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Fairness in Machine Learning

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Recent Trends in Learning From Data

Part of the book series: Studies in Computational Intelligence ((SCI,volume 896))

Abstract

Machine learning based systems are reaching society at large and in many aspects of everyday life. This phenomenon has been accompanied by concerns about the ethical issues that may arise from the adoption of these technologies. ML fairness is a recently established area of machine learning that studies how to ensure that biases in the data and model inaccuracies do not lead to models that treat individuals unfavorably on the basis of characteristics such as e.g. race, gender, disabilities, and sexual or political orientation. In this manuscript, we discuss some of the limitations present in the current reasoning about fairness and in methods that deal with it, and describe some work done by the authors to address them. More specifically, we show how causal Bayesian networks can play an important role to reason about and deal with fairness, especially in complex unfairness scenarios. We describe how optimal transport theory can be leveraged to develop methods that impose constraints on the full shapes of distributions corresponding to different sensitive attributes, overcoming the limitation of most approaches that approximate fairness desiderata by imposing constraints on the lower order moments or other functions of those distributions. We present a unified framework that encompasses methods that can deal with different settings and fairness criteria, and that enjoys strong theoretical guarantees. We introduce an approach to learn fair representations that can generalize to unseen tasks. Finally, we describe a technique that accounts for legal restrictions about the use of sensitive attributes.

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Notes

  1. 1.

    Throughout the manuscript, we denote random variables with capital letters, and their values with small letters.

  2. 2.

    This is the case as the non-causal path \(A\leftarrow C\rightarrow Y\) is open.

  3. 3.

    This condition is satisfied as we use empirical approximations of distributions.

  4. 4.

    If \(r <\min (d,T)\), Problem (49) is equivalent to trace norm regularization plus a rank constraint.

  5. 5.

    For all intents and purposes, one may also assume throughout that \(\mathbb {H}=\mathbb {R}^d\), the standard d-dimensional vector space, for some positive integer d.

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Acknowledgements

This work was partially supported by Amazon AWS Machine Learning Research Award.

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Authors and Affiliations

  1. University of Pisa, Pisa, Italy

    Luca Oneto

  2. DeepMind, London, UK

    Silvia Chiappa

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  1. Luca Oneto
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  1. DIBRIS, University of Genoa, Genova, Italy

    Luca Oneto

  2. Department of Mathematics “Tullio Levi-Civita”, University of Padua, Padova, Italy

    Nicolò Navarin

  3. Department of Mathematics “Tullio Levi-Civita”, University of Padua, Padova, Italy

    Alessandro Sperduti

  4. DIBRIS, Università degli Studi di Genova, Genova, Italy

    Davide Anguita

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Oneto, L., Chiappa, S. (2020). Fairness in Machine Learning. In: Oneto, L., Navarin, N., Sperduti, A., Anguita, D. (eds) Recent Trends in Learning From Data. Studies in Computational Intelligence, vol 896. Springer, Cham. https://doi.org/10.1007/978-3-030-43883-8_7

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