k-NN as an implementation of situation testing for discrimination discovery and prevention
k-NN as an implementation of situation testing for discrimination discovery and prevention
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k-NN 作为情境测试的实现，以发现和预防歧视
DOI：
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发表时间：
2011
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通讯作者：
F. Turini
中科院分区：
文献类型：
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作者：
Binh Luong Thanh;S. Ruggieri;F. Turini
在基于情境测试的合法方法的支持下，我们通过采用一种k - 近邻（k - NN）分类变体，从历史决策数据集中解决歧视发现和预防的问题。如果我们能观察到一个元组在属于受法律保护群体的邻居和不属于该群体的邻居之间存在显著的处理差异，那么这个元组就被标记为受到歧视。歧视发现归结为从标记的元组中提取一个分类模型。歧视预防是通过在训练分类器之前改变被标记为受到歧视的元组的决策值来解决的。本文的方法克服了现有方案的法律缺陷和技术局限性。
With the support of the legally-grounded methodology of situation testing, we tackle the problems of discrimination discovery and prevention from a dataset of historical decisions by adopting a variant of k-NN classification. A tuple is labeled as discriminated if we can observe a significant difference of treatment among its neighbors belonging to a protected-by-law group and its neighbors not belonging to it. Discrimination discovery boils down to extracting a classification model from the labeled tuples. Discrimination prevention is tackled by changing the decision value for tuples labeled as discriminated before training a classifier. The approach of this paper overcomes legal weaknesses and technical limitations of existing proposals.