795: Fast-Evolving Data and AI Regulatory Frameworks, with Dr. Gina Guillaume-Joseph

Gina Guillaume-Joseph talks to Jon Krohn about the data and regulatory frameworks set to transform the AI industry and why that’s important to anyone working with data. This episode offers a solid path to understanding AI regulation’s past, present and future. Gina walks listeners through the AI Bill of Rights, the NIST AI Risk Framework and the MITRE ATLAS threat model. This episode is brought to you by AWS Inferentia and AWS Trainium, by Crawlbase, the ultimate data crawling platform, and by Babbel, the science-backed language-learning platform. Interested in sponsoring a SuperDataScience Podcast episode? Email natalie@superdatascience.com for sponsorship information. In this episode you will learn: • What “responsible AI” means [08:14] • Why the federal government should be behind AI regulation [12:22] • The US vs EU on AI regulation [18:46] • About the AI Bill of Rights [26:14] • About MITRE and the MITRE Atlas [37:19] • What a systems engineer does [54:11] Additional materials: www.superdatascience.com/795

Om Podcasten

The latest machine learning, A.I., and data career topics from across both academia and industry are brought to you by host Dr. Jon Krohn on the Super Data Science Podcast. As the quantity of data on our planet doubles every couple of years and with this trend set to continue for decades to come, there's an unprecedented opportunity for you to make a meaningful impact in your lifetime. In conversation with the biggest names in the data science industry, Jon cuts through hype to fuel that professional impact. Whether you're curious about getting started in a data career or you're a deep technical expert, whether you'd like to understand what A.I. is or you'd like to integrate more data-driven processes into your business, we have inspiring guests and lighthearted conversation for you to enjoy. We cover tools, techniques, and implementation tricks across data collection, databases, analytics, predictive modeling, visualization, software engineering, real-world applications, commercialization, and entrepreneurship − everything you need to crush it with data science.