A METHODOLOGY FOR DIRECT AND INDIRECT DISCRIMINATION PREVENTION IN DATA MINING

By M. Mohana, Mrs. K. Sumathi, C. Senthilraja. 2015

11 citations recorded by the source corpus.

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Fairness in Criminal Justice Risk Assessments: The State of the Art (2017, neutral): “As a first approximation, there are three different strategies (Hajian and Domingo-Ferrer 2013), although they can also be combined when accuracy as well as fairness are considered.”
A multidisciplinary survey on discrimination analysis (2013, methodology): “Using additional background knowledge, Hajian and Domingo-Ferrer (2012) perturb the training set so as to reduce the degree of indirect discrimination, which is measured in terms of the number of rules that could be inferred using the approach of Ruggieri et al. (2010a, 2010b).”
Situation Testing-Based Discrimination Discovery: A Causal Inference Approach (2016, background): “Discrimination discovery has been an active research area in data mining recently [Hajian and Domingo-Ferrer, 2013; Kamiran and Calders, 2012; Ruggieri et al., 2010; Romei and Ruggieri, 2014].”
On Discrimination Discovery Using Causal Networks (2016, background): “, association rules for group discrimination discovery [16, 17], situation testing for individual discrimination discovery [11], correlation analysis considering explanatory attributes for direct discrimination discovery [6, 25], rule inference for indirect discrimination discovery [5, 16], and fair”