Discrimination prevention in data mining for intrusion and crime detection

By Sara Hajian, Josep Domingo-Ferrer, Antoni Martínez-Ballesté. 2011

107 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems, Sociology and Political Science

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Citations in the corpus

European Union Regulations on Algorithmic Decision-Making and a "Right to Explanation" (2016, methodology): “Furthermore, a number of recent studies have attempted to tackle the issue of discrimination within algorithms by introducing tools to both identify [5, 29] and rectify [9, 16, 32, 6, 12, 14] cases of unwanted bias.”
Algorithmic Decision Making and the Cost of Fairness (2017, methodology): “i•ed as high risk meant they were subjected to harsher treatment by the courts. To reduce racial disparities of this kind, several authors recently have proposed a variety of fair decision algorithms [16, 21, 22, 24, 25, 27].1 Here we reformulate algorithmic fairness as constrained optimization: the”
Exploring Discrimination: A User-centric Evaluation of Discrimination-Aware Data Mining (2012, methodology): “Prevention is realised by a number of pre-processing and in-processing methods for DADM, and identiﬁcation/ﬁltering by a number of post-processing methods, e.g., [5-10].”
Quantifying explainable discrimination and removing illegal discrimination in automated decision making (2013, methodology): “The authors of [20,21] also propose methods similar to local massaging to preprocess the training data in such a way that only potentially non-discriminatory rules can be extracted.”
Rule Protection for Indirect Discrimination Prevention in Data Mining (2011, methodology): “In [ 10 ] and [11] we concentrated on direct discrimination and considered α-discriminatory rules.”
Discrimination-aware data mining: a survey (2017, methodology): “Hajian et al. (2011) have stated that pre-processing techniques described by Hajian and Domingo-Ferrer (2013) for discrimination prevention are better than pre-processing techniques developed by Kamiran and Calders (2012), because of the following two reasons: • The discrimination prevention…”
Better decision support through exploratory discrimination-aware data mining: foundations and empirical evidence (2014, background): “4 see for example [30,27] for measures of utility”
A Methodology for Direct and Indirect Discrimination Prevention in Data Mining (2013, background): “In our earlier work [5], we introduced the initial idea of using rule protection and rule generalization for direct discrimination prevention, but we gave no experimental results.”