01567nas a2200193   4500008004100000020001400041245005700055210005700112260001500169300001400184490000700198520100400205100001901209700002001228700001601248700002401264700002001288856006501308       2020                        eng d  a1573-767500aCausal inference for social discrimination reasoning0 aCausal inference for social discrimination reasoning  c2020/04/01  a425 - 4370 v543 aThe discovery of discriminatory bias in human or automated decision making is a task of increasing importance and difficulty, exacerbated by the pervasive use of machine learning and data mining. Currently, discrimination discovery largely relies upon correlation analysis of decisions records, disregarding the impact of confounding biases. We present a method for causal discrimination discovery based on propensity score analysis, a statistical tool for filtering out the effect of confounding variables. We introduce causal measures of discrimination which quantify the effect of group membership on the decisions, and highlight causal discrimination/favoritism patterns by learning regression trees over the novel measures. We validate our approach on two real world datasets. Our proposed framework for causal discrimination has the potential to enhance the transparency of machine learning with tools for detecting discriminatory bias both in the training data and in the learning algorithms.1 aQureshi, Bilal1 aKamiran, Faisal1 aKarim, Asim1 aRuggieri, Salvatore1 aPedreschi, Dino  uhttps://link.springer.com/article/10.1007/s10844-019-00580-x01492nas a2200157   4500008004100000245005700041210005700098300001100155520100400166100001901170700002001189700001601209700002401225700002001249856006501269       2019                        eng d00aCausal inference for social discrimination reasoning0 aCausal inference for social discrimination reasoning  a1–133 aThe discovery of discriminatory bias in human or automated decision making is a task of increasing importance and difficulty, exacerbated by the pervasive use of machine learning and data mining. Currently, discrimination discovery largely relies upon correlation analysis of decisions records, disregarding the impact of confounding biases. We present a method for causal discrimination discovery based on propensity score analysis, a statistical tool for filtering out the effect of confounding variables. We introduce causal measures of discrimination which quantify the effect of group membership on the decisions, and highlight causal discrimination/favoritism patterns by learning regression trees over the novel measures. We validate our approach on two real world datasets. Our proposed framework for causal discrimination has the potential to enhance the transparency of machine learning with tools for detecting discriminatory bias both in the training data and in the learning algorithms.1 aQureshi, Bilal1 aKamiran, Faisal1 aKarim, Asim1 aRuggieri, Salvatore1 aPedreschi, Dino  uhttps://link.springer.com/article/10.1007/s10844-019-00580-x01704nas a2200133   4500008004100000245007000041210006900111520127400180100001901454700002001473700001601493700002401509856003701533       2016                        eng d00aCausal Discrimination Discovery Through Propensity Score Analysis0 aCausal Discrimination Discovery Through Propensity Score Analysi3 aSocial discrimination is considered illegal and unethical in the modern world. Such discrimination is often implicit in observed decisions' datasets, and anti-discrimination organizations seek to discover cases of discrimination and to understand the reasons behind them. Previous work in this direction adopted simple observational data analysis; however, this can produce biased results due to the effect of confounding variables. In this paper, we propose a causal discrimination discovery and understanding approach based on propensity score analysis. The propensity score is an effective statistical tool for filtering out the effect of confounding variables. We employ propensity score weighting to balance the distribution of individuals from protected and unprotected groups w.r.t. the confounding variables. For each individual in the dataset, we quantify its causal discrimination or favoritism with a neighborhood-based measure calculated on the balanced distributions. Subsequently, the causal discrimination/favoritism patterns are understood by learning a regression tree. Our approach avoids common pitfalls in observational data analysis and make its results legally admissible. We demonstrate the results of our approach on two discrimination datasets.1 aQureshi, Bilal1 aKamiran, Faisal1 aKarim, Asim1 aRuggieri, Salvatore  uhttps://arxiv.org/abs/1608.03735