  Fair machine learning works have been focusing on the development of
equitable algorithms that address discrimination of certain groups. Yet, many
of these fairness-aware approaches aim to obtain a unique solution to the
problem, which leads to a poor understanding of the statistical limits of bias
mitigation interventions. We present the first methodology that allows to
explore those limits within a multi-objective framework that seeks to optimize
any measure of accuracy and fairness and provides a Pareto front with the best
feasible solutions. In this work, we focus our study on decision tree
classifiers since they are widely accepted in machine learning, are easy to
interpret and can deal with non-numerical information naturally. We conclude
experimentally that our method can optimize decision tree models by being
fairer with a small cost of the classification error. We believe that our
contribution will help stakeholders of sociotechnical systems to assess how far
they can go being fair and accurate, thus serving in the support of enhanced
decision making where machine learning is used.
