2020
Journal article  Open Access

Causal inference for social discrimination reasoning

Qureshi B., Kamiran F., Karim A., Ruggieri S., Pedreschi D.

Computer Science - Computers and Society  Social discrimination  Artificial Intelligence  Information Systems  Hardware and Architecture  Computer Networks and Communications  Computers and Society (cs.CY)  Fairness  Propensity score  Causal analysis  FOS: Computer and information sciences  Statistics - Applications  Accountability and transparency  Software  Applications (stat.AP) 

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.

Source: Journal of intelligent information systems 54 (2020): 425–437. doi:10.1007/s10844-019-00580-x

Publisher: Kluwer Academic Publishers, Boston , Paesi Bassi


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BibTeX entry
@article{oai:it.cnr:prodotti:422748,
	title = {Causal inference for social discrimination reasoning},
	author = {Qureshi B. and Kamiran F. and Karim A. and Ruggieri S. and Pedreschi D.},
	publisher = {Kluwer Academic Publishers, Boston , Paesi Bassi},
	doi = {10.1007/s10844-019-00580-x and 10.48550/arxiv.1608.03735},
	journal = {Journal of intelligent information systems},
	volume = {54},
	pages = {425–437},
	year = {2020}
}