Discrimination like privacy is a big issue when legal and ethical aspects of Data mining are considered. Most people don’t like to be discriminated for their gender, religion, nationality, age and so on, especially when those attributes are needed for making decisions. Decisions like giving them a job, loan, insurance, etc. Hence it is highly desirable to discover such potential biases and eliminating them from the training data without harming their decision-making utility .Therefore antidiscrimination techniques including discrimination discovery and prevention have been introduced in data mining. Discrimination prevention consist inducing patterns which do not lead to discriminatory decisions even if the original training datasets are inherently biased. So By focusing on the discrimination prevention, we present a group of pre-processing discrimination prevention methods with different features of each approach and how These approaches deal with direct or indirect discrimination.
Antidiscrimination, data mining, direct and indirect discrimination prevention, rule protection, rule generalization, Privacy.
 European Union Legislation. Directive 95/46/EC, 1995.
 Australian Legislation. (a) Equal Opportunity Act Victoria State, (b) Anti-Discrimination Act -Queensland State, 2008. http://www.austlii.edu.au./.
 European Union Legislation, (a) Race Equality Directive,2000/43/EC, 2000; (b) Employment Equality Directive, 2000/78/EC, 2000; (c) Equal Treatment of Persons, European Parliament legislative resolution, P6 TA(2009)0211, 2009.
 S. Ruggieri, D. Pedreschi and F. Turini, “Data mining for discrimination discovery”, ACM Transactions on Knowledge Discovery from Data, 4(2) Article9, ACM, 2010.
 F. Kamiran and T. Calders, “Classification without discrimination”, Proc. of the 2nd IEEE International Conference on Computer, Control and Communication (IC4 2009). IEEE, 2009.
 F. Kamiran and T. Calders, “Classification with no discrimination by preferentialsampling”, Proc. of the 19th Machine Learning conference of Belgium and The Netherlands, 2010.
 T. Calders and S. Verwer, “Three naive Bayes approaches for discrimination-free classification”, Data Mining and Knowledge Discovery, 21(2):277-292,2010.
 F. Kamiran, T. Calders and M. Pechenizkiy, “Discrimination aware decision tree learning”, Proc. of the IEEE International Conference on Data Mining (ICDM2010), pp. 869-874. ICDM, 2010.
 D. Pedreschi, S. Ruggieri, and F. Turini, “Measuring Discrimination in Socially-Sensitive Decision Records,” Proc. Ninth SIAM Data Mining Conf. (SDM ’09), pp. 581-592, 2009.
 D. Pedreschi, S. Ruggieri, and F. Turini, “Integrating Induction and Deduction for Finding Evidence of Discrimination,” Proc. 12th ACM Int’l Conf. Artificial Intelligence and Law (ICAIL ’09), pp. 157-166, 2009.
 S. Ruggieri, D. Pedreschi, and F. Turini, “Data Mining for Discrimination Discovery,” ACM Trans. Knowledge Discovery from Data, vol. 4, no. 2, article 9, 2010.  S. Ruggieri, D. Pedreschi, and F. Turini, “DCUBE: Discrimination Discovery in Databases,” Proc. ACM Int’l Conf. Management of Data(SIGMOD ’10), pp. 1127-1130, 2010.
[Prajakta A. Soundankar , . (2015), Discrimination Avoidance Methods in Data Mining, International Journal of Innovative Research in Computer Science & Technology (IJIRCST), Vol-3, Issue-1, Page No-20-23], (ISSN 2347 - 5552). www.ijircst.org
Prajakta A. Soundankar
Computer Department, MET BKC, Savitribai Phule Pune University, Nasik , India,