Classification Without Discrimination

By Faisal Kamiran, Toon Calders. 2009

60 citations recorded by the source corpus.

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Citations in the corpus

Discrimination prevention in data mining for intrusion and crime detection (2011, methodology): “Information technologies could play an important role in discrimination discovery and prevention (i.e. anti-discrimination [5], [6]).”
Measuring discrimination in algorithmic decision making (2017, methodology): “Difference in proportions, although not formally used as a statistical test, has been used in a number of data mining studies (Calders and Verwer 2010; Kamiran and Calders 2009; Kamiran et al. 2010; Pedreschi et al. 2009; Zemel et al. 2013).”
Learning under Concept Drift: an Overview (2010, background): “However, a dataset shift can have a number of other reasons, for instance, sample selection bias [11], domain shift due to changes in measurements, model shift due to imbalance of data [125], discrimination in decision making [71], which are out of the scope of this thesis.”
Integrating induction and deduction for finding evidence of discrimination (2009, background): “A first approach, dealing with data mining classifiers, is reported in [13].”
Measuring Discrimination in Socially-Sensitive Decision Records (2009, methodology): “We mention here that an orthogonal approach, based on massaging the training set, is presented in [11].”
A Methodology for Direct and Indirect Discrimination Prevention in Data Mining (2013, methodology): “One must prevent data mining from becoming itself a source of discrimination, due to data mining tasks generating discriminatory models from biased data sets as part of the automated decision making.”
Rule Protection for Indirect Discrimination Prevention in Data Mining (2011, methodology): “The term “redlining” was invented in the late 1960s by community activists in Chicago [2].”
Data mining for discrimination discovery (2010, unknown): “Along this line, [Kamiran and Calders 2009] adopts a controlled distortion of the training set.”
Using sensitive personal data may be necessary for avoiding discrimination in data-driven decision models (2016, background): “The general idea that excluding sensitive data from the process of creating decision-making models would yield non-discriminatory models does not hold (Kamiran and Calders 2009).”