How Artificial Intelligence and Machine Learning Are Impacting the Litigation Landscape
Mike DeCesaris has twenty years of consulting and litigation experience. He leads the firm’s Data Science Center. Mr. DeCesaris specializes in directing complex, data-intensive projects requiring data management, production, and integration, along with advanced empirical analysis.
Ernest Kim Song
Ernest Kim Song has over ten years of experience consulting on all phases of commercial litigation, internal investigations, and regulatory matters.
On-Demand:August 28, 2023
$95.001 hour CLE
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Receive access to recorded class and earn self-study credit. Recording is made available 5 business days after live broadcast.
Recent advancements in artificial intelligence (AI) and machine learning (ML) capabilities, including developments in generative AI, are leading to both new business opportunities for law firms as well as changes in litigation practice and expert testimony. In the world of litigation, the power of AI and ML have been long understood by law firms and economic and financial consulting firms. Law firms have been using technology assisted review and predictive coding technology to make e-discovery more efficient for many years. The support of expert witnesses has always required leading-edge analytical tools and data science techniques, and AI and ML are increasingly important tools in experts’ arsenals. As older, rulesbased AI has evolved into ML where computers are programmed to accurately predict outcomes by learning from patterns found in massive data sets, the legal industry has found that AI can do far more than many imagined. Then, in the last six months, Large Language Models (LLMs) such as GPT-4 have captured the attention of the legal industry and promise to augment existing AI and ML capabilities and provide broad access to a non-technical audience through web interfaces such as ChatGPT.
This course is co-sponsored with myLawCLE.
Key topics to be discussed:
Potential new business opportunities created by clients’ use of AI and LLMs
Potential impact of LLMs and generative AI on litigation practice
The use of AI and ML in expert testimony by both plaintiff and defense side experts over the last several years
Date / Time: September 8, 2023
Mike DeCesaris | Cornerstone Research
Mike DeCesaris has twenty years of consulting and litigation experience. He leads the firm’s Data Science Center. Mr. DeCesaris specializes in directing complex, data-intensive projects requiring data management, production, and integration, along with advanced empirical analysis. He has worked with both outside and inside counsel to efficiently manage the discovery of large, proprietary databases in complex litigation, investigations, and merger reviews.
His extensive experience spans matters related to healthcare, the False Claims Act, antitrust and competition, consumer fraud and product misrepresentation, internal investigations, intellectual property, and breaches of contracts. Mr. DeCesaris is experienced leading teams assisting clients seeking to conduct complex internal investigations, address class certification, establish the impact of conduct, estimate damages, assess exposure, or calculate settlement payments. The matters he has worked have involved a range of industries, including healthcare and life sciences, financial services, energy and commodities, telecommunications and media, high technology, consumer products, tobacco, and airlines.
Mr. DeCesaris is the author of the article “Using Big Data in Gathering Expert Testimony,” published in Law360. He also coauthored “Mobile Advertising: An Economic Perspective,” published in Distribution, the newsletter of the American Bar Association’s Antitrust Section; and “Energy Trading Strategies in California: Market Manipulation?” published in Obtaining the Best from Regulation and Competition. Metropolitan Corporate Counsel published an interview with Mr. DeCesaris on “Digging for Deep Expertise: Big data and discovery deadlines drive surging need for well-prepared experts.”
Ernest Kim Song | Cornerstone Research
Ernest Kim Song has over ten years of experience consulting on all phases of commercial litigation, internal investigations, and regulatory matters. As director of Cornerstone Research’s Data Science Center, Mr. Song leads interdisciplinary data scientists in applying artificial intelligence (AI), social media analysis, geospatial analysis, big data analytics, and machine learning (ML) techniques to support expert testimony.
Mr. Song consults on issues at the intersection of law, technology, big data, and data science. These include analyzing algorithmic trading platform code, interfacing with cryptocurrency network infrastructure, and assessing how information is disseminated through social media and chatting platforms. His expertise includes advanced analytics and evaluating complex data workflow issues in matters involving cryptocurrency, antitrust, merger review, securities class actions, and mortgage-backed securities. He has also provided data science support in matters alleging defamation and market manipulation.
I. Potential new business opportunities created by clients’ use of AI and LLMs | 2:00pm – 2:20pm
Privacy and data security | 2:00pm – 2:02pm
Intellectual property | 2:02pm – 2:04pm
Product liability | 2:04pm – 2:08pm
Labor and employment | 2:08pm – 2:12pm
Antitrust and competition | 2:12pm – 2:16pm
Healthcare and life sciences | 2:16pm – 2:20pm
II. Potential impact of LLMs and generative AI on litigation practice | 2:20pm – 2:40pm
Assess the current state of the technology | 2:20pm – 2:30pm
What use cases law firms are exploring and where things may be in a few years | 2:30pm – 2:40pm
III. The use of AI and ML in expert testimony by both plaintiff and defense side experts over the last several years | 2:40pm – 3:00pm
One area where these techniques can be particularly helpful is in analyzing large volumes of user-generated content such as social media posts and consumer reviews | 2:40pm – 2:50pm
Explore use cases for AI and ML in expert testimony, including | 2:50pm – 3:00pm
Domain-specific sentiment analysis
Assessing marketing influence on social media
Image object detection
Public press topic modeling
Assessing the nature of allegedly defamatory statements, product liability, and class certification