一种量化边缘流和边缘不公平性的因果线性模型用于UnfairEdge优先级和歧视消除
A Causal Linear Model to Quantify Edge Flow and Edge Unfairness for UnfairEdge Prioritization and Discrimination Removal
摘要：
考虑到资源有限，执法部门必须优先处理不公平的根源，然后才能减轻其根本的不公平。与以往的作品，只有提出警告性的歧视和去偏见的数据生成后，本文试图优先考虑不公平的来源之前，减轻他们在现实世界中的不公平。我们假设一个因果baidu网络，代表的数据生成过程，以及敏感的节点，导致不公平，给出。我们量化边缘流，这是通过衰减间接路径影响沿着边缘流动的信念，并使用它来量化边缘不公平。我们证明，累积的不公平是不存在的任何决定，如司法保释，对任何敏感群体，如种族，当边缘不公平是缺席的，给出了一个无误差的线性模型的条件概率。然后，我们衡量的潜力，以减轻累积的不公平时，边缘不公平减少。基于这些措施，我们提出了一个不公平的边缘优先级算法，优先考虑不公平的边缘和歧视消除程序，去偏见生成的数据分布。实验部分验证了用于量化上述测量的规范。
英文摘要：
Law enforcement must prioritize sources of unfairness before mitigating their underlying unfairness, considering that they have limited resources. Unlike previous works that only make cautionary claims of discrimination and de-biases data after its generation, this paper attempts to prioritize unfair sources before mitigating their unfairness in the real-world. We assume that a causal bayesian network, representative of the data generation procedure, along with the sensitive nodes, that result in unfairness, are given. We quantify Edge Flow, which is the belief flowing along an edge by attenuating the indirect path influences, and use it to quantify Edge Unfairness. We prove that cumulative unfairness is non-existent in any decision, like judicial bail, towards any sensitive groups, like race, when the edge unfairness is absent, given an error-free linear model of conditional probability. We then measure the potential to mitigate the cumulative unfairness when edge unfairness is decreased. Based on these measures, we propose an unfair edge prioritization algorithm that prioritizes the unfair edges and a discrimination removal procedure that de-biases the generated data distribution. The experimental section validates the specifications used for quantifying the above measures.
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