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Causal inference for social discrimination reasoning
Journal of Intelligent Information Systems ( IF 3.4 ) Pub Date : 2019-11-07 , DOI: 10.1007/s10844-019-00580-x
Bilal Qureshi , Faisal Kamiran , Asim Karim , Salvatore Ruggieri , Dino Pedreschi

The discovery of discriminatory bias in human or automated decision making is a task of increasing importance and difficulty, exacerbated by the pervasive use of machine learning and data mining. Currently, discrimination discovery largely relies upon correlation analysis of decisions records, disregarding the impact of confounding biases. We present a method for causal discrimination discovery based on propensity score analysis , a statistical tool for filtering out the effect of confounding variables. We introduce causal measures of discrimination which quantify the effect of group membership on the decisions, and highlight causal discrimination/favoritism patterns by learning regression trees over the novel measures. We validate our approach on two real world datasets. Our proposed framework for causal discrimination has the potential to enhance the transparency of machine learning with tools for detecting discriminatory bias both in the training data and in the learning algorithms.

中文翻译:

社会歧视推理的因果推理

在人类或自动决策中发现歧视性偏见是一项越来越重要和困难的任务,由于机器学习和数据挖掘的普遍使用而加剧。目前,歧视发现主要依赖于决策记录的相关分析,而忽略了混杂偏见的影响。我们提出了一种基于倾向评分分析的因果歧视发现方法,这是一种过滤混杂变量影响的统计工具。我们引入了歧视的因果度量,量化了群体成员对决策的影响,并通过学习新度量的回归树来突出因果歧视/偏爱模式。我们在两个真实世界的数据集上验证了我们的方法。
更新日期:2019-11-07
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