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A supervised machine learning algorithm determines a model from a learningsample that will be used to predict new observations. To this end, itaggregates individual characteristics of the observations of the learningsample. But this information aggregation does not consider any potentialselection on unobservables and any status-quo biases which may be contained inthe training sample. The latter bias has raised concerns around the so-called\textit{fairness} of machine learning algorithms, especially towardsdisadvantaged groups. In this chapter, we review the issue of fairness inmachine learning through the lenses of structural econometrics models in whichthe unknown index is the solution of a functional equation and issues ofendogeneity are explicitly accounted for. We model fairness as a linearoperator whose null space contains the set of strictly {\it fair} indexes. A{\it fair} solution is obtained by projecting the unconstrained index into thenull space of this operator or by directly finding the closest solution of thefunctional equation into this null space. We also acknowledge that policymakersmay incur a cost when moving away from the status quo. Achieving\textit{approximate fairness} is obtained by introducing a fairness penalty inthe learning procedure and balancing more or less heavily the influence betweenthe status quo and a full fair solution.

Fairness constraint in Structural Econometrics and Application to fair estimation using Instrumental Variables