What is the Chance of Being so Unfair?

Fairness has often been seen as an ethical concern that needs to be considered at some cost on the utility. In contrast, in this work, we formulate fairness, and especially fairness in ranking, as a way to avoid unjust biases and provide a more accurate ranking that results in improvement on the actual unbiased utility. With this in mind, we design a fairness measure that, instead of blindly forcing some approximate equality constraint, checks if the outcome is plausible in a just world. Our fairness measure asks a simple and fundamental statistical question: ''What is the chance of observing this outcome in an unbiased world?''. If the chance is high enough, the outcome is fair. We provide a dynamic programming algorithm that, given a ranking, calculates our fairness measure. Secondly, given a sequence of potentially biased scores, along with the sensitive feature, we provide a fair ranking algorithm based on our fairness measure. Finally, we run some experiments to understand the behavior of our ranking algorithm against other fundamental algorithms.