一句话：提出基于 K 近邻的可信解释方法，揭示黑盒 ML 模型的潜在偏见，弥补现有偏见度量与解释方法的不足。
机器学习软件广泛应用于金融、招聘、招生和刑事司法等具有重大社会影响的场景，但其预测可能基于性别、种族等敏感属性产生偏见与歧视。已有工作主要关注发现和缓解 ML 模型中的偏见，近期则用 LIME 等基于实例的模型无关解释方法检测预测偏见。本文指出现有偏见度量与解释方法存在不足，并提出一种基于 K 最近邻（KNN）的方法来克服这些不足，从而发现黑盒模型背后的潜在偏见。作者表示其结果更可信、对实践者更有帮助，并展望了一个结合解释与规划以构建公平软件的框架。
Machine learning software is being used in many applications (finance, hiring, admissions, criminal justice) having 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[36] 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.