  In this paper, I argue that counterfactual fairness does not constitute a
necessary condition for an algorithm to be fair, and subsequently suggest how
the constraint can be modified in order to remedy this shortcoming. To this
end, I discuss a hypothetical scenario in which counterfactual fairness and an
intuitive judgment of fairness come apart. Then, I turn to the question how the
concept of discrimination can be explicated in order to examine the
shortcomings of counterfactual fairness as a necessary condition of algorithmic
fairness in more detail. I then incorporate the insights of this analysis into
a novel fairness constraint, causal relevance fairness, which is a modification
of the counterfactual fairness constraint that seems to circumvent its
shortcomings.

Machine Learning;Artificial Intelligence;Machine Learning