  In the literature of mitigating unfairness in machine learning, many fairness
measures are designed to evaluate predictions of learning models and also
utilised to guide the training of fair models. It has been theoretically and
empirically shown that there exist conflicts and inconsistencies among accuracy
and multiple fairness measures. Optimising one or several fairness measures may
sacrifice or deteriorate other measures. Two key questions should be
considered, how to simultaneously optimise accuracy and multiple fairness
measures, and how to optimise all the considered fairness measures more
effectively. In this paper, we view the mitigating unfairness problem as a
multi-objective learning problem considering the conflicts among fairness
measures. A multi-objective evolutionary learning framework is used to
simultaneously optimise several metrics (including accuracy and multiple
fairness measures) of machine learning models. Then, ensembles are constructed
based on the learning models in order to automatically balance different
metrics. Empirical results on eight well-known datasets demonstrate that
compared with the state-of-the-art approaches for mitigating unfairness, our
proposed algorithm can provide decision-makers with better tradeoffs among
accuracy and multiple fairness metrics. Furthermore, the high-quality models
generated by the framework can be used to construct an ensemble to
automatically achieve a better tradeoff among all the considered fairness
metrics than other ensemble methods. Our code is publicly available at
https://github.com/qingquan63/FairEMOL
