Rule Protection for Indirect Discrimination Prevention in Data Mining
نویسندگان
چکیده
Services in the information society allow automatically and routinely collecting large amounts of data. Those data are often used to train classification rules in view of making automated decisions, like loan granting/denial, insurance premium computation, etc. If the training datasets are biased in what regards sensitive attributes like gender, race, religion, etc., discriminatory decisions may ensue. Direct discrimination occurs when decisions are made based on biased sensitive attributes. Indirect discrimination occurs when decisions are made based on non-sensitive attributes which are strongly correlated with biased sensitive attributes. This paper discusses how to clean training datasets and outsourced datasets in such a way that legitimate classification rules can still be extracted but indirectly discriminating rules cannot.
منابع مشابه
Simultaneous Discrimination Prevention and Privacy Protection in Data Publishing and Mining
Data mining is an increasingly important technology for extracting useful knowledge hidden in large collections of data. There are, however, negative social perceptions about data mining, among which potential privacy violation and potential discrimination. The former is an unintentional or deliberate disclosure of a user profile or activity data as part of the output of a data mining algorithm...
متن کاملAn Efficient Method For Discrimination Prevention Using Differentiated Virtual Passwords And Secret Little Functions
In classification, discrimination is a type of treatment that includes denying the membership in one group opportunities that are available in another group. Discrimination based on age, religion, gender, caste, disability, employment, language, race and nationality. In this technique, direct and indirect discrimination is prevented using rule protection and rule generalization methods and BIRC...
متن کاملDirect and Indirect Discrimination Prevention Methods
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 ...
متن کاملInference Mining using Direct and Indirect Discrimination Prevention in Data Mining
Data Mining is an essential and flourishing technology to extract the relevant and useful information hidden in the large collections of data. Privacy preservation in data mining is an important issue when considering the legal and ethical aspects of data mining. Discrimination is one of the facts that pave the way for negative perceptions in the data mining. Direct and Indirect discrimination ...
متن کاملDiscrimination Discovery and Prevention in Data Mining: A Survey
Data Mining is the computation process of discovering knowledge or patterns in large data sets. But extract knowledge without violation such as privacy and non-discrimination is most difficult and challenging. This is mainly because of data mining techniques such as classification rules are actually learned by the system from the training data and training data sets itself are biased in what re...
متن کامل