Generative AI meets Responsible AI: Practical Challenges and Opportunities

Generative AI models and applications are being rapidly developed and deployed across a wide spectrum of industries and applications ranging from writing and email assistants to graphic design and art generation to educational assistants to coding to drug discovery. However, there are several ethical and social considerations associated with generative AI models and applications. 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 tutorial, we first motivate the need for adopting responsible AI principles when developing and deploying large language models (LLMs) and other generative AI models, as part of a broader AI model governance and responsible AI framework, from societal, legal, user, and model developer perspectives, and provide a roadmap for thinking about responsible AI for generative AI in practice. We provide a brief technical overview of text and image generation models, and highlight the key responsible AI desiderata associated with these models. We then describe the technical considerations and challenges associated with realizing the above desiderata in practice. We focus on real-world generative AI use cases spanning domains such as media generation, writing assistants, copywriting, code generation, and conversational assistants, present practical solution approaches / guidelines for applying responsible AI techniques effectively, discuss lessons learned from deploying responsible AI approaches for generative AI applications in practice, and highlight the key open research problems. We hope that our tutorial will inform both researchers and practitioners, stimulate further research on responsible AI in the context of generative AI, and pave the way for building more reliable and trustworthy generative AI applications in the future.