Machine learning software is increasingly being used to make decisions that affect people's lives. But sometimes, the core part of this software (the learned model), behaves in a biased manner that gives undue advantages to a specific group of people (where those groups are determined by sex, race, etc.). This "algorithmic discrimination" in the AI software systems has become a matter of serious concern in the machine learning and software engineering community. There have been works done to find "algorithmic bias" or "ethical bias" in the software system. Once the bias is detected in the AI software system, the mitigation of bias is extremely important. In this work, we a)explain how ground-truth bias in training data affects machine learning model fairness and how to find that bias in AI software,b)propose a methodFairwaywhich combines pre-processing and in-processing approach to remove ethical bias from training data and trained model. Our results show that we can find bias and mitigate bias in a learned model, without much damaging the predictive performance of that model. We propose that (1) test-ing for bias and (2) bias mitigation should be a routine part of the machine learning software development life cycle. Fairway offers much support for these two purposes.

中文翻译：

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Fairway：一种构建公平机器学习软件的方法

机器学习软件越来越多地用于做出影响人们生活的决策。但有时，该软件的核心部分（学习模型）会以有偏见的方式运行，为特定人群（这些人群由性别、种族等决定）提供不应有的优势。AI 软件系统中的这种“算法歧视”已成为机器学习和软件工程界严重关注的问题。已经做了一些工作来发现软件系统中的“算法偏差”或“道德偏差”。一旦在 AI 软件系统中检测到偏差，缓解偏差就变得极为重要。在这项工作中，我们 a) 解释了训练数据中的真实偏差如何影响机器学习模型的公平性，以及如何在 AI 软件中找到这种偏差，b) 提出一种方法Fairway，它结合了预处理和处理中的方法，以消除训练数据和训练模型中的道德偏见。我们的结果表明，我们可以在学习模型中找到偏差并减轻偏差，而不会对该模型的预测性能造成太大损害。我们建议 (1) 测试偏差和 (2) 偏差缓解应该是机器学习软件开发生命周期的常规部分。Fairway 为这两个目的提供了很多支持。我们建议 (1) 测试偏差和 (2) 偏差缓解应该是机器学习软件开发生命周期的常规部分。Fairway 为这两个目的提供了很多支持。我们建议 (1) 测试偏差和 (2) 偏差缓解应该是机器学习软件开发生命周期的常规部分。Fairway 为这两个目的提供了很多支持。