Abstract
Along with privacy, discrimination is a very important issue when considering the legal and ethical aspects of data mining. It is more than obvious that most people do not want to be discriminated because of their gender, religion, nationality, age and so on, especially when those attributes are used for making decisions about them like giving them a job, loan, insurance, etc. Discovering such potential biases and eliminating them from the training data without harming their decision-making utility is therefore highly desirable. For this reason, anti-discrimination techniques including discrimination discovery and prevention have been introduced in data mining. Discrimination prevention consists of inducing patterns that do not lead to discriminatory decisions even if the original training datasets are inherently biased. In this chapter, by focusing on the discrimination prevention, we present a taxonomy for classifying and examining discrimination prevention methods. Then, we introduce a group of pre-processing discrimination prevention methods and specify the different features of each approach and how these approaches deal with direct or indirect discrimination. A presentation of metrics used to evaluate the performance of those approaches is also given. Finally, we conclude our study by enumerating interesting future directions in this research body.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Calders, T., Verwer, S.: Three naive Bayes approaches for discrimination-free classification. Data Mining and Knowledge Discovery 21(2), 277–292 (2010)
Hajian, S., Domingo-Ferrer, J., Martinez-Ballesté, A.: Discrimination prevention in data mining for intrusion and crime detection. In: Proc. of the IEEE Symposium on Computational Intelligence in Cyber Security (CICS 2011), pp. 47–54. IEEE (2011a)
Hajian, S., Domingo-Ferrer, J., Martínez-Ballesté, A.: Rule Protection for Indirect Discrimination Prevention in Data Mining. In: Torra, V., Narakawa, Y., Yin, J., Long, J. (eds.) MDAI 2011. LNCS, vol. 6820, pp. 211–222. Springer, Heidelberg (2011b)
Hajian, S., Domingo-Ferrer, J.: A methodology for direct and indirect discrimination prevention in data mining. IEEE Transaction on Knowledge and Data Engineering (to appear)
Kamiran, F., Calders, T.: Classification without discrimination. In: Proc. of the 2nd IEEE International Conference on Computer, Control and Communication (IC4 2009). IEEE (2009)
Kamiran, F., Calders, T.: Classification with no discrimination by preferential sampling. In: Proc. of the 19th Machine Learning Conference of Belgium and The Netherlands (2010)
Kamiran, F., Calders, T., Pechenizkiy, M.: Discrimination aware decision tree learning. In: Proc. of the IEEE International Conference on Data Mining ICDM 2010, pp. 869–874. ICDM (2010)
Newman, D.J., Hettich, S., Blake, S.L., Merz, C.J.: UCI Repository of Machine Learning Databases (1998), http://archive.ics.uci.edu/ml
Parliament of the United Kingdom. Sex Discrimination Act (1975), http://www.opsi.gov.uk/acts/acts1975/PDF/ukpga19750065en.pdf
Parliament of the United Kingdom. Race Relations Act (1976), http://www.statutelaw.gov.uk/content.aspx?activeTextDocId=2059995
Pedreschi, D., Ruggieri, S., Turini, F.: Discrimination-aware data mining. In: Proc. of the 14th ACM International Conference on Knowledge Discovery and Data Mining (KDD 2008), pp. 560–568. ACM (2008)
Pedreschi, D., Ruggieri, S., Turini, F.: Measuring discrimination in socially-sensitive decision records. In: Proc. of the 9th SIAM Data Mining Conference SDM 2009, pp. 581–592. SIAM (2009a)
Pedreschi, D., Ruggieri, S., Turini, F.: Integrating induction and deduction for finding evidence of discrimination. In: Proc. of the 12th ACM International Conference on Artificial Intelligence and Law (ICAIL 2009), pp. 157–166. ACM (2009b)
Ruggieri, S., Pedreschi, D., Turini, F.: Data mining for discrimination discovery. ACM Transactions on Knowledge Discovery from Data 4(2) Article 9 (2010)
United States Congress. Employment Non-Discrimination Act (1994), http://www.govtrack.us/congress/bill.xpd?bill=h111-3017
United States Congress. US Equal Pay Act (1963), http://archive.eeoc.gov/epa/anniversary/epa-40.html
Verykios, V., Gkoulalas-Divanis, A.: A survey of association rule hiding methods for privacy. In: Aggarwal, C.C., Yu, P.S. (eds.) Privacy- Preserving Data Mining: Models and Algorithms. Springer (2008)
Yin, X., Han, J.: CPAR: Classification based on Predictive Association Rules. In: Proc. of SIAM ICDM 2003. SIAM (2003)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Hajian, S., Domingo-Ferrer, J. (2013). Direct and Indirect Discrimination Prevention Methods. In: Custers, B., Calders, T., Schermer, B., Zarsky, T. (eds) Discrimination and Privacy in the Information Society. Studies in Applied Philosophy, Epistemology and Rational Ethics, vol 3. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-30487-3_13
Download citation
DOI: https://doi.org/10.1007/978-3-642-30487-3_13
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-30486-6
Online ISBN: 978-3-642-30487-3
eBook Packages: EngineeringEngineering (R0)