Panel Discussion: AI Regulation and Fairness in the Generative AI Era

Join us for a dynamic panel discussion on "AI Regulation and Fairness in the Generative AI Era," tailored for the Black in AI community at NeurIPS 2024. This session will explore the critical intersection of AI technology, ethics, and community advocacy, focusing on how we can ensure that the voices and communities of practitioners of African descent are included in shaping AI regulations. Our expert panel will discuss the challenges of bias in generative AI, the importance of equitable representation, and strategies for fostering innovation while promoting fairness. Attendees will leave with insights into actionable steps for creating a more inclusive AI landscape. Join us to engage in this vital conversation about the future of AI and its impact on society.

PANELISTS

Jessica Schrouff is a senior research scientist at Google DeepMind, working on responsible AI. Previously, she was at Google Research where she investigated responsible machine learning for healthcare. Before joining Alphabet in 2019, she was a Marie Curie post-doctoral fellow at University College London (UK) and Stanford University (USA), developing machine learning techniques for neuroscience discovery and clinical predictions. Throughout her career, Jessica's interests have lied not only in the technical advancement of machine learning methods, but also in critical aspects of their deployment such as their credibility, fairness, robustness or interpretability. She is also involved in DEI initiatives, such as Women in Machine Learning (WiML) and founded the Women in Neuroscience Repository.

Jonas NGNAWE is a Ph.D. Student in Computer Science at Université Laval and Mila-Quebec AI Institute, working on local robustness of Deep Neural Networks. Jonas holds a Master of Engineering in Computer Science from Ecole Polytechnique Yaoundé, a Master’s in Mathematical Sciences from the African Institute for Mathematical Sciences and a Master’s in Machine Learning at AMMI, the African Master’s in Machine Intelligence. Before starting his PhD, he was a Google AI resident at the Google AI lab in Accra, Ghana.

Yefing Li is an Associate Professor and Canada Research Chair (Tier 2) in Machine Learning for Biomedical Data Science at the Department of Computer Science, Department of Biological Sciences, and Centre for Biotechnology, Brock University, Canada. During 2015-2019, he was a Research Officer at the Digital Technologies Research Centre, National Research Council Canada (NRC). He received the NRC Rising Star Award in 2018. Prior to his joining to NRC, he was a post-doctorate at the Wasserman Laboratory of the Centre for Molecular Medicine and Therapeutics, University of British Columbia, Canada. He obtained his Ph.D. in Computer Science, from the University of Windsor, Canada, in 2013. His doctoral dissertation was recognized by a Gold Medal from the Governor General of Canada. His research interests include neural networks, machine learning, data science, optimization, bioinformatics, chemoinformatics, and drug design.

MODERATOR: Dr. Blessing Ogbuokiri is an Assistant Professor in the Department of Computer Science at Brock University, Canada, specializing in Machine Learning (ML) and its applications in health. He has extensive experience in ML, Natural Language Processing (NLP), and Theoretical Computing. Dr. Ogbuokiri focuses on developing predictive models for infectious diseases, with a particular emphasis on understanding disease transmission dynamics and evaluating public health interventions. He leads the Responsible and Applied Machine Learning Laboratory (RAML Lab) at Brock University, Canada, where he works on advancing ML methodologies and their applications to real-world challenges. Dr. Ogbuokiri collaborates across disciplines to address community-based infectious disease outbreaks, including COVID-19, Malaria, and Mpox.