Regulatory Ramblings: Episode 69 - Human Intelligence vs. Machine Judgment with Nigel Morris-Cotterill and Patrick Dransfield
Early Returns Podcast - Oliver Roberts: AI and the Law, and an Education
AI Legislation: The Statewide Spotlight - Regulatory Oversight Podcast
AI Legislation: The Statewide Spotlight — The Consumer Finance Podcast
AI in Employment: Navigating the Legal Landscape with Lessons from I, Robot — The Good Bot Podcast
5 Key Takeaways | Artificial Intelligence: What Tax Professionals Need to Know
The FinReg Frontier: AI and Machine Learning in Consumer Finance — The Consumer Finance Podcast
Key Discovery Points: AI Says AI Will Replace Paralegals… But Not So Fast!
AI Discrimination and Emerging Best Practices – Part 2 - The Good Bot Podcast
Podcast - Decoding the Future of AI Regulation and Frontier Models
A Conversation with Alexandra Johnson, CEO/Co-Founder of Rubber Ducky Labs, AI Recommender Systems Pioneer
AI Discrimination and Emerging Best Practices – Part 1 — The Good Bot Podcast
Dan Rudoy Examines the Impact of AI on IP Strategy
Seeing into the Future: Moving Beyond AI to Visual Intelligence with Oculi CEO Charbel Rizk
A Brief Overview of Colorado’s Recently Enacted AI Law
Spotlight on Lightmatter, the Photonic (Super) Computing Company Ready to Power the AI Revolution
Podcast - Part 2: An FTC Official Speaks About the Regulation of AI Technology
A Deep Dive into HUD's New Guidance on AI-Driven Targeted Advertising — The Consumer Finance Podcast
[Webinar] AI and Data Privacy: Minimizing Risk and Maximizing Opportunity
AD Nauseam: AI – We Had to Discuss it Eventually – Part 2
“With great power comes great responsibility,” and in the rapidly evolving landscape of Artificial Intelligence (“AI”), the intersection of innovation and legal responsibility is becoming increasingly complex. As AI becomes...more
Artificial Intelligence (“AI”) use in business has proliferated in recent years; risks arising from this therefore must be managed. Whilst the use of AI can drive significant efficiency gains for most businesses, the...more
The rapid rise of artificial intelligence (“AI”) is reshaping industries, transforming economies, and presenting profound legal and ethical challenges....more
According to a Gartner survey, by 2026, more than 80% of enterprises will have implemented generative AI, which will have a significant impact on their operations. The aim is to enhance overall business processes and...more
On March 7, Deputy Attorney General Lisa Monaco delivered the keynote remarks at the American Bar Association’s (ABA) 39th National Institute on White Collar Crime. She noted that artificial intelligence (AI) “holds great...more
In Short - The Background: The prevalence of generative artificial intelligence ("GenAI") is rapidly expanding, providing vast opportunities for efficiency and innovation, while also creating new risks....more
Recent breakthroughs in generative artificial intelligence (AI) have captured significant media attention. Developers argue that the technology, which learns from data to produce new text, visual, or audio content based on a...more
One of the primary concerns surrounding ChatGPT and AI is the security of the data used to train and operate these machines and the potential privacy implications if the data is mishandled or intercepted by malicious actors. ...more
Artificial intelligence (AI) and machine learning applications like ChatGPT are the hot new technologies expected to changes our lives fundamentally. Taking the human element out of monotonous tasks promises to increase...more
AI Training data play a key role in the development of AI systems. However, they contain a risk of being inaccurate, discriminating or imbalanced. Accordingly, they can trigger significant liability claims. The current and...more
Artificial intelligence (AI) and machine-learning algorithms are powerful tools that can automate or inform decision-making. At the same time, those algorithms can be quite complex and appear to be a “black box”—inscrutable...more
Bias in AI can result from assumptions in the machine learning process, or as a result of data that is imbalanced or incomplete and does not reflect a true representation of the relevant population. Examples of such skewed...more