使用可信解释制作公平的ML软件
Making Fair ML Software using Trustworthy Explanation
摘要：
机器学习软件被用于许多应用（金融、招聘、招生、刑事司法），产生了巨大的社会影响。但有时这种软件的行为是有偏见的，它显示出基于一些敏感属性（如性别、种族等）的歧视。最近的趋势是使用基于实例的模型不可知解释方法，如LIME，以找出模型预测中的偏差。我们的工作集中在寻找现有的偏差措施和解释方法的缺点。我们展示了我们提出的基于K近邻的方法如何克服这些缺点，并找到黑盒模型的潜在偏差。我们的研究结果更值得信赖，对从业人员更有帮助。最后，我们描述了我们未来的框架结合解释和规划，以建立公平的软件。
英文摘要：
Machine learning software is being used in many applications (finance, hiring, admissions, criminal justice) having a huge social impact. But sometimes the behavior of this software is biased and it shows discrimination based on some sensitive attributes such as sex, race, etc. Prior works concentrated on finding and mitigating bias in ML models. A recent trend is using instance-based model-agnostic explanation methods such as LIME to find out bias in the model prediction. Our work concentrates on finding shortcomings of current bias measures and explanation methods. We show how our proposed method based on K nearest neighbors can overcome those shortcomings and find the underlying bias of black-box models. Our results are more trustworthy and helpful for the practitioners. Finally, We describe our future framework combining explanation and planning to build fair software.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
