local Privacy
It is based on the application of controlled noise at the interface between the server that stores and processes the data, and the data consumers.
Dissecting Causal Biases
Accurately measuring discrimination in machine learning-based automated decision systems is required to address the vital issue of fairness between subpopulations and/or individuals.
Shedding light on underrepresentation and Sampling Bias in machine learning
Sampling bias, is inconsistently used in the literature to describe bias due to the sampling procedure.
On the Need and Applicability of Causality for Fair Machine Learning
Besides its common use cases in epidemiology, political, and social sciences, causality turns out to be crucial in evaluating the fairness of automated decisions, both in a legal and everyday sense.
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