Discrimination and Privacy in the Information Society - Data Mining and Profiling in Large Databases
By Bart Custers, Toon Calders, Bart W. Schermer, Tal Z. Zarsky. 2012
155 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Economics and Econometrics, Sociology and Political Science
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Indrė Žliobaitė
- Reuben Binns
- Francesco Bonchi
- Sara Hajian
- Michael Veale
- Vrushali Kulkarni
- Asmita Kashid
- Ruhi Patankar
In Economics and Econometrics
In Sociology and Political Science
- Indrė Žliobaitė
- Francesco Bonchi
- Vrushali Kulkarni
- Asmita Kashid
- Ruhi Patankar
- Bart Custers
- Reuben Binns
- Matthias Leese
Citations in the corpus
- Exposing the probabilistic causal structure of discrimination (2015, background): “Discrimination analysis is a multi-disciplinary problem, involving sociological causes, legal reasoning, economic models, and statistical techniques [5,36].”
- Discrimination-aware data mining: a survey (2017, background): “Custers et al. (2013) present detailed information about DADM such as discrimination discovery, discrimination prevention, privacy protection and conditional discrimination.”
- Using sensitive personal data may be necessary for avoiding discrimination in data-driven decision models (2016, background): “legislation relating to discrimination on specific protected grounds (such as race, gender or age) and the general protection of equal treatment (Gellert et al. 2013).”
- Fairer machine learning in the real world: Mitigating discrimination without collecting sensitive data (2017, background): “Even though this research field has some history (Andrews et al., 1995; Custers et al., 2013; Hajian, 2013; Pedreshi et al., 2008; Vedder, 1999), usable software libraries remain largely unavailable, and little training exists.”
- Algorithmic Decision-Making Based on Machine Learning from Big Data: Can Transparency Restore Accountability? (2017, background): “15 Cf. the edited volume about this and related issues: Custers et al. (2013); in particular, the volume chapters Kamiran and Zliobaite (2013); Kamiran et al. (2013); and Hajian and Domingo-Ferrer (2013).”
- The new profiling: Algorithms, black boxes, and the failure of anti-discriminatory safeguards in the European Union (2014, background): “More specifically, the EU legal framework is composed of several directives that implement the non-discriminatory treatment of persons, but focus primarily on labor market issues (Gellert et al., 2013: 68).”