Fair Feature Selection with a Lexicographic Multi-objective Genetic Algorithm

There is growing interest in learning from data classifiers whose predictions are both accurate and fair, avoiding discrimination against sub-groups of people based e.g. on gender or race. This paper proposes a new Lexicographic multi-objective Genetic Algorithm for Fair Feature Selection (LGAFFS). LGAFFS selects a subset of relevant features which is optimised for a given classification algorithm, by simultaneously optimising one measure of accuracy and four measures of fairness. This is achieved by using a lexicographic multi-objective optimisation approach where the objective of optimising accuracy has higher priority over the objective of optimising the four fairness measures. LGAFFS was used to select features in a pre-processing phase for a random forest algorithm. The experiments compared LGAFFS' performance against two feature selection approaches: (a) the baseline approach of letting the random forest algorithm use all features, i.e. no feature selection in a pre-processing phase; and (b) a Sequential Forward Selection method. The results showed that LGAFFS significantly improved fairness measures in several cases, with no significant difference regarding predictive accuracy, across all experiments.