01492nas 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-x