Situation Testing-Based Discrimination Discovery: A Causal Inference Approach
Situation Testing-Based Discrimination Discovery: A Causal Inference Approach
复制标题
基于情境测试的歧视发现：因果推理方法
DOI：
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发表时间：
2016
期刊：
影响因子：
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通讯作者：
Xintao Wu
中科院分区：
文献类型：
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作者：
Lu Zhang;Yongkai Wu;Xintao Wu
歧视发现是通过分析历史数据集来公布对特定个人的歧视。在本文中，我们开发了一种一般技术，以基于合法的情况测试方法来捕获歧视。对于任何个人，我们从数据集中找到了成对的元素，除了归属或不归属于受保护的by-law组，并分为两组。如果两组的决策之间观察到很大的差异，则认为该人被认为是歧视的。为了找到类似的元素，我们利用因果贝叶斯网络和相关的因果推断作为指导。数据集的因果结构和每个属性对决策的因果效应用于促进相似性测量。通过对真实数据集的经验评估，我们的方法在准确性和效率方面都表现出良好的功效。
Discrimination discovery is to unveil discrimination against a specific individual by analyzing the historical dataset. In this paper, we develop a general technique to capture discrimination based on the legally grounded situation testing methodology. For any individual, we find pairs of tuples from the dataset with similar characteristics apart from belonging or not to the protected-by-law group and assign them in two groups. The individual is considered as discriminated if significant di?erence is observed between the decisions from the two groups. To find similar tuples, we make use of the Causal Bayesian Networks and the associated causal inference as a guideline. The causal structure of the dataset and the causal effect of each attribute on the decision are used to facilitate the similarity measurement. Through empirical assessments on a real dataset, our approach shows good efficacy both in accuracy and efficiency.
使用对数线性模型进行歧视发现和预防
DOI：
10.1109/dsaa.2016.18
发表时间：
2016
期刊：
2016 IEEE International Conference on Data Science and Advanced Analytics (DSAA
影响因子：
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作者：
Wu, Yongkai;Wu, Xintao
通讯作者：
Wu, Xintao