Detecting labeling bias using influence functions

TL;DR

This paper explores the use of influence functions to detect labeling bias and mislabeled samples in datasets, demonstrating promising results on MNIST and CheXpert datasets.

Contribution

It introduces a novel pipeline leveraging influence functions to identify labeling errors, especially in complex datasets like CheXpert.

Findings

Successfully detected nearly 90% of mislabeled samples in MNIST.

Mislabeled samples in CheXpert show higher influence scores.

Influence functions can effectively reveal label errors in real-world datasets.

Abstract

Labeling bias arises during data collection due to resource limitations or unconscious bias, leading to unequal label error rates across subgroups or misrepresentation of subgroup prevalence. Most fairness constraints assume training labels reflect the true distribution, rendering them ineffective when labeling bias is present; leaving a challenging question, that \textit{how can we detect such labeling bias?} In this work, we investigate whether influence functions can be used to detect labeling bias. Influence functions estimate how much each training sample affects a model's predictions by leveraging the gradient and Hessian of the loss function -- when labeling errors occur, influence functions can identify wrongly labeled samples in the training set, revealing the underlying failure mode. We develop a sample valuation pipeline and test it first on the MNIST dataset, then scaled to the more complex CheXpert medical imaging dataset. To examine label noise, we introduced controlled errors by flipping 20\% of the labels for one class in the dataset. Using a diagonal Hessian approximation, we demonstrated promising results, successfully detecting nearly 90\% of mislabeled samples in MNIST. On CheXpert, mislabeled samples consistently exhibit higher influence scores. These results highlight the potential of influence functions for identifying label errors.