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Group and Individual Fairness in Clustering Algorithms

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Ethics in Artificial Intelligence: Bias, Fairness and Beyond

Part of the book series: Studies in Computational Intelligence ((SCI,volume 1123))

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Abstract

Clustering is a classical unsupervised machine learning technique. It has various applications in criminal justice, automated resume processing, bank loan approvals, recommender systems, and many more. Despite being so popular, traditional clustering algorithms may result in discriminatory behavior towards a group of people (or individuals) and have societal impacts. It has led to the study of fair clustering algorithms that aim to minimize the clustering cost while ensuring fairness. This chapter outlines existing group and individual fairness notions, discusses their relationships, and comprehensively categorizes the current algorithms. The chapter further discusses the advantages and disadvantages of existing algorithms in terms of theoretical guarantees, time complexity, and reproducibility. Finally, the chapter concludes with a discussion of new directions and open problems in the field of fair clustering.

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Notes

  1. 1.

    \(\boldsymbol{\tau }\) vector is written in the form (red, blue) respectively in \(\boldsymbol{\tau }\)-mp, \(\boldsymbol{\tau }\)-rd and \(\boldsymbol{\tau }\)-fair notion.

  2. 2.

    (pq)-approx bicriteria denotes cost approximation of p and fairness approximation of q.

  3. 3.

    Ratio of clustering objective value under fairness constraint to the standard objective value.

  4. 4.

    Mean center of all points belonging to a single color (say red points) in the dataset.

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Correspondence to Narayanan C. Krishnan .

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Gupta, S., Jain, S., Ghalme, G., Krishnan, N.C., Hemachandra, N. (2023). Group and Individual Fairness in Clustering Algorithms. In: Mukherjee, A., Kulshrestha, J., Chakraborty, A., Kumar, S. (eds) Ethics in Artificial Intelligence: Bias, Fairness and Beyond. Studies in Computational Intelligence, vol 1123. Springer, Singapore. https://doi.org/10.1007/978-981-99-7184-8_2

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