论文标题
通过因果关系，信息理论偏见减少了伪造相关性
Information-Theoretic Bias Reduction via Causal View of Spurious Correlation
论文作者
论文摘要
我们通过因果关系解释提出了一种信息理论偏见测量技术，这有效地通过利用有条件的相互信息来识别特征级算法偏见。尽管已经提出并广泛研究了几种偏差测量方法，以实现各种任务（例如面部识别）的算法公平性，但它们的准确性或基于logit的指标容易导致琐碎的预测得分调整，而不是基本偏见。因此，我们设计了一个针对算法偏见的新型伪造框架，该框架结合了通过提出的信息理论偏见测量方法得出的偏差正则化损失。此外，我们基于随机标签噪声提出了一种简单而有效的无监督式伪标技术，该技术不需要明确监督偏见信息。通过对多种标准基准的广泛实验，在不同的现实情况下，在各种现实的情况下验证了提出的偏差测量方法和歧义方法。
We propose an information-theoretic bias measurement technique through a causal interpretation of spurious correlation, which is effective to identify the feature-level algorithmic bias by taking advantage of conditional mutual information. Although several bias measurement methods have been proposed and widely investigated to achieve algorithmic fairness in various tasks such as face recognition, their accuracy- or logit-based metrics are susceptible to leading to trivial prediction score adjustment rather than fundamental bias reduction. Hence, we design a novel debiasing framework against the algorithmic bias, which incorporates a bias regularization loss derived by the proposed information-theoretic bias measurement approach. In addition, we present a simple yet effective unsupervised debiasing technique based on stochastic label noise, which does not require the explicit supervision of bias information. The proposed bias measurement and debiasing approaches are validated in diverse realistic scenarios through extensive experiments on multiple standard benchmarks.
