通过增强数据缓解稀疏张量中的群体偏差，提升公平性同时保持高精度。
Improving Group Fairness in Tensor Completion via Imbalance Mitigating Entity Augmentation
- 引入增强实体填补稀疏数据，缓解群体间不平衡问题。
- 在多个数据集上，最高降低36%的均方误差和59%的平均绝对差异误差。
- 适用于需兼顾准确率与公平性的推荐系统、医疗数据分析等场景。
群体公平性在张量分解中至关重要，可防止基于性别、年龄等社会因素的歧视。尽管已有少量研究关注张量分解中的群体公平性，但普遍伴随性能下降。为此，我们提出STAFF（Sparse Tensor Augmentation For Fairness），通过最小化不同群体间的完成误差差距，同时降低整体张量完成误差，来提升公平性。核心思想是通过包含足够观测值的增强实体对张量进行扩充，以缓解稀疏张量中的不平衡与群体偏见。我们在多种数据集上评估了该方法在传统与深度学习张量模型下的表现。STAFF在完成误差与群体公平性之间表现出最佳权衡；相比次优基线，最多可降低36%的均方误差（MSE）和59%的平均绝对差异误差（MADE）。
原文摘要 · Abstract (English)
Group fairness is important to consider in tensor decomposition to prevent discrimination based on social grounds such as gender or age. Although few works have studied group fairness in tensor decomposition, they suffer from performance degradation. To address this, we propose STAFF(Sparse Tensor Augmentation For Fairness) to improve group fairness by minimizing the gap in completion errors of different groups while reducing the overall tensor completion error. Our main idea is to augment a tensor with augmented entities including sufficient observed entries to mitigate imbalance and group bias in the sparse tensor. We evaluate \method on tensor completion with various datasets under conventional and deep learning-based tensor models. STAFF consistently shows the best trade-off between completion error and group fairness; at most, it yields 36% lower MSE and 59% lower MADE than the second-best baseline.
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