The Effects of Changing the Causal Model of a Counterfactually Fair Predictor
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2019-06-01
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en
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Abstract
The body of research on fairness in machine learning has been growing
in recent years. One de nition of fairness that has been proposed is that
of counterfactual fairness, in which one tries to model and take into ac-
count how variables in
uence each other in a biased world. This is done
via a causal model, which needs to be assumed. This thesis investigates
the e ects on the outcomes of a counterfactually fair predictor, when its
underlying causal model is changed.
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Faculteit der Sociale Wetenschappen