新分类树算法通过公平性感知分裂，降低预测歧视。
Uncertainty-Aware Fairness-Adaptive Classification Trees
- 用公平性感知不纯度衡量，平衡准确率与群体公平性。
- 引入置信区间处理公平性度量不确定性，避免误判不公平。
- 在保持高准确率前提下，显著减少对弱势群体的歧视性预测。
在人工智能日益影响人类生活的时代，开发能考虑潜在歧视的模型至关重要。本文提出一种新型分类树算法，采用新颖的分裂准则，将公平性调整融入树构建过程。该方法整合了公平性感知的不纯度度量，平衡预测准确性与受保护群体间的公平性。通过确保每个分裂节点同时考量分类误差的降低和公平性，算法鼓励减少歧视性的分裂。尤为重要的是，在惩罚不公平分裂时，我们利用公平性度量的置信区间而非点估计，以考虑其不确定性。在基准数据集和合成数据集上的实验结果表明，相比传统分类树，该方法能有效降低歧视性预测，且整体准确率损失不显著。
原文摘要 · Abstract (English)
In an era where artificial intelligence and machine learning algorithms increasingly impact human life, it is crucial to develop models that account for potential discrimination in their predictions. This paper tackles this problem by introducing a new classification tree algorithm using a novel splitting criterion that incorporates fairness adjustments into the tree-building process. The proposed method integrates a fairness-aware impurity measure that balances predictive accuracy with fairness across protected groups. By ensuring that each splitting node considers both the gain in classification error and the fairness, our algorithm encourages splits that mitigate discrimination. Importantly, in penalizing unfair splits, we account for the uncertainty in the fairness metric by utilizing its confidence interval instead of relying on its point estimate. Experimental results on benchmark and synthetic datasets illustrate that our method effectively reduces discriminatory predictions compared to traditional classification trees, without significant loss in overall accuracy.
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