Discrimination detection by causal effect estimation
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Date
2017
Authors
Li, J.
Liu, J.
Liu, L.
Le, T.D.
Ma, S.
Han, Y.
Editors
Nie, J.Y.
Obradovic, Z.
Suzumura, T.
Ghosh, R.
Nambiar, R.
Wang, C.
Zang, H.
BaezaYates, R.
Hu, X.
Kepner, J.
Cuzzocrea, A.
Tang, J.
Toyoda, M.
Obradovic, Z.
Suzumura, T.
Ghosh, R.
Nambiar, R.
Wang, C.
Zang, H.
BaezaYates, R.
Hu, X.
Kepner, J.
Cuzzocrea, A.
Tang, J.
Toyoda, M.
Advisors
Journal Title
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Conference paper
Citation
Proceedings 2017 IEEE International Conference on Big Data Big Data 2017, 2017 / Nie, J.Y., Obradovic, Z., Suzumura, T., Ghosh, R., Nambiar, R., Wang, C., Zang, H., BaezaYates, R., Hu, X., Kepner, J., Cuzzocrea, A., Tang, J., Toyoda, M. (ed./s), vol.2018-January, pp.1087-1094
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Conference Name
IEEE International Conference on Big Data (IEEE Big Data) (11 Dec 2017 - 14 Dec 2017 : MA, Boston)
Abstract
With more and more decisions being made by learnt algorithms from data, algorithmic discriminations have become a risk for civil rights. The detection of discrimination is a process of counterfactual reasoning. This paper proposes a general detection framework by combining a data mining method with a well established counterfactual reasoning framework, potential outcome model. The potential outcome model supports operational definitions of global and local discriminations and discriminations by combined factors, while a data mining method makes the detection efficient. The proposed method, instantiated by association rule mining with potential outcome model based causal effect estimation, is evaluated with four real world data sets and is compared with a Bayesian network (BN) based detection method. It is able to detect not only global discriminations that are detected by the BN based method, but also local and combined discriminations that the BN based method cannot find. The proposed method is efficient, and scales well with the data set size and the number of attributes.
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Copyright 2017 IEEE
Access Condition Notes: Accepted manuscript available open access