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AI in Real World Litigation Scenarios: Understanding Litigation-Focused Applications and Addressing Potential Pitfalls
Raising Awareness About the Need For Explainability, "Lost in the Middle" Issues, and "Hallucinations"
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Description
The rapidly evolving legal technology landscape has carved out a significant role for AI in litigation processes, presenting attorneys with new tools for handling cases, enhancing efficiency, and delivering meaningful results. However, the use of these technologies should be weighed against its potential drawbacks.
AI can benefit litigation processes and has a number of potential uses in areas such as discovery, document review, predictive analytics, case outcome analysis, demand letters, deposition analysis, and more.
The panel will dive into practical case studies showcasing how AI is already being used in real world litigation scenarios and the qualitative and quantitative impacts they have had on these cases, and in industries such as consumer warranty, personal injury, insurance, pharmaceutical, and employment.
The panel will then address some of the potential issues in using AI systems and a checklist to look for when evaluating and using such systems. This includes the ability to have some explainability, a phenomenon known as "lost in the middle" in longer context analysis, and the dreaded hallucination effect, and how to mitigate these risks. This segment will highlight the significance of transparency and ensuring proper understanding of AI reasoning for its litigation applications.
Listen as we offer an understanding of AI's growing role in litigation, its strengths, and its limitations and equip attendees with actionable knowledge enabling more effective and forward-thinking strategies in their legal practice.
Presented By
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Mr. Lee is the Co-Founder and CEO of LegalMation. He is dedicated to helping companies and law firms achieve greater profitability and efficiency through the innovative use of AI. Mr. Lee is also a founding partner of LTL Attorneys LLP, a nationally recognized litigation boutique. He is frequent speaker and commentator on AI and innovation in law, sharing insights and developments with peers and industry leaders.

As Chief Client Solutions Group Officer, Mr. Rueff is responsible for oversight of an innovation team at Baker Donelson which includes responsibilities for knowledge management, legal project management, matter budgeting, pricing, process improvement, machine learning/AI applications and legal research. He regularly works with lawyers and clients to develop unique process, pricing and technology solutions. Prior to law school, Mr. Rueff worked in the technology industry for a hardware and software manufacturer where he was first exposed to project management, agile and the development of software applications for the engineering industry. He has also been a practicing lawyer for more than 20 years, is PMP® certified through the Project Management Institute, is certified in the Toyota Manufacturing Production System (Lean) and is a member of the Professional Pricing Society. As a practicing lawyer, Mr. Rueff's experience included both litigation and transactional matters, but in later years, his practice was primarily focused on the distribution of federal grants to victims of hurricane and flood disasters. Mr. Rueff has staffed and managed project teams in Mississippi, New York, Texas, New Jersey and Louisiana. His experience includes the management and oversight of a program legal department for two years that included a team of 11 attorneys and 20 paralegals.
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This 90-minute webinar is eligible in most states for 1.5 CLE credits.
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Live Online
On Demand
Date + Time
- event
Tuesday, June 4, 2024
- schedule
1:00 p.m. ET./10:00 a.m. PT
Outline
- Overview of artificial intelligence in law
- Case studies of AI application in litigation
- Potential pitfalls
Benefits
The panel will discuss these and other key issues:
- What is the "lost in the middle" issue?
- Where do AI vendors tend to "oversell" the benefits of AI applications?
- Does AI yield cost savings? What kind of cases justify the costs and risks of AI ?
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