@article {1423,
	title = {Causal inference for social discrimination reasoning},
	volume = {54},
	year = {2020},
	month = {2020/04/01},
	pages = {425 - 437},
	abstract = {The 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.},
	isbn = {1573-7675},
	doi = {https://doi.org/10.1007/s10844-019-00580-x},
	url = {https://link.springer.com/article/10.1007/s10844-019-00580-x},
	author = {Qureshi, Bilal and Kamiran, Faisal and Karim, Asim and Salvatore Ruggieri and Dino Pedreschi}
}
@article {1296,
	title = {Causal inference for social discrimination reasoning},
	journal = {Journal of Intelligent Information Systems},
	year = {2019},
	pages = {1{\textendash}13},
	abstract = {The 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.},
	doi = {10.1007/s10844-019-00580-x},
	url = {https://link.springer.com/article/10.1007/s10844-019-00580-x},
	author = {Qureshi, Bilal and Kamiran, Faisal and Karim, Asim and Salvatore Ruggieri and Dino Pedreschi}
}
@article {972,
	title = {Causal Discrimination Discovery Through Propensity Score Analysis},
	journal = {arXiv preprint arXiv:1608.03735},
	year = {2016},
	abstract = {Social discrimination is considered illegal and unethical in the modern world. Such discrimination is often implicit in observed decisions{\textquoteright} 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.},
	url = {https://arxiv.org/abs/1608.03735},
	author = {Qureshi, Bilal and Kamiran, Faisal and Karim, Asim and Salvatore Ruggieri}
}
@conference {983,
	title = {Anti-discrimination analysis using privacy attack strategies},
	booktitle = {Joint European Conference on Machine Learning and Knowledge Discovery in Databases},
	year = {2014},
	publisher = {Springer, Berlin, Heidelberg},
	organization = {Springer, Berlin, Heidelberg},
	abstract = {Social discrimination discovery from data is an important task to identify illegal and unethical discriminatory patterns towards protected-by-law groups, e.g., ethnic minorities. We deploy privacy attack strategies as tools for discrimination discovery under hard assumptions which have rarely tackled in the literature: indirect discrimination discovery, privacy-aware discrimination discovery, and discrimination data recovery. The intuition comes from the intriguing parallel between the role of the anti-discrimination authority in the three scenarios above and the role of an attacker in private data publishing. We design strategies and algorithms inspired/based on Fr{\`e}chet bounds attacks, attribute inference attacks, and minimality attacks to the purpose of unveiling hidden discriminatory practices. Experimental results show that they can be effective tools in the hands of anti-discrimination authorities.},
	doi = {10.1007/978-3-662-44851-9_44},
	author = {Salvatore Ruggieri and Sara Hajian and Kamiran, Faisal and Zhang, Xiangliang}
}
