DCUBE: discrimination discovery in databases

By Salvatore Ruggieri, Dino Pedreschi, Franco Turini. 2010

80 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems, Management Science and Operations Research

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Citations in the corpus

Data preprocessing techniques for classification without discrimination (2011, background): “We consider related work in Discrimination-Aware Data Mining itself, cost-sensitive classiﬁcation, constraint-based classiﬁcation, and sampling techniques for unbalanced datasets.”
Discovering Gender Discrimination in Project Funding (2012, methodology): “The approach has been implemented on top of an Oracle database by relying on tools for frequent itemset mining [9], and extended in [10] to take into account statistical significance of the discrimination measures.”
Algorithmic Bias: From Discrimination Discovery to Fairness-aware Data Mining (2016, methodology): “6 Tools: DCUBE [26], FairTest Testing Toolkit [27].”
Discrimination-aware classification (2011, background): “In Discrimination-Aware Data Mining, two main research directions can be distinguished: the detection of discrimination [64, 66, 73, 65, 74, 58, 72] and the direction followed in this thesis, namely learning discrimination-free classifiers if the data is discriminatory.”
Fairness-Aware Classifier with Prejudice Remover Regularizer (2012, background): “A system for finding such unfair rules has been proposed [20].”
A Methodology for Direct and Indirect Discrimination Prevention in Data Mining (2013, methodology): “Those data are often used to train association/classification rules in view of making automated decisions, like loan granting/denial, insurance premium computation, personnel selection, etc.”
Prediction with Model-Based Neutrality (2013, background): “Several techniques that take account of fairness or discrimination have recently received attention [4][6][12].”
Exploring Discrimination: A User-centric Evaluation of Discrimination-Aware Data Mining (2012, methodology): “We determined critical items by mining for rules with DCUBE [12], ﬁxing the item “foreign worker” as the PD item, and used default settings of rule interestingness measures. explained in Section II-D, risk scores were the χ 2 values of items with the target outcome (a successful loan) within this…”
Explainable and Non-explainable Discrimination in Classification (2013, background): “The studies by (Pedreschi et al., 2008, 2009; Ruggieri et al., 2010) aim at the detection of discrimination from training data and identify the potentially discriminatory classification rules.”
Discrimination Prevention using Privacy Preserving Techniques (2015, methodology): “The tool DCUBE has been generated to discover discrimination [10].”
Discrimination prevention in data mining for intrusion and crime detection (2011, background): “• Discrimination discovery is based on formalizing legal definitions of discrimination1 and proposing quantitative measures for it.”
Quantifying explainable discrimination and removing illegal discrimination in automated decision making (2013, background): “In discrimination-aware data mining there are two important directions to work on: discrimination discovery from the given datasets [20,21,32,33,35,36] and the discrimination prevention from the future decision making [7,23–26,47].”
Fairness-aware Learning through Regularization Approach (2011, background): “A system for finding such unfair rules has been proposed [9].”
Better decision support through exploratory discrimination-aware data mining: foundations and empirical evidence (2014, methodology): “DCUBEGUI employs methods from constraint-oriented DADM (more specifically, it builds on rules mined by DCUBE [54]) and complements them by risk scores defined on items or item pairs.”
A multidisciplinary survey on discrimination analysis (2013, methodology): “Extensions of the approach by Pedreschi et al. (2009) and Ruggieri et al. (2010a) deal with: (1) any measure built from a contingency table as in Figure 1; (2) the statistical significance of measures of discrimination of an extracted rule; (3) the discovery of affirmative actions and favoritism;…”