通过特征重要性聚类，帮助用户在公平性与性能间做出更明智的模型选择。
Visual Model Selection using Feature Importance Clusters in Fairness-Performance Similarity Optimized Space
- 用弱监督度量学习构建公平性-性能相似空间，量化模型间差异
- 基于特征重要性聚类，将模型分组以揭示预测行为共性
- 适合需权衡公平与性能的决策者使用，尤其适用于高风险场景
在算法决策背景下，公平机器学习方法常产生多个在预测公平性与性能间不同权衡的模型。这种多样性给利益相关方带来挑战，因其需选择符合自身需求和价值观的模型。为此，我们提出一种交互式框架，辅助在模型组合中导航并解读权衡关系。该方法利用弱监督度量学习，学习一个马氏距离，反映模型在公平性与性能结果上的相似性，有效根据利益相关方关注的标准对模型的特征重要性空间进行结构化。随后应用k-means聚类技术，基于模型特征重要性的转换表示进行分组，使用户可探索具有相似预测行为和公平特征的模型集群。这有助于用户不仅理解模型在公平性-性能平衡上的差异，还理解驱动其预测的关键特征，从而支持更明智的决策。
原文摘要 · Abstract (English)
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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