AI Survival Guide: Best Practices to Mitigate AI Litigation Risk
Learn about recent trends in high-stakes litigation involving AI technologies and best practices to consider to mitigate AI litigation risk.
Organizations using artificial intelligence-based technologies that perform facial recognition or other facial analysis, website advertising, profiling, automated decision making, educational operations, clinical medicine, generative AI, and more, increasingly face the risk of being targeted by class action lawsuits and government enforcement actions alleging that they improperly obtained, disclosed, and misused personal data of website visitors, employees, customers, students, patients, and others, or that they infringed copyrights, fixed prices, and more. These disputes often seek millions or billions of dollars against businesses of all sizes.
This webinar identifies recent trends in such varied but similar AI litigation, draws common threads, and discusses best practices that corporate counsel should consider to mitigate AI litigation risk, including adding or updating arbitration clauses to mitigate the risks of mass arbitration; establishing an AI Committee; collaborating with IT, cybersecurity, and risk/compliance departments and outside advisors to identify and manage AI risks; and updating notices to third parties and vendor agreements.
Learning Objectives
Identify Recent Trends in High-Stakes Litigation Involving AI Technologies
“Biometric” Technology Litigation
Website Advertising Technology (Adtech) Litigation
Profiling / Automated Decision Making (ADM) Technology Litigation
Other AI Litigation – Key Examples (Healthcare Tech & Gen AI)
Identify State Laws Governing AI
Identify Best Practices to Mitigate AI Litigation Risk
Steps to Mitigate the Risk of Mass Arbitration
Steps to Mitigate the Risk of Legal Noncompliance
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Speakers
Mr. Maatman also writes and lectures extensively on class action and employment litigation topics. He has authored six books on employment law topics and has spoken to employer groups throughout the United States, as well as in Asia, Europe, Canada and Mexico. Mr. Maatman is the author and editor of a widely circulated, highly regarded industry class action report, published yearly since 2003. The report, called by EPLiC Magazine "the bible on class actions that no corporate counsel should do without," is widely praised for its sharp analysis backed by comprehensive research, helps corporate employers navigate an increasingly volatile class action landscape.
Mr. Maatman is recognized regularly by legal publications for his excellent work on behalf of clients. He is a 2021 Law360 MVP for Employment Law, which is his sixth such honor from Law360 since 2013. Winners of this accolade have distinguished themselves from their peers by securing impressive successes in high-stakes litigation, complex global matters and record-breaking deals. Overall, Mr. Maatman has received more Law360 MVP awards than any other attorney in the United States.
Mr. Maatman is a graduate of Northwestern University School of Law (J.D. 1981) and Washington and Lee University (B.A., magna cum laude, 1978). He has served as an adjunct professor of law at Northwestern for more than 30 years.
He has successfully litigated data privacy and cybersecurity issues under states’ wiretap acts, consumer fraud statutes, and common laws; the Illinois Biometric Information Privacy Act (BIPA); the Electronic Communications Privacy Act (ECPA); the California Consumer Privacy Act (CCPA); the EU’s General Data Protection Regulation (GDPR); and other laws.
Mr. Donoho leads case teams from complaint to resolution. He has first- and second-chaired trials and mediations. Clients often compliment Mr. Donoho’s legal strategies, courtroom performances, persuasive writings, settlement negotiations, handling of depositions, management of complex discovery, dedication, and responsiveness to their needs.
Mr. Donoho is a graduate of the University of Chicago Law School (J.D., 2009) and the University of Illinois at Urbana-Champaign (B.S., Computer Engineering, 1999).