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In this paper, I argue that counterfactual fairness does not constitute anecessary condition for an algorithm to be fair, and subsequently suggest howthe constraint can be modified in order to remedy this shortcoming. To thisend, I discuss a hypothetical scenario in which counterfactual fairness and anintuitive judgment of fairness come apart. Then, I turn to the question how theconcept of discrimination can be explicated in order to examine theshortcomings of counterfactual fairness as a necessary condition of algorithmicfairness in more detail. I then incorporate the insights of this analysis intoa novel fairness constraint, causal relevance fairness, which is a modificationof the counterfactual fairness constraint that seems to circumvent itsshortcomings.

Shortcomings of Counterfactual Fairness and a Proposed Modification