A study of top-k measures for discrimination discovery
A study of top-k measures for discrimination discovery
复制标题
歧视发现的top-k措施研究
DOI：
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发表时间：
2012
期刊：
影响因子：
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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：
D. Pedreschi;S. Ruggieri;F. Turini
歧视发现的数据挖掘方法通过从历史决策记录的数据集中提取分类规则来揭示可能歧视受法律保护的群体的背景。规则是根据一个4重列联表中定义的一些合法的对比度量进行排名的，包括风险差异，风险比，比值比和其他一些。然而，由于时间和成本的限制，反歧视分析师只进一步考虑排名前k的规则。在本文中，我们研究在何种程度上的top-k排名规则的任何两对措施同意。
Data mining approaches for discrimination discovery unveil contexts of possible discrimination against protected-by-law groups by extracting classification rules from a dataset of historical decision records. Rules are ranked according to some legally-grounded contrast measure defined over a 4-fold contingency table, including risk difference, risk ratio, odds ratio, and a few others. Due to time and cost constraints, however, only the top-k ranked rules are taken into further consideration by an anti-discrimination analyst. In this paper, we study to what extent the sets of top-k ranked rules with respect to any two pairs of measures agree.