Imagine an AI that can not only read text but also understand images and sounds. That's the promise of multimodal AI, a field aiming to create systems that process and integrate information from multiple sources, much like humans do. But what if these advanced AI models inherit and amplify the biases present in the data they learn from? This research paper delves into the critical issue of fairness and bias in multimodal AI, exploring how biases like stereotypes, misogyny, and racial prejudice can creep into these seemingly objective systems. The study highlights the surprising finding that multimodal models, while powerful, can actually exacerbate biases compared to models that focus on only one type of data, like text. For example, certain AI models were found to generate sexualized images of women more frequently than men, even with seemingly harmless prompts. The paper examines popular multimodal models like CLIP and Stable Diffusion, revealing how biases in their massive training datasets—often scraped from the internet—lead to skewed outputs. The researchers offer several strategies to combat these biases. One key recommendation is a shift from simply collecting vast quantities of data to carefully curating higher-quality, more representative datasets. Other approaches include counterfactual data augmentation, which involves creating alternative examples to challenge biased associations, and improved filtering methods to remove harmful content from training data. The research underscores the need for ongoing vigilance in developing multimodal AI. While technical solutions are essential, the authors emphasize the importance of social analysis and ethical considerations to ensure these technologies are used responsibly and don't perpetuate harmful biases.