Fostering fairness in image classification through awareness of sensitive dataColakovic, Ivona (Avtor) Karakatič, Sašo (Avtor) fairnesssearch-basimage classificationmachine learningsupervised learnignneural networksMachine 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.Elsevier20252025-04-23 15:57:47Članek v reviji92602UDK: 004.8COBISS_ID: 232495363DOI: 10.1016/j.asoc.2025.113090ISSN pri članku: 1872-9681sl© 2025 The Authors