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The revolution involving Machine Learning has transformed data analytics, making algorithms important in decision-making processes across various domains, even in sensitive scenarios. Indeed, traditional clustering algorithms often lack interpretability and exhibit biases, leading to discriminatory practices and opaque decision-making. To overcome these limitations, we introduce FairParTree, a fair and interpretable clustering algorithm that integrates fairness constraints directly into the clustering process, ensuring that the resulting clusters do not disproportionately disadvantage any particular group. By leveraging the structure of decision trees, FairParTree enhances the interpretability of clustering results by providing clear and understandable motivations for cluster assignments through rule-based explanations. We evaluate FairParTree against state-of-the-art competitors. Through extensive experiments, we show that it maintains strong performances w.r.t. fairness, interpretability, and clustering quality across different dataset sizes, thus positioning itself as a competitive, fair, and interpretable clustering algorithm.

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Balancing Fairness and Interpretability in Clustering with FairParTree

  • 会议论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年

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  • Cristiano Landi,
  • Alessio Cascione,
  • Marta Marchiori Manerba,
  • Riccardo Guidotti

摘要

The revolution involving Machine Learning has transformed data analytics, making algorithms important in decision-making processes across various domains, even in sensitive scenarios. Indeed, traditional clustering algorithms often lack interpretability and exhibit biases, leading to discriminatory practices and opaque decision-making. To overcome these limitations, we introduce FairParTree, a fair and interpretable clustering algorithm that integrates fairness constraints directly into the clustering process, ensuring that the resulting clusters do not disproportionately disadvantage any particular group. By leveraging the structure of decision trees, FairParTree enhances the interpretability of clustering results by providing clear and understandable motivations for cluster assignments through rule-based explanations. We evaluate FairParTree against state-of-the-art competitors. Through extensive experiments, we show that it maintains strong performances w.r.t. fairness, interpretability, and clustering quality across different dataset sizes, thus positioning itself as a competitive, fair, and interpretable clustering algorithm.