Variational Fair Clustering

Authors

  • Imtiaz Masud Ziko ÉTS Montreal, Canada
  • Jing Yuan Xidian University, China
  • Eric Granger ÉTS Montreal, Canada
  • Ismail Ben Ayed ÉTS Montreal, Canada

Keywords:

Clustering, Ethics -- Bias, Fairness, Transparency & Privacy, Societal Impact of AI, Optimization

Abstract

We propose a general variational framework of fair clustering, which integrates an original Kullback-Leibler (KL) fairness term with a large class of clustering objectives, including prototype or graph based. Fundamentally different from the existing combinatorial and spectral solutions, our variational multi-term approach enables to control the trade-off levels between the fairness and clustering objectives. We derive a general tight upper bound based on a concave-convex decomposition of our fairness term, its Lipschitz-gradient property and the Pinsker’s inequality. Our tight upper bound can be jointly optimized with various clustering objectives, while yielding a scalable solution, with convergence guarantee. Interestingly, at each iteration, it performs an independent update for each assignment variable. Therefore, it can be easily distributed for large-scale datasets. This scalability is important as it enables to explore different trade-off levels between the fairness and clustering objectives. Unlike spectral relaxation, our formulation does not require computing its eigenvalue decomposition. We report comprehensive evaluations and comparisons with state-of-the-art methods over various fair clustering benchmarks, which show that our variational formulation can yield highly competitive solutions in terms of fairness and clustering objectives.

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Published

2021-05-18

How to Cite

Ziko, I. M., Yuan, J., Granger, E., & Ben Ayed, I. (2021). Variational Fair Clustering. Proceedings of the AAAI Conference on Artificial Intelligence, 35(12), 11202-11209. Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/17336

Issue

Section

AAAI Technical Track on Machine Learning V