# Resampled Datasets Are Not Enough: Mitigating Societal Bias Beyond Single Attributes

Source: [Resampled Datasets Are Not Enough: Mitigating Societal Bias Beyond Single Attributes](<https://www.sota2.com/research/paper/68fa74131e209131cea5face>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T03:02:03.292Z

Authors: Yusuke Hirota, Jerone T. A. Andrews, Dora Zhao, Orestis Papakyriakopoulos, Apostolos Modas, Yuta Nakashima, Alice Xiang

Published: 2024-07-04T04:06:43.000Z

## Abstract

We tackle societal bias in image-text datasets by removing spurious correlations between protected groups and image attributes. Traditional methods only target labeled attributes, ignoring biases from unlabeled ones. Using text-guided inpainting models, our approach ensures protected group independence from all attributes and mitigates inpainting biases through data filtering. Evaluations on multi-label image classification and image captioning tasks show our method effectively reduces bias without compromising performance across various models.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2407.03623>)

[PDF](<https://arxiv.org/pdf/2407.03623.pdf>)