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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2510.22209 |
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| _version_ | 1866912670070865920 |
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| author | Kitharidis, Sofoklis Veenman, Cor J. Bäck, Thomas van Stein, Niki |
| author_facet | Kitharidis, Sofoklis Veenman, Cor J. Bäck, Thomas van Stein, Niki |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_22209 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Visual Model Selection using Feature Importance Clusters in Fairness-Performance Similarity Optimized Space Kitharidis, Sofoklis Veenman, Cor J. Bäck, Thomas van Stein, Niki Machine Learning 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. |
| title | Visual Model Selection using Feature Importance Clusters in Fairness-Performance Similarity Optimized Space |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2510.22209 |