Fairness in Machine Learning: Lessons from Political Philosophy

Reuben Binns
Proceedings of the 1st Conference on Fairness, Accountability and Transparency, PMLR 81:149-159, 2018.

Abstract

What does it mean for a machine learning model to be ‘fair’, in terms which can be operationalised? Should fairness consist of ensuring everyone has an equal probability of obtaining some benefit, or should we aim instead to minimise the harms to the least advantaged? Can the relevant ideal be determined by reference to some alternative state of affairs in which a particular social pattern of discrimination does not exist? Various definitions proposed in recent literature make different assumptions about what terms like discrimination and fairness mean and how they can be defined in mathematical terms. Questions of discrimination, egalitarianism and justice are of significant interest to moral and political philosophers, who have expended significant efforts in formalising and defending these central concepts. It is therefore unsurprising that attempts to formalise ‘fairness’ in machine learning contain echoes of these old philosophical debates. This paper draws on existing work in moral and political philosophy in order to elucidate emerging debates about fair machine learning.

Cite this Paper

BibTeX
@InProceedings{pmlr-v81-binns18a, title = {Fairness in Machine Learning: Lessons from Political Philosophy}, author = {Binns, Reuben}, booktitle = {Proceedings of the 1st Conference on Fairness, Accountability and Transparency}, pages = {149--159}, year = {2018}, editor = {Friedler, Sorelle A. and Wilson, Christo}, volume = {81}, series = {Proceedings of Machine Learning Research}, month = {23--24 Feb}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v81/binns18a/binns18a.pdf}, url = {https://proceedings.mlr.press/v81/binns18a.html}, abstract = {What does it mean for a machine learning model to be ‘fair’, in terms which can be operationalised? Should fairness consist of ensuring everyone has an equal probability of obtaining some benefit, or should we aim instead to minimise the harms to the least advantaged? Can the relevant ideal be determined by reference to some alternative state of affairs in which a particular social pattern of discrimination does not exist? Various definitions proposed in recent literature make different assumptions about what terms like discrimination and fairness mean and how they can be defined in mathematical terms. Questions of discrimination, egalitarianism and justice are of significant interest to moral and political philosophers, who have expended significant efforts in formalising and defending these central concepts. It is therefore unsurprising that attempts to formalise ‘fairness’ in machine learning contain echoes of these old philosophical debates. This paper draws on existing work in moral and political philosophy in order to elucidate emerging debates about fair machine learning.} }
Endnote
%0 Conference Paper %T Fairness in Machine Learning: Lessons from Political Philosophy %A Reuben Binns %B Proceedings of the 1st Conference on Fairness, Accountability and Transparency %C Proceedings of Machine Learning Research %D 2018 %E Sorelle A. Friedler %E Christo Wilson %F pmlr-v81-binns18a %I PMLR %P 149--159 %U https://proceedings.mlr.press/v81/binns18a.html %V 81 %X What does it mean for a machine learning model to be ‘fair’, in terms which can be operationalised? Should fairness consist of ensuring everyone has an equal probability of obtaining some benefit, or should we aim instead to minimise the harms to the least advantaged? Can the relevant ideal be determined by reference to some alternative state of affairs in which a particular social pattern of discrimination does not exist? Various definitions proposed in recent literature make different assumptions about what terms like discrimination and fairness mean and how they can be defined in mathematical terms. Questions of discrimination, egalitarianism and justice are of significant interest to moral and political philosophers, who have expended significant efforts in formalising and defending these central concepts. It is therefore unsurprising that attempts to formalise ‘fairness’ in machine learning contain echoes of these old philosophical debates. This paper draws on existing work in moral and political philosophy in order to elucidate emerging debates about fair machine learning.
APA
Binns, R.. (2018). Fairness in Machine Learning: Lessons from Political Philosophy. Proceedings of the 1st Conference on Fairness, Accountability and Transparency, in Proceedings of Machine Learning Research 81:149-159 Available from https://proceedings.mlr.press/v81/binns18a.html.

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