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Enhancing Fairness and Accuracy in Machine Learning Through Similarity Networks

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Cooperative Information Systems (CoopIS 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14353))

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Abstract

Machine Learning is a powerful tool for uncovering relationships and patterns within datasets. However, applying it to a large datasets can lead to biased outcomes and quality issues, due to confounder variables indirectly related to the outcome of interest. Achieving fairness often alters training data, like balancing imbalanced groups (privileged/unprivileged) or excluding sensitive features, impacting accuracy. To address this, we propose a solution inspired by similarity network fusion, preserving dataset structure by integrating global and local similarities. We evaluate our method, considering data set complexity, fairness, and accuracy. Experimental results show the similarity network’s effectiveness in balancing fairness and accuracy. We discuss implications and future directions.

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Notes

  1. 1.

    https://rpubs.com/rhuebner/hrd_cb_v14.

  2. 2.

    https://scikit-learn.org/stable/modules/impute.html.

  3. 3.

    https://search.r-project.org/CRAN/refmans/mice/html/mice.html.

  4. 4.

    https://radimrehurek.com/gensim/models/word2vec.html.

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Authors and Affiliations

  1. Department of Computer Science, Università degli Studi di Milano, Milan, Italy

    Samira Maghool, Elena Casiraghi & Paolo Ceravolo

Authors
  1. Samira Maghool
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  2. Elena Casiraghi
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  3. Paolo Ceravolo
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Corresponding authors

Correspondence to Samira Maghool or Paolo Ceravolo .

Editor information

Editors and Affiliations

  1. Telecom SudParis, Evry, France

    Mohamed Sellami

  2. Leibniz University Hannover, Hannover, Germany

    Maria-Esther Vidal

  3. Eindhoven University of Technology, Eindhoven, The Netherlands

    Boudewijn van Dongen

  4. Telecom SudParis, Evry, France

    Walid Gaaloul

  5. University of Lorraine, Vandoeuvre-les-Nancy, France

    Hervé Panetto

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Maghool, S., Casiraghi, E., Ceravolo, P. (2024). Enhancing Fairness and Accuracy in Machine Learning Through Similarity Networks. In: Sellami, M., Vidal, ME., van Dongen, B., Gaaloul, W., Panetto, H. (eds) Cooperative Information Systems. CoopIS 2023. Lecture Notes in Computer Science, vol 14353. Springer, Cham. https://doi.org/10.1007/978-3-031-46846-9_1

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  • DOI: https://doi.org/10.1007/978-3-031-46846-9_1

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