Deploying Trustworthy Generative AI

Generative AI models and applications are being rapidly deployed across several industries, but there are several ethical and social considerations that need to be addressed. These concerns include lack of interpretability, bias and discrimination, privacy, lack of model robustness, fake and misleading content, copyright implications, plagiarism, and environmental impact associated with training and inference of generative AI models. In this talk, we first motivate the need for adopting responsible AI principles when developing and deploying large language models (LLMs) and other generative AI models, and provide a roadmap for thinking about responsible AI for generative AI in practice. Focusing on real-world LLM use cases (e.g., evaluating LLMs for robustness, security, bias, etc.), we present practical solution approaches / guidelines for applying responsible AI techniques effectively and discuss lessons learned from deploying responsible AI approaches for generative AI applications in practice. By providing real-world generative AI use cases, lessons learned, and best practices, this talk will enable practitioners to build more reliable and trustworthy generative AI applications. Please take a look at our recent ICML/KDD/FAccT tutorial (https://sites.google.com/view/responsible-gen-ai-tutorial) for an expanded version of this talk.