Brussels, Belgium Belgium
Dec. 10, 2012 to Dec. 10, 2012
With the spread of data mining technologies and the accumulation of social data, such technologies and data are being used for determinations that seriously affect individuals' lives. For example, credit scoring is frequently determined based on the records of past credit data together with statistical prediction techniques. Needless to say, such determinations must be nondiscriminatory and fair regarding sensitive features such as race, gender, religion, and so on. Several researchers have recently begun to develop fairness-aware or discrimination-aware data mining techniques that take into account issues of social fairness, discrimination, and neutrality. In this paper, after demonstrating the applications of these techniques, we explore the formal concepts of fairness and techniques for handling fairness in data mining. We then provide an integrated view of these concepts based on statistical independence. Finally, we discuss the relations between fairness-aware data mining and other research topics, such as privacy-preserving data mining or causal inference.
Data mining, Data models, Equations, Training, Indexes, Mathematical model, Predictive models, privacy, fairness, discrimination, data mining
Toshihiro Kamishima, Shotaro Akaho, Hideki Asoh, Jun Sakuma, "Considerations on Fairness-Aware Data Mining", ICDMW, 2012, 2013 IEEE 13th International Conference on Data Mining Workshops, 2013 IEEE 13th International Conference on Data Mining Workshops 2012, pp. 378-385, doi:10.1109/ICDMW.2012.101