Discrimination-aware data mining: a survey

Autor:	Ruhi Patankar, Asmita Kashid, Vrushali Kulkarni
Rok vydání:	2017
Předmět:	Minority group Computer science computer.software_genre 01 natural sciences Data science 010305 fluids & plasmas ComputingMethodologies_PATTERNRECOGNITION Lead (geology) Work (electrical) 0103 physical sciences Data mining 010306 general physics Raw data computer
Zdroj:	International Journal of Data Science. 2:70
ISSN:	2053-082X 2053-0811
DOI:	10.1504/ijds.2017.082748
Popis:	Data mining is a very important and useful technique to extract knowledge from raw data. However, there is a challenge faced by data mining researchers, in the form of potential discrimination. Discrimination means giving unfair treatment to a person just because one belongs to a minority group, without considering one's individual merit or qualification. The results extracted using data mining techniques may lead to discrimination, if a biased historical/training dataset is used. It is very important to prevent data mining technique from becoming a source of discrimination. A detailed survey of discrimination discovery methods and discrimination prevention methods is presented in this paper. This paper also presents the list of datasets used for experiments in different discrimination-aware data mining (DADM) approaches. Some ideas for future research work that may help in preventing discrimination are also discussed.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::f722f04d8bbf1c102eb01844777e9ca8 https://doi.org/10.1504/ijds.2017.082748 Zobrazit plný text záznamu