Visual Model Selection using Feature Importance Clusters in Fairness-Performance Similarity Optimized Space

• URL: http://arxiv.org/abs/2510.22209v1
• Date: Sat, 25 Oct 2025 08:18:41 GMT
• Title: Visual Model Selection using Feature Importance Clusters in Fairness-Performance Similarity Optimized Space
• Authors: Sofoklis Kitharidis, Cor J. Veenman, Thomas Bäck, Niki van Stein,
• Abstract summary: We propose an interactive framework that assists in navigating and interpreting the trade-offs across a portfolio of models.<n>Our approach leverages weakly supervised metric learning to learn a Mahalanobis distance that reflects similarity in fairness and performance outcomes.<n>We then apply clustering technique (k-means) to group models based on their transformed representations of feature importances.
• Score: 1.8682641481190012
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In the context of algorithmic decision-making, fair machine learning methods often yield multiple models that balance predictive fairness and performance in varying degrees. This diversity introduces a challenge for stakeholders who must select a model that aligns with their specific requirements and values. To address this, we propose an interactive framework that assists in navigating and interpreting the trade-offs across a portfolio of models. Our approach leverages weakly supervised metric learning to learn a Mahalanobis distance that reflects similarity in fairness and performance outcomes, effectively structuring the feature importance space of the models according to stakeholder-relevant criteria. We then apply clustering technique (k-means) to group models based on their transformed representations of feature importances, allowing users to explore clusters of models with similar predictive behaviors and fairness characteristics. This facilitates informed decision-making by helping users understand how models differ not only in their fairness-performance balance but also in the features that drive their predictions.

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