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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.

    (p, q)-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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Author information

Authors and Affiliations

  1. Indian Institute of Technology Ropar, Bara Phool, India

    Shivam Gupta & Shweta Jain

  2. Indian Institute of Technology Hyderabad, Kandi, India

    Ganesh Ghalme

  3. Indian Institute of Technology Palakkad, Kanjikode, India

    Narayanan C. Krishnan

  4. Indian Institute of Technology Bombay, Mumbai, India

    Nandyala Hemachandra

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  1. Shivam Gupta
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  2. Shweta Jain
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  3. Ganesh Ghalme
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  4. Narayanan C. Krishnan
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  5. Nandyala Hemachandra
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Correspondence to Narayanan C. Krishnan .

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Editors and Affiliations

  1. Department of Computer Science and Engineering, IIT Kharagpur, West Bengal, India

    Animesh Mukherjee

  2. Department of Computer Science, Aalto University, Espoo, Finland

    Juhi Kulshrestha

  3. Department of Computer Science and Engineering, IIT Delhi, New Delhi, Delhi, India

    Abhijnan Chakraborty

  4. School of Computational Science and Engineering, College of Computing, Georgia Institute of Technology, Atlanta, GA, USA

    Srijan Kumar

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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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