Data mining is the study of data for relationships that have not previously been discovered. In sociology, discrimination is the hurtful treatment of an individual based on the group, class or category to which that person or things belongs rather than on individual merit: racial and religious intolerance and discrimination. Along with confidentiality, discrimination is a very essential 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 race, gender, religion, nationality, age etc, especially when those attributes are used for making decisions about them like giving them a job, loan, education, insurance etc. Because of this reason, antidiscrimination techniques with discrimination discovery as well as discrimination prevention have been introduced in data mining. Discrimination can be either direct or indirect. Direct discrimination occurs when decisions are taken by considering sensitive attributes. Indirect discrimination occurs when decisions are taken on the basis of nonsensitive attributes which are strongly associated with biased sensitive ones. Here, discrimination prevention in data mining is tackle as well as propose new techniques applicable for direct or indirect discrimination prevention individually or both at the same time. Several decision-making tasks are there which let somebody use themselves to discrimination, such as education, life insurances, loan granting, and staff selection. In many applications, information systems are used for decision-making tasks.