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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. 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