Benchmarking Bias Mitigation Toward Fairness Without Harm from Vision to LVLMs

TL;DR

This paper introduces NH-Fair, a comprehensive benchmark for evaluating bias mitigation methods in vision and vision-language models, emphasizing standardized evaluation, hyperparameter tuning, and practical fairness strategies.

Contribution

It presents a unified benchmarking framework, systematic hyperparameter tuning insights, and evidence that data augmentation outperforms many debiasing methods for fairness without utility loss.

Findings

Many debiasing methods do not outperform a well-tuned ERM baseline.

Data augmentation consistently improves fairness without sacrificing utility.

LVLMs have higher accuracy but still exhibit subgroup disparities.

Abstract

Machine learning models trained on real-world data often inherit and amplify biases against certain social groups, raising urgent concerns about their deployment at scale. While numerous bias mitigation methods have been proposed, comparing the effectiveness of bias mitigation methods remains difficult due to heterogeneous datasets, inconsistent fairness metrics, isolated evaluation of vision versus multi-modal models, and insufficient hyperparameter tuning that undermines fair comparisons. We introduce NH-Fair, a unified benchmark for fairness without harm that spans both vision models and large vision-language models (LVLMs) under standardized data, metrics, and training protocols, covering supervised and zero-shot regimes. Our key contributions are: (1) a systematic ERM tuning study that identifies training choices with large influence on both utility and disparities, yielding empirically grounded guidelines to help practitioners reduce expensive hyperparameter tuning space in achieving strong fairness and accuracy; (2) evidence that many debiasing methods do not reliably outperform a well-tuned ERM baseline, whereas a composite data-augmentation method consistently delivers parity gains without sacrificing utility, emerging as a promising practical strategy. (3) an analysis showing that while LVLMs achieve higher average accuracy, they still exhibit subgroup disparities, and gains from scaling are typically smaller than those from architectural or training-protocol choices. NH-Fair provides a reproducible, tuning-aware pipeline for rigorous, harm-aware fairness evaluation.