Probabilistic Fair Clustering

Abstract

In clustering problems, a central decision-maker is given a complete metric graph over vertices and must provide a clustering of vertices that minimizes some objective function. In fair clustering problems, vertices are endowed with a color (e.g., membership in a group), and the features of a valid clustering might also include the representation of colors in that clustering. Prior work in fair clustering assumes complete knowledge of group membership. In this paper, we generalize prior work by assuming imperfect knowledge of group membership through probabilistic assignments. We present clustering algorithms in this more general setting with approximation ratio guarantees. We also address the problem of "metric membership", where different groups have a notion of order and distance. Experiments are conducted using our proposed algorithms as well as baselines to validate our approach and also surface nuanced concerns when group membership is not known deterministically.

References in corpus (8)

• Scikit-learn: Machine Learning in Python
• Equality of Opportunity in Supervised Learning
• Clustering without Over-Representation
• Scalable Fair Clustering
• Coresets for Clustering with Fairness Constraints
• Assessing Algorithmic Fairness with Unobserved Protected Class Using Data Combination
• Measuring Non-Expert Comprehension of Machine Learning Fairness Metrics
• Equalized odds postprocessing under imperfect group information

Cited by in corpus (2)

• A survey on datasets for fairness-aware machine learning
• Fair Clustering Under a Bounded Cost