Abstract: 

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, etc. using https://github.com/fiddler-labs/fiddler-auditor), 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 researchers & 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.

Session Outline:

The tutorial will consist of the following parts: (1) Introduction and overview of the generative AI landscape, (2) Technical and ethical challenges with generative AI, and (3) Solutions for alleviating the challenges with real-world use cases and case studies (including practical challenges and lessons learned in industry).

Please see the webpage of our recent ICML/KDD/FAccT tutorial, https://sites.google.com/view/responsible-gen-ai-tutorial for a more detailed outline and description of this talk.

Background Knowledge:

The goal of the tutorial is to highlight the key responsible AI challenges associated with generative AI models and applications, present practical solution approaches / guidelines for applying responsible AI techniques effectively, and discuss lessons learned from deploying responsible AI approaches for generative AI applications across industries.

The attendees will learn about the foundations and the applications of responsible AI techniques for generative AI models and systems in industry, real-world LLM use cases (e.g., evaluating LLMs for robustness, security, etc. using open source tools such as https://github.com/fiddler-labs/fiddler-auditor), practical solution approaches / guidelines for applying responsible AI techniques effectively, and lessons learned from deploying responsible AI approaches for generative AI applications in practice.

This tutorial is aimed at attendees with a wide range of interests and backgrounds, including practitioners interested in implementing responsible AI tools for various generative AI applications. We will not assume any prerequisite knowledge, and present the advances, challenges, and opportunities of responsible generative AI by building intuition to ensure that the material is accessible to all ODSC attendees.

Bio: 

Krishnaram Kenthapadi is the Chief AI Officer & Chief Scientist of Fiddler AI, an enterprise startup building a responsible AI and ML monitoring platform. Previously, he was a Principal Scientist at Amazon AWS AI, where he led the fairness, explainability, privacy, and model understanding initiatives in the Amazon AI platform. Prior to joining Amazon, he led similar efforts at the LinkedIn AI team, and served as LinkedIn’s representative in Microsoft’s AI and Ethics in Engineering and Research (AETHER) Advisory Board. Previously, he was a Researcher at Microsoft Research Silicon Valley Lab. Krishnaram received his Ph.D. in Computer Science from Stanford University in 2006. He serves regularly on the senior program committees of FAccT, KDD, WWW, WSDM, and related conferences, and co-chaired the 2014 ACM Symposium on Computing for Development. His work has been recognized through awards at NAACL, WWW, SODA, CIKM, ICML AutoML workshop, and Microsoft’s AI/ML conference (MLADS). He has published 50+ papers, with 7000+ citations and filed 150+ patents (70 granted). He has presented tutorials on privacy, fairness, explainable AI, model monitoring, responsible AI, and generative AI at forums such as ICML, KDD, WSDM, WWW, FAccT, and AAAI, given several invited industry talks, and instructed a course on responsible AI at Stanford.