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Towards Fairness and Privacy: A Novel Data Pre-processing Optimization Framework for Non-binary Protected Attributes

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Data Science and Machine Learning (AusDM 2023)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1943))

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Abstract

The reason behind the unfair outcomes of AI is often rooted in biased datasets. Therefore, this work presents a framework for addressing fairness by debiasing datasets containing a (non-)binary protected attribute. The framework proposes a combinatorial optimization problem where heuristics such as genetic algorithms can be used to solve for the stated fairness objectives. The framework addresses this by finding a data subset that minimizes a certain discrimination measure. Depending on a user-defined setting, the framework enables different use cases, such as data removal, the addition of synthetic data, or exclusive use of synthetic data. The exclusive use of synthetic data in particular enhances the framework’s ability to preserve privacy while optimizing for fairness. In a comprehensive evaluation, we demonstrate that under our framework, genetic algorithms can effectively yield fairer datasets compared to the original data. In contrast to prior work, the framework exhibits a high degree of flexibility as it is metric- and task-agnostic, can be applied to both binary or non-binary protected attributes, and demonstrates efficient runtime.

This work was supported by the Federal Ministry of Education and Research (BMBF) under Grand No. 16DHB4020.

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Authors and Affiliations

  1. Heinrich Heine University, Universitätsstraße 1, 40225, Düsseldorf, Germany

    Manh Khoi Duong & Stefan Conrad

Authors
  1. Manh Khoi Duong
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  2. Stefan Conrad
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Correspondence to Manh Khoi Duong .

Editor information

Editors and Affiliations

  1. The University of Auckland, Auckland, New Zealand

    Diana Benavides-Prado

  2. The University of Melbourne, Carlton, VIC, Australia

    Sarah Erfani

  3. Shenzhen University, Shenzhen, China

    Philippe Fournier-Viger

  4. RMIT University, Melbourne, VIC, Australia

    Yee Ling Boo

  5. The University of Auckland, Auckland, New Zealand

    Yun Sing Koh

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Duong, M.K., Conrad, S. (2024). Towards Fairness and Privacy: A Novel Data Pre-processing Optimization Framework for Non-binary Protected Attributes. In: Benavides-Prado, D., Erfani, S., Fournier-Viger, P., Boo, Y.L., Koh, Y.S. (eds) Data Science and Machine Learning. AusDM 2023. Communications in Computer and Information Science, vol 1943. Springer, Singapore. https://doi.org/10.1007/978-981-99-8696-5_8

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  • DOI: https://doi.org/10.1007/978-981-99-8696-5_8

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