Machine Learning models have been deployed across many different aspects of society, often in situations that affect social welfare. Although these models offer streamlined solutions to large problems, they may contain biases and treat groups or individuals unfairly based on protected attributes such as gender. In this paper, we introduce several examples of machine learning gender bias in practice followed by formalizations of fairness. We provide a survey of fairness research by detailing influential pre-processing, in-processing, and post-processing bias mitigation algorithms. We then propose an \textup{end-to-end bias mitigation} framework, which employs a fusion of pre-, in-, and post-processing methods to leverage the strengths of each individual technique. We test this method, along with the standard techniques we review, on a deep neural network to analyze bias mitigation in a deep learning setting. We find that our end-to-end bias mitigation framework outperforms the baselines with respect to several fairness metrics, suggesting its promise as a method for improving fairness. As society increasingly relies on artificial intelligence to help in decision-making, addressing gender biases present in deep learning models is imperative. To provide readers with the tools to assess the fairness of machine learning models and mitigate the biases present in them, we discuss multiple open source packages for fairness in AI.
翻译:虽然这些模型为大规模问题提供了简化的解决办法,但它们可能含有偏见,并不公平地对待以性别等受保护属性为基础的群体或个人。在本文件中,我们介绍了几个在实践中学习机器性别偏见的实例,随后将公正化正规化。我们通过详细说明有影响力的预处理、处理和处理后减缓偏向的算法,对公平性研究进行了调查。然后我们提出了一个\textup{end-end-end end-end adute reformation}框架,该框架利用了预先、内部和后处理方法的结合,来利用每一种个别技术的优势。我们连同我们审查的标准技术一起测试这一方法,在深层学习环境中分析减少偏向的深神经网络。我们发现,我们的端对端减少偏向框架超越了若干公平性衡量法的基线,建议它作为改善公平性的方法。随着社会日益依赖人工智能来帮助决策,解决深层学习模型中存在的性别偏见问题。我们有必要为读者提供多种公平性的工具,用以评估机器学习模型的公平性。