Given a discriminating neural network, the problem of fairness improvement is to systematically reduce discrimination without significantly scarifies its performance (i.e., accuracy). Multiple categories of fairness improving methods have been proposed for neural networks, including pre-processing, in-processing and post-processing. Our empirical study however shows that these methods are not always effective (e.g., they may improve fairness by paying the price of huge accuracy drop) or even not helpful (e.g., they may even worsen both fairness and accuracy). In this work, we propose an approach which adaptively chooses the fairness improving method based on causality analysis. That is, we choose the method based on how the neurons and attributes responsible for unfairness are distributed among the input attributes and the hidden neurons. Our experimental evaluation shows that our approach is effective (i.e., always identify the best fairness improving method) and efficient (i.e., with an average time overhead of 5 minutes).

翻译：鉴于神经网络存在歧视性的神经网络,公平改善的问题是系统性地减少歧视,而不会严重削弱其性能(即准确性); 已经为神经网络提出了多种类别的公平改进方法,包括预处理、处理和后处理。然而,我们的经验研究表明,这些方法并不总是有效的(例如,它们可能通过支付高精度下降的代价来提高公平性),甚至没有帮助(例如,它们甚至可能使公平和准确性更加恶化)。在这项工作中,我们建议了一种适应性地选择基于因果关系分析的公平改进方法的方法。也就是说,我们选择的方法是基于输入属性和隐藏的神经元之间如何分配的神经元和对不公平负责的属性。我们的实验评估表明,我们的方法是有效的(即,总是确定最佳的公平改进方法)和效率(即平均时间5分钟)。