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Making ML Models Fairer Through Explanations: The Case of LimeOut

Part of the Lecture Notes in Computer Science book series (LNISA,volume 12602)

Abstract

Algorithmic decisions are now being used on a daily basis, and based on Machine Learning (ML) processes that may be complex and biased. This raises several concerns given the critical impact that biased decisions may have on individuals or on society as a whole. Not only unfair outcomes affect human rights, they also undermine public trust in ML and AI. In this paper we address fairness issues of ML models based on decision outcomes, and we show how the simple idea of “feature dropout” followed by an “ensemble approach” can improve model fairness. To illustrate, we will revisit the case of “LimeOut” that was proposed to tackle “process fairness”, which measures a model’s reliance on sensitive or discriminatory features. Given a classifier, a dataset and a set of sensitive features, LimeOut first assesses whether the classifier is fair by checking its reliance on sensitive features using “Lime explanations”. If deemed unfair, LimeOut then applies feature dropout to obtain a pool of classifiers. These are then combined into an ensemble classifier that was empirically shown to be less dependent on sensitive features without compromising the classifier’s accuracy. We present different experiments on multiple datasets and several state of the art classifiers, which show that LimeOut’s classifiers improve (or at least maintain) not only process fairness but also other fairness metrics such as individual and group fairness, equal opportunity, and demographic parity.

Keywords

  • Fairness metrics
  • Feature importance
  • Feature-dropout
  • Ensemble classifier
  • LIME explanations

This research was partially supported by TAILOR, a project funded by EU Horizon 2020 research and innovation programme under GA No 952215, and the Inria Project Lab “Hybrid Approaches for Interpretable AI” (HyAIAI).

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Fig. 1.
Fig. 2.
Fig. 3.
Fig. 4.

Notes

  1. 1.

    It is also referred to as disparate treatment or predictive parity.

  2. 2.

    It is also referred to as group fairness.

  3. 3.

    It is also referred to as disparate mistreatment.

  4. 4.

    It is also referred to as procedural fairness.

  5. 5.

    In [2] k was set to 10.

  6. 6.

    http://archive.ics.uci.edu/ml/datasets/Adult.

  7. 7.

    https://archive.ics.uci.edu/ml/datasets/statlog+(german+credit+data).

  8. 8.

    https://www.consumerfinance.gov/data-research/hmda/.

  9. 9.

    https://archive.ics.uci.edu/ml/datasets/default+of+credit+card+clients.

  10. 10.

    http://www.seaphe.org/databases.php.

  11. 11.

    The gitlab repository of LimeOut can be found here:

    https://gitlab.inria.fr/orpailleur/limeout.

  12. 12.

    https://machinelearningmastery.com/threshold-moving-for-imbalanced-classification/.

  13. 13.

    We used version 0.23.1 of Scikit-learn.

  14. 14.

    https://github.com/Trusted-AI/AIF360.

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Correspondence to Miguel Couceiro .

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A Appendix

A Appendix

Fig. 5.
figure 5

Fairness metrics for the HMDA dataset (first and second lines) and the Default dataset (third and fourth lines). For both datasets, lesser original models were deemed unfair, namely, ADA, Bagging and RF on HMDA, and ADA and Bagging on Default. Even though these models were deemed unfair by LimeOut, most of the fairness metrics actually indicate a rather fair behaviour by the original and LimeOut ’s ensemble models.

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Alves, G., Bhargava, V., Couceiro, M., Napoli, A. (2021). Making ML Models Fairer Through Explanations: The Case of LimeOut. In: , et al. Analysis of Images, Social Networks and Texts. AIST 2020. Lecture Notes in Computer Science(), vol 12602. Springer, Cham. https://doi.org/10.1007/978-3-030-72610-2_1

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  • DOI: https://doi.org/10.1007/978-3-030-72610-2_1

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