Reducing Gender and Racial Biases in Multimodal Vision Language Models via Contrastive Debiasing and Representative Dataset Synthesis - Dylan Clarke (Computational Intelligence Systems, 2026)
The rapid proliferation of multimodal vision language models has transformed the landscape of artificial intelligence, enabling unprecedented capabilities in image captioning, visual question answering, and generative content synthesis. However, these advancements have also surfaced profound ethical concerns regarding the systematic reproduction of gender and racial biases inherent in foundational training data. This research investigates a holistic systems-level framework for mitigating these biases through a dual-pronged strategy involving contrastive debiasing architectures and representative dataset synthesis. Rather than relying on post-hoc filtering or simple data augmentation, the proposed approach integrates fairness as a core architectural constraint during the alignment phase of multimodal training. By synthesizing diverse datasets that fill historical representation gaps and implementing contrastive learning objectives that penalize stereotypical associations between protected attributes and unrelated semantic features, the framework aims to decouple social identity from task-related performance. The discussion encompasses the structural trade-offs between model accuracy and fairness, the infrastructure required for large-scale synthetic data generation, and the governance frameworks necessary for auditing multimodal systems. Ultimately, this paper argues that achieving equitable artificial intelligence requires an interdisciplinary commitment to re-engineering the socio-technical pipeline, moving beyond algorithmic fixes toward sustainable, bias-aware systemic infrastructures.