The field of multimodal analysis and AI-driven decision making is rapidly evolving, with a focus on developing more interpretable and explainable models. Recent studies have explored the application of multimodal fusion techniques to predict commercial memorability, assess mortgage risk, and detect greenwashing in oil and gas advertising. Additionally, researchers have investigated the use of AI-enabled representatives to empower retail investors and improve shareholder democracy. Noteworthy papers in this area include DAO-AI, which evaluates collective decision-making through agentic AI in decentralized governance, and VOGUE, a multimodal dataset for conversational recommendation in fashion. CreditXAI, a multi-agent system for explainable corporate credit rating, and MMPersuade, a dataset and evaluation framework for multimodal persuasion, also demonstrate significant advancements in the field.