Increasingly, software is making autonomous decisions in case of criminal sentencing, approving credit cards, hiring employees, and so on. Some of these decisions show bias and adversely affect certain social groups (e.g. those defined by sex, race, age, marital status). Many prior works on bias mitigation take the following form: change the data or learners in multiple ways, then see if any of that improves fairness. Perhaps a better approach is to postulate root causes of bias and then applying some resolution strategy. This paper postulates that the root causes of bias are the prior decisions that affect- (a) what data was selected and (b) the labels assigned to those examples. Our Fair-SMOTE algorithm removes biased labels; and rebalances internal distributions such that based on sensitive attribute, examples are equal in both positive and negative classes. On testing, it was seen that this method was just as effective at reducing bias as prior approaches. Further, models generated via Fair-SMOTE achieve higher performance (measured in terms of recall and F1) than other state-of-the-art fairness improvement algorithms. To the best of our knowledge, measured in terms of number of analyzed learners and datasets, this study is one of the largest studies on bias mitigation yet presented in the literature.

翻译：在刑事判决、批准信用卡、雇用雇员等情况下,软件正在越来越多地作出自主决定。其中一些决定显示偏见,对某些社会群体(例如,按照性别、种族、年龄、婚姻状况定义的)有不利影响。许多先前关于减少偏见的工作采取的形式如下:以多种方式改变数据或学习者,然后看看是否有这种方法可以改善公平性。也许一种更好的办法是假定偏见的根源,然后适用某种解决战略。本文假定偏见的根源是影响(a) 哪些数据被选取的先前决定,以及(b) 分配给这些例子的标签。我们的公平-SMOTE算法删除了有偏见的标签;重新平衡内部分布,例如基于敏感属性的分布,在正和负两个班上都一样。在测试中,人们认为这种方法在减少偏见方面与以前的做法一样有效。此外,通过公平-SMOTE产生的模型取得了比其他州级改进公平性算法更高的业绩(从回顾和F1角度衡量 ) 。我们的最佳知识是,在分析学习者和文献研究中衡量的最大偏差率。