Fostering fairness in image classification through awareness of sensitive data
Year: 2025
Type: article
Abstract: Machine learning (ML) has demonstrated remarkable ability to uncover hidden patterns in data. However, the presence of biases and discrimination originating from the data itself and, consequently, emerging in the ML outcomes, remains a pressing concern. With the exponential growth of unstructured data, such as images, fairness has become increasingly critical, as neural network (NN) models may inadvertently learn and perpetuate societal and historical biases. To address this challenge, we propose a fairness-aware loss function that iteratively prioritizes the worst-performing sensitive group during NN training. This approach aims to balance treatment quality across sensitive groups, achieving fairer image classification outcomes while incurring only a slight compromise in overall performance. Our method, evaluated on the FairFace dataset, demonstrates significant improvements in fairness metrics while maintaining comparable overall quality. These trade-offs highlight that the minor decrease in overall quality is justified by the improvement in fairness of the models. • We propose fairness-aware loss to improve group fairness in image classification. • It focuses on the worst-performing group during training to ensure balanced quality. • The results show superior performance in terms of fairness. • Improved fairness is achieved without significant drop in overall quality.
Source: Applied Soft Computing
Authors: Ivona Colakovic, Sašo Karakatič
Institution: University of Maribor
Language: English
Subfield: Safety Research
Field: Social Sciences
Domain: Social Sciences
SDG: Reduced inequalities
Open Access status: hybrid
Estimated APC paid: $3,730
OpenAlex ID: W4409200482