@InProceedings{kamishima:icdmw:2012,
  author    = {Kamishima, Toshihiro and Akaho, Shotaro and Asoh, Hideki and Sakuma, Jun},
  title     = {Considerations on Fairness-Aware Data Mining},
  booktitle = {IEEE International Conference on Data Mining Workshops},
  year      = {2012},
  address   = {Brussels, Belgium},
  month     = {December 10},
  url       = {https://www.kamishima.net/archive/2012-ws-icdm-HN.pdf},
  keywords  = {Data mining, Data models, Equations, Training, Indexes. Mathematical model. Predictive models},
  doi       = {10.1109/ICDMW.2012.101},
  abstract  = {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\textquotesingle  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.}
}