The XAI project introduces FairParTree (Landi et al., 2025), an innovative clustering algorithm that integrates fairness and interpretability directly into the data partitioning process.
Unlike traditional clustering algorithms that often lack interpretability and exhibit bias, FairParTree integrates fairness constraints during the clustering process, ensuring that the resulting clusters do not disadvantage any particular group. By leveraging the structure of decision trees, FairParTree provides clear and understandable explanations of cluster assignments through logical rules.
Experiments demonstrate that FairParTree maintains excellent performance in terms of fairness, interpretability, and clustering quality on datasets of various sizes, positioning itself as a competitive, fair, and interpretable clustering algorithm.
References
2025
Balancing Fairness and Interpretability in Clustering with FairParTree
Cristiano
Landi, Alessio
Cascione, Marta Marchiori
Manerba, and Riccardo
Guidotti
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.
@inbook{LCM2025,author={Landi, Cristiano and Cascione, Alessio and Manerba, Marta Marchiori and Guidotti, Riccardo},booktitle={Explainable Artificial Intelligence},doi={10.1007/978-3-032-08324-1_5},isbn={9783032083241},issn={1865-0937},line={1,5},month=oct,open_access={Gold},pages={104–127},publisher={Springer Nature Switzerland},title={Balancing Fairness and Interpretability in Clustering with FairParTree},visible_on_website={YES},year={2025}}
