The future of AI-powered content recommendation demands more than just technical precision—it requires building systems that users can trust. I will share frameworks and methodologies for evaluating recommendation quality beyond traditional metrics, incorporating dimensions of transparency, accountability, and user agency. Through case studies from the industry, attendees will gain insights into effectively navigating trade-offs between innovation and responsible deployment, establishing appropriate human oversight mechanisms, and designing recommendation systems that not only understand content but respect the complex human values and preferences they serve. This session offers practical guidance for organizations seeking to harness the power of multimodal LLMs while maintaining robust ethical standards in their recommendation practices.