MinMax fairness: from Rawlsian Theory of Justice to solution for algorithmic bias

Abstract This paper presents an intuitive explanation about why and how Rawlsian Theory of Justice (Rawls in A theory justice, Harvard University Press, Harvard, 1971) provides the foundations to a solution for algorithmic bias. The contribution is discuss show ideas their original form (e.g. veil ignorance , position allowing inequalities that serve worst-off) are relevant operationalize fairness decision making. also explains this leads specific MinMax solution, which addresses basic challenges justice. We combine substantive elements perspective with order provide accessible practical insights. goal propose motivate derived from principles overcomes some current bias highlight benefits provided when compared other approaches. discusses by building bridge between qualitative theoretical aspects quantitative technical approach.