Natural language processing (NLP) has become somewhat well-known because of its many uses; deep neural networks have driven major developments. Still, there are difficulties, especially in Arabic NLP, where the language’s large vocabulary of over 12 million words and several dialects cause special issues. Arabic has a large speaker base; however, NLP studies in this language find challenges, particularly with class imbalance. Many times, standard class balancing methods overlook intra-class similarity, a crucial element influencing model training. We present a new approach for computing intra-class similarity using cosine similarity and embedding models to find ideal class weights for model training, hence bridging this difference. On two benchmark datasets—the Arabic Semantic Question Similarity dataset (NSURL) and the Microsoft Research Paragraph Corpus (MRPC)—we assessed our method. With an accuracy of state-of-the-art 83.25% on the MRPC dataset and 96.931% on the NSURL dataset, our approach proved successful in improving model performance in Arabic text classification.

The rapid adoption of multimodal learning systems across high-stakes applications has intensified concerns about fairness and social bias, yet existing research remains scattered across modalities, tasks, protected attributes, mitigation strategies, and evaluation methodologies. This survey provides an overview of fairness and social bias in multimodal artificial intelligence. By reviewing 44 papers and 16 datasets, we synthesize the current landscape of fairness research in multimodal systems and identify emerging challenges and research opportunities. Our primary contribution is twofold. First, we introduce a multi-dimensional taxonomy that organizes the literature across nine operational dimensions, providing a unified perspective on multimodal fairness research. Second, we propose an end-to-end operational lifecycle framework that illustrates how biases emerge within shared latent spaces and identifies targeted pre-processing, training-time, and post-training intervention strategies. Overall, this survey discusses key performance-fairness trade-offs and provides a structured roadmap for evaluating and mitigating bias, ultimately supporting the development of more equitable multimodal AI systems.