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Achieving Long-Term Fairness in Submodular Maximization Through Randomization

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Graphs and Combinatorial Optimization: from Theory to Applications (CTW 2023)

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Abstract

Submodular function optimization is applied in ML and data analysis, including diverse dataset summarization. Fairness-aware algorithms are essential for handling sensitive attributes. Our research investigates the problem of maximizing a monotone submodular function while adhering to constraints on the expected number of selected items per group. Our goal is to compute a distribution over feasible sets, and to achieve this, we develop a series of approximation algorithms.

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Authors and Affiliations

  1. Naveen Jindal School of Management, The University of Texas at Dallas, Richardson, USA

    Shaojie Tang

  2. Department of Computer Science and Engineering, University of North Texas, Denton, USA

    Jing Yuan & Twumasi Mensah-Boateng

Authors
  1. Shaojie Tang
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  2. Jing Yuan
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  3. Twumasi Mensah-Boateng
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Correspondence to Shaojie Tang .

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Editors and Affiliations

  1. Fakultät für Wirtschafts- und Organisationswissenschaften, Universität der Bundeswehr München, Munich, Germany

    Andreas Brieden

  2. Fakultät für Informatik, Universität der Bundeswehr München, Munich, Germany

    Stefan Pickl

  3. Fakultät für Informatik, Universität der Bundeswehr München, Munich, Germany

    Markus Siegle

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Tang, S., Yuan, J., Mensah-Boateng, T. (2024). Achieving Long-Term Fairness in Submodular Maximization Through Randomization. In: Brieden, A., Pickl, S., Siegle, M. (eds) Graphs and Combinatorial Optimization: from Theory to Applications. CTW 2023. AIRO Springer Series, vol 13. Springer, Cham. https://doi.org/10.1007/978-3-031-46826-1_13

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