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Improving Fairness via Deep Ensemble Framework Using Preprocessing Interventions

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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

In recent years, automated decision making has grown as a consequence of wide use of machine learning models in real-world applications. Biases inherited in these algorithms might eventually result in discrimination and can significantly impact people’s lives. Increased social awareness and ethical concerns in the human-centered AI community has led to responsible AI to address issues such as fairness in ML models. In this paper we propose an ensemble framework of deep learning models to improve fairness. We propose four different sampling strategies to analyze the impact of different ensemble strategies. Through experiments on two real-world datasets, we show our proposed framework achieves higher fairness than several benchmark models with minimal compromise of accuracy. Also our experiments show that a standard ensemble model without any fairness constraint does not remove bias and a proper design is necessary.

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Notes

  1. 1.

    The scores of this paper was extracted and presented in [50].

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Authors and Affiliations

  1. Department of Industrial Engineering and Management Systems, University of Central Florida, Orlando, USA

    Aida Tayebi & Ozlem Ozmen Garibay

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  1. Aida Tayebi
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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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Tayebi, A., Garibay, O.O. (2023). Improving Fairness via Deep Ensemble Framework Using Preprocessing Interventions. 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_29

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  • DOI: https://doi.org/10.1007/978-3-031-35891-3_29

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