Anti-discrimination learning: a causal modeling-based framework
Anti-discrimination learning: a causal modeling-based framework
复制标题
反歧视学习：基于因果建模的框架
DOI：
10.1007/s41060-017-0058-x
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发表时间：
2017
影响因子：
2.4
通讯作者：
Wu, Xintao
中科院分区：
文献类型：
--
作者：
Zhang, Lu;Wu, Xintao
反歧视学习是数据挖掘中越来越重要的任务。歧视发现是通过分析历史决策记录的数据集来揭示歧视性做法的问题，而歧视预防旨在通过修改有偏见的数据和/或预测算法来消除歧视。歧视是因果关系，这意味着要证明歧视，需要推导出因果关系，而不是关联关系。虽然关联并不意味着因果关系是众所周知的，但关联与因果关系之间的差距却没有得到许多研究者的足够重视。在本文中，我们介绍了一个因果建模为基础的反歧视学习的框架。歧视分为两个方面：直接/间接和系统/群体/个人。在因果框架内，我们介绍了一个工作，发现和防止直接和间接的系统级歧视的训练数据，和一个工作，从训练数据的非歧视性结果扩展到预测。然后，我们介绍了两个工作组水平的直接歧视和个人水平的直接歧视。本文的目的是从因果建模的角度加深对数据挖掘中歧视的理解，并提出几个潜在的未来研究方向。
Anti-discrimination learning is an increasingly important task in data mining. Discrimination discovery is the problem of unveiling discriminatory practices by analyzing a dataset of historical decision records, and discrimination prevention aims to remove discrimination by modifying the biased data and/or the predictive algorithms. Discrimination is causal, which means that to prove discrimination one needs to derive a causal relationship rather than an association relationship. Although it is well known that association does not mean causation, the gap between association and causation is not paid enough attention by many researchers. In this paper, we introduce a causal modeling-based framework for anti-discrimination learning. Discrimination is categorized according to two dimensions: direct/indirect and system/group/individual level. Within the causal framework, we introduce a work for discovering and preventing both direct and indirect system-level discrimination in the training data, and a work for extending the non-discrimination result from the training data to prediction. We then introduce two works for group-level direct discrimination and individual-level direct discrimination respectively. The aim of this paper is to deepen the understanding of discrimination in data mining from the causal modeling perspective, and suggest several potential future research directions.
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使用贝叶斯网络打击歧视
影响因子：
4.1
作者：
Koray Mancuhan;Chris Clifton
通讯作者：
Chris Clifton
DOI：
10.1145/1401890.1401959
发表时间：
2008-08
期刊：
ArXiv
影响因子：
--
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DOI：
--
发表时间：
2005
期刊：
影响因子：
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作者：
C. Avin;I. Shpitser
通讯作者：
I. Shpitser
DOI：
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发表时间：
2016
期刊：
International Joint Conference on Artificial Intelligence
影响因子：
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作者：
Lu Zhang;Yongkai Wu;Xintao Wu
通讯作者：
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实现数据发布中的非歧视
DOI：
10.1145/3097983.3098167
发表时间：
2017
期刊：
the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD
影响因子：
--
作者：
Zhang, Lu;Wu, Yongkai;Wu, Xintao
通讯作者：
Wu, Xintao