Machine learning classifiers are widely used to make decisions with a major
impact on people's lives (e.g. accepting or denying a loan, hiring decisions,
etc). In such applications,the learned classifiers need to be both accurate and
fair with respect to different groups of people, with different values of
variables such as sex and race. This paper focuses on fair feature selection
for classification, i.e. methods that select a feature subset aimed at
maximising both the accuracy and the fairness of the predictions made by a
classifier. More specifically, we compare two recently proposed Genetic
Algorithms (GAs) for fair feature selection that are based on two different
multi-objective optimisation approaches: (a) a Pareto dominance-based GA; and
(b) a lexicographic optimisation-based GA, where maximising accuracy has higher
priority than maximising fairness. Both GAs use the same measures of accuracy
and fairness, allowing for a controlled comparison. As far as we know, this is
the first comparison between the Pareto and lexicographic approaches for fair
classification. The results show that, overall, the lexicographic GA
outperformed the Pareto GA with respect to accuracy without degradation of the
fairness of the learned classifiers. This is an important result because at
present nearly all GAs for fair classification are based on the Pareto
approach, so these results suggest a promising new direction for research in
this area.

该研究论文研究了公平分类中的特征选择问题，通过比较两种基于遗传算法的多目标优化方法，即帕累托支配型遗传算法和词典优化型遗传算法，发现词典优化型遗传算法在提升准确性的同时，不降低分类器的公平性，为公平分类研究指明了一条有前景的新方向。