Combating discrimination using Bayesian networks
Combating discrimination using Bayesian networks
复制标题
使用贝叶斯网络打击歧视
DOI：
--
复制
发表时间：
2014
影响因子：
4.1
通讯作者：
Chris Clifton
中科院分区：
文献类型：
--
作者：
Koray Mancuhan;Chris Clifton
在许多属性（宗教，性别等）上，禁止在决策中进行歧视，但在历史决策中常常存在。数据。在使用估计的概率分布的数据集（通过贝叶斯网络）的一个子集中，我们提出了一种对发现的歧视的分类方法，而无需在决策过程中使用受保护的属性。两个不同的数据集。
Discrimination in decision making is prohibited on many attributes (religion, gender, etc…), but often present in historical decisions. Use of such discriminatory historical decision making as training data can perpetuate discrimination, even if the protected attributes are not directly present in the data. This work focuses on discovering discrimination in instances and preventing discrimination in classification. First, we propose a discrimination discovery method based on modeling the probability distribution of a class using Bayesian networks. This measures the effect of a protected attribute (e.g., gender) in a subset of the dataset using the estimated probability distribution (via a Bayesian network). Second, we propose a classification method that corrects for the discovered discrimination without using protected attributes in the decision process. We evaluate the discrimination discovery and discrimination prevention approaches on two different datasets. The empirical results show that a substantial amount of discrimination identified in instances is prevented in future decisions.