Integrating induction and deduction for finding evidence of discrimination

By Salvatore Ruggieri, Dino Pedreschi, Franco Turini. 2009

97 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems, Political Science and International Relations

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Citations in the corpus

Data preprocessing techniques for classification without discrimination (2011, background): “In Discrimination-Aware Data Mining two main directions can be distinguished: detection of discrimination [21–24], and the direction followed in this paper, namely learning classifiers if the data are discriminatory [4,15].”
Exposing the probabilistic causal structure of discrimination (2015, background): “[29,38] adopt a form of rule inference to cope with the indirect discovery of discrimination.”
A Methodology For Direct and Indirect Discrimination Prevention In Data Mining (2013, background): “This evolution has been absolute to include statistical importance of the extracted patterns of discrimination in [13] and to reason about affirmative action and favoritism [14].”
Discrimination prevention in data mining for intrusion and crime detection (2011, methodology): “According to Figure 1, let MRs be the database of αdiscriminatory rules extracted from database DB. 2Note that α is a fixed threshold stating an acceptable level of discrimination according to laws and regulations.”
Discovering Gender Discrimination in Project Funding (2012, methodology): “8 s4 Scores S4 received at the peer-review Numeric [1, 10] 8.”
Better decision support through exploratory discrimination-aware data mining: foundations and empirical evidence (2014, background): “DCUBE (Ruggieri et al. 2010) and LP2DD (Pedreschi et al. 2009) are systems that focus on detecting all assumption-based bad patterns.”
Discrimination-aware data mining: a survey (2017, methodology): “The terminologies as defined by Ruggieri et al. (2010a) and Pedreschi et al. (2009a) are mentioned here: • A data item is said to be potentially discriminatory (PD) if it is decided as discriminatory according to laws and regulations.”
Exploring Discrimination: A User-centric Evaluation of Discrimination-Aware Data Mining (2012, background): “DCUBE [12] and LP2DD [13] are systems that focus on detecting all assumption-based bad patterns.”
Discovering Unwarranted Associations in Data-Driven Applications with the FairTest Testing Toolkit (2015, methodology): “, favored and disfavored status [7, 16, 20, 30, 31, 38, 42, 47, 48, 56, 57], or are instead difficult to test using a sample [12]; (2) They ignore disparities in user subpopulations [7, 16, 30–32, 57]; (3) They fail to consider reasonable explanations that may negate the significance of an associati”
A Methodology for Direct and Indirect Discrimination Prevention in Data Mining (2013, background): “Services in the information society allow for automatic and routine collection of large amounts of data.”
Certifying and Removing Disparate Impact (2014, background): “[14] D. Pedreschi, S. Ruggieri, and F. Turini.”
FA*IR: A Fair Top-k Ranking Algorithm (2017, background): “3 In general these measures do not mandate perfect parity, as distributions of quali cations across groups can be unbalanced for legitimate, explainable reasons [29, 39].”
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.”
Discrimination- and privacy-aware patterns (2015, methodology): “Pedreschi et al. (2009a) alter the confidence of classification rules inferred by the CPAR algorithm.”
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;…”
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].”
Rule Protection for Indirect Discrimination Prevention in Data Mining (2011, background): “Some proposals are oriented to the discovery and measure of discrimination [1,3,4,7].”