Work

BAdd: Bias Mitigation through Bias Addition

Year: 2025

Type: conference-paper

Abstract: Computer vision datasets often exhibit biases in the form of spurious correlations between certain attributes and target variables. While recent efforts aim to mitigate such biases and foster bias-neutral representations, they fail in complex real-world scenarios. In particular, existing methods excel in controlled experiments on benchmarks with single-attribute injected biases, but struggle with complex multi-attribute biases that naturally occur in established CV datasets. In this paper, we introduce BAdd, a simple yet effective method that allows for learning bias-neutral representations invariant to bias-inducing attributes. This is achieved by injecting features encoding these attributes into the training process. BAdd is evaluated on seven benchmarks and exhibits competitive performance, surpassing state-of-the-art methods on both single- and multi-attribute bias settings. Notably, it achieves +27.5% and +5.5% absolute accuracy improvements on the challenging multi-attribute benchmarks, FB-Biased-MNIST and CelebA, respectively.

Source: Zenodo (CERN European Organization for Nuclear Research)

DOI: 10.5281/zenodo.17107123

Authors: Sarridis, Ioannis, Koutlis, Christos, Papadopoulos, Symeon, Diou, Christos

Institutions: Centre for Research and Technology Hellas, Harokopio University of Athens

Language: English

Topic: Ethics and Social Impacts of AI

Subfield: Safety Research

Field: Social Sciences

Domain: Social Sciences

SDG: Reduced inequalities

Open Access status: green

Funder: European Commission