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Distance Correlation GAN: Fair Tabular Data Generation with Generative Adversarial Networks

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Artificial Intelligence in HCI (HCII 2023)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 14050))

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Abstract

With the growing impact of artificial intelligence, the topic of fairness in AI has received increasing attention for valid reasons. In this paper, we propose a generative adversarial network for fair tabular data generation. The model is a WGAN, where the generator is enforcing fairness by penalizing distance correlation between protected attribute and target attribute. We compare our results with another state-of-the-art generative adversarial network for fair tabular data generation and a preprocessing repairment method on four datasets, and show that our model is able to produce synthetic data, such that training a classifier on it results in a fair classifier, beating the other two methods. This makes the model suitable for applications that concern with fairness and preserving privacy.

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Authors and Affiliations

  1. Department of Computer Science, University of Central Florida, Orlando, FL, 32816, USA

    Amirarsalan Rajabi & Ozlem Ozmen Garibay

  2. Department of Industrial Engineering and Management Systems, Orlando, FL, 32816, USA

    Ozlem Ozmen Garibay

Authors
  1. Amirarsalan Rajabi
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  2. Ozlem Ozmen Garibay
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Correspondence to Ozlem Ozmen Garibay .

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Editors and Affiliations

  1. Siemens Corporation, Princeton, NJ, USA

    Helmut Degen

  2. Foundation for Research and Technology – Hellas (FORTH), Heraklion, Crete, Greece

    Stavroula Ntoa

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Rajabi, A., Garibay, O.O. (2023). Distance Correlation GAN: Fair Tabular Data Generation with Generative Adversarial Networks. In: Degen, H., Ntoa, S. (eds) Artificial Intelligence in HCI. HCII 2023. Lecture Notes in Computer Science(), vol 14050. Springer, Cham. https://doi.org/10.1007/978-3-031-35891-3_26

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  • DOI: https://doi.org/10.1007/978-3-031-35891-3_26

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  • Online ISBN: 978-3-031-35891-3

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