  We address the problem of correcting group discriminations within a score
function, while minimizing the individual error. Each group is described by a
probability density function on the set of profiles. We first solve the problem
analytically in the case of two populations, with a uniform bonus-malus on the
zones where each population is a majority. We then address the general case of
n populations, where the entanglement of populations does not allow a similar
analytical solution. We show that an approximate solution with an arbitrarily
high level of precision can be computed with linear programming. Finally, we
address the inverse problem where the error should not go beyond a certain
value and we seek to minimize the discrimination.
