Fairness Constraints: Mechanisms for Fair Classification

Muhammad Bilal Zafar, Isabel Valera, Manuel Gomez Rogriguez, Krishna P. Gummadi
Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, PMLR 54:962-970, 2017.

Abstract

Algorithmic decision making systems are ubiquitous across a wide variety of online as well as offline services. These systems rely on complex learning methods and vast amounts of data to optimize the service functionality, satisfaction of the end user and profitability. However, there is a growing concern that these automated decisions can lead, even in the absence of intent, to a lack of fairness, i.e., their outcomes can disproportionately hurt (or, benefit) particular groups of people sharing one or more sensitive attributes (e.g., race, sex). In this paper, we introduce a flexible mechanism to design fair classifiers by leveraging a novel intuitive measure of decision boundary (un)fairness. We instantiate this mechanism with two well-known classifiers, logistic regression and support vector machines, and show on real-world data that our mechanism allows for a fine-grained control on the degree of fairness, often at a small cost in terms of accuracy.

Cite this Paper

BibTeX
@InProceedings{pmlr-v54-zafar17a, title = {{Fairness Constraints: Mechanisms for Fair Classification}}, author = {Zafar, Muhammad Bilal and Valera, Isabel and Rogriguez, Manuel Gomez and Gummadi, Krishna P.}, booktitle = {Proceedings of the 20th International Conference on Artificial Intelligence and Statistics}, pages = {962--970}, year = {2017}, editor = {Singh, Aarti and Zhu, Jerry}, volume = {54}, series = {Proceedings of Machine Learning Research}, month = {20--22 Apr}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v54/zafar17a/zafar17a.pdf}, url = {https://proceedings.mlr.press/v54/zafar17a.html}, abstract = {Algorithmic decision making systems are ubiquitous across a wide variety of online as well as offline services. These systems rely on complex learning methods and vast amounts of data to optimize the service functionality, satisfaction of the end user and profitability. However, there is a growing concern that these automated decisions can lead, even in the absence of intent, to a lack of fairness, i.e., their outcomes can disproportionately hurt (or, benefit) particular groups of people sharing one or more sensitive attributes (e.g., race, sex). In this paper, we introduce a flexible mechanism to design fair classifiers by leveraging a novel intuitive measure of decision boundary (un)fairness. We instantiate this mechanism with two well-known classifiers, logistic regression and support vector machines, and show on real-world data that our mechanism allows for a fine-grained control on the degree of fairness, often at a small cost in terms of accuracy.} }
Endnote
%0 Conference Paper %T Fairness Constraints: Mechanisms for Fair Classification %A Muhammad Bilal Zafar %A Isabel Valera %A Manuel Gomez Rogriguez %A Krishna P. Gummadi %B Proceedings of the 20th International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2017 %E Aarti Singh %E Jerry Zhu %F pmlr-v54-zafar17a %I PMLR %P 962--970 %U https://proceedings.mlr.press/v54/zafar17a.html %V 54 %X Algorithmic decision making systems are ubiquitous across a wide variety of online as well as offline services. These systems rely on complex learning methods and vast amounts of data to optimize the service functionality, satisfaction of the end user and profitability. However, there is a growing concern that these automated decisions can lead, even in the absence of intent, to a lack of fairness, i.e., their outcomes can disproportionately hurt (or, benefit) particular groups of people sharing one or more sensitive attributes (e.g., race, sex). In this paper, we introduce a flexible mechanism to design fair classifiers by leveraging a novel intuitive measure of decision boundary (un)fairness. We instantiate this mechanism with two well-known classifiers, logistic regression and support vector machines, and show on real-world data that our mechanism allows for a fine-grained control on the degree of fairness, often at a small cost in terms of accuracy.
APA
Zafar, M.B., Valera, I., Rogriguez, M.G. & Gummadi, K.P.. (2017). Fairness Constraints: Mechanisms for Fair Classification. Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research 54:962-970 Available from https://proceedings.mlr.press/v54/zafar17a.html.

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