[论文解读] Identifying and Correcting Label Bias in Machine Learning

Heinrich Jiang, Ofir Nachum|arXiv (Cornell University)|Jan 15, 2019

Ethics and Social Impacts of AI参考文献 41被引用 116

一句话总结

本文提出一个模型来描述偏见标签如何产生，并提出一种重新加权方案，在不改变标签的前提下，在多种公平性 notions 下获得无偏的分类器。它给出理论保证并在标准公平数据集上进行了实证验证。

ABSTRACT

Datasets often contain biases which unfairly disadvantage certain groups, and classifiers trained on such datasets can inherit these biases. In this paper, we provide a mathematical formulation of how this bias can arise. We do so by assuming the existence of underlying, unknown, and unbiased labels which are overwritten by an agent who intends to provide accurate labels but may have biases against certain groups. Despite the fact that we only observe the biased labels, we are able to show that the bias may nevertheless be corrected by re-weighting the data points without changing the labels. We show, with theoretical guarantees, that training on the re-weighted dataset corresponds to training on the unobserved but unbiased labels, thus leading to an unbiased machine learning classifier. Our procedure is fast and robust and can be used with virtually any learning algorithm. We evaluate on a number of standard machine learning fairness datasets and a variety of fairness notions, finding that our method outperforms standard approaches in achieving fair classification.

研究动机与目标

• Motivote and formalize how label bias arises from biased labeling processes while aiming for unbiased ground-truth outcomes.
• Propose a data re-weighting technique to recover unbiased labels without modifying observed labels or features.
• Provide theoretical guarantees showing equivalence between re-weighted biased-label training and unbiased-label training.
• Develop an algorithm to estimate bias coefficients and apply them to training with common classifiers.
• Demonstrate effectiveness across multiple fairness notions and benchmark datasets.

提出的方法

• Assume an unknown unbiased ground-truth label function and a biased observed label function related through a constrained optimization with KL-divergence.
• Derive a closed-form relation: y_bias is proportional to y_true times exp(-sum_k lambda_k c_k(x,y)).
• Show that y_true is proportional to y_bias times exp(+sum_k lambda_k c_k(x,y)).
• Propose a weighting w(x,y) = exp(sum_k lambda_k c_k(x,y)) normalized by sum_y w(x,y) to re-weight training examples.
• Prove that training with weights on biased labels is equivalent to training on true labels under a distribution tilde P.
• Provide an algorithm (Algorithm 1) to learn coefficients lambda_k and train with re-weighted loss to satisfy fairness constraints.]
• research_questions:["如何在真实标签未知但期望实现公平约束时，对标签偏差进行数学建模？","是否可以对带偏见的标注数据进行重新加权，使训练动态如同在无偏标签上优化？","如何学习并更新偏差系数，以满足人口统计平等、差异影响、机会均等和等机会等？","对使用重新加权的带偏数据学习的理论保证（一致性速率）是什么？","与后处理和拉格朗日方法相比，该方法在标准公平数据集和 notions 上的表现如何？"]
• key_findings:[
• A closed-form expression links observed biased labels to the underlying unbiased labels via exponential weights tied to fairness constraints.
• The re-weighting scheme yields an equivalent training objective to learning with unbiased labels under a modified feature distribution, under mild conditions.
• An iterative Algorithm 1 can learn the bias coefficients and sample weights to achieve fairness notions such as demographic parity, disparate impact, and equal opportunity.
• Empirical evaluation on standard fairness datasets shows the proposed re-weighting method outperforming baseline approaches in fairness violations across several notions.
• Theoretical results establish finite-sample rates of consistency for the weighted estimator, with manifold-aware rates improving dimensionality dependence.

实验结果

主要发现

• A closed-form expression links observed biased labels to the underlying unbiased labels via exponential weights tied to fairness constraints.
• The re-weighting scheme yields an equivalent training objective to learning with unbiased labels under a modified feature distribution, under mild conditions.
• An iterative Algorithm 1 can learn the bias coefficients and sample weights to achieve fairness notions such as demographic parity, disparate impact, and equal opportunity.
• Empirical evaluation on standard fairness datasets shows the proposed re-weighting method outperforming baseline approaches in fairness violations across several notions.
• Theoretical results establish finite-sample rates of consistency for the weighted estimator, with manifold-aware rates improving dimensionality dependence.