AI, Law, and Liability: Who's Winning, What's Stopping Us, and What's Next

Artificial intelligence platforms are reshaping industries - from finance and healthcare to law and cybersecurity. But as adoption accelerates, organizations face dual challenges: the legal uncertainties around liability, data use, and compliance, and the technical blockers that slow implementation, such as lack of model transparency, interoperability gaps, security vulnerabilities, and massive energy demands.

Unlike traditional technologies, AI platforms introduce novel legal risks. If a generative AI system hallucinates financial advice, an autonomous agent triggers unauthorized transactions, or a model amplifies bias - who is accountable? Developers, deployers, vendors, or end users? Can current doctrines of tort law, contract liability, and intellectual property stretch to cover these scenarios?

This program, presented by Ewa Kozlowska Esq., will examine how legal frameworks intersect with the realities of AI technology. Attendees will explore how adoption is blocked not just by regulation but also by platform limitations (explainability, bias, compute resources, data provenance) - and how those blockers translate into legal exposure.

Learning Objectives: 

1. Understand how tort, contract, IP, and consumer protection laws apply to AI platforms

2. Identify technical adoption blockers - from explainability gaps to interoperability, data integrity, energy costs, and cyber risks - and how they create downstream liability

3. Explore how global regulatory regimes (EU AI Act, U.S. state-level laws, China's algorithm rules) frame obligations for AI providers and users

4. Analyze case studies of AI platform failures (fraud exploitation, ransomware using AI, biased outputs, faulty legal advice) and the litigation strategies evolving around them

5. Learn best practices for structuring AI risk governance: vendor contracts, liability clauses, insurance coverage, model validation, and monitoring