BarbriSFCourseDetails
  • videocam On-Demand
  • signal_cellular_alt Intermediate
  • card_travel Class Action and Other Litigation
  • schedule 90 minutes

Changes in Classwide Conduct: Leveraging Machine Learning to Overcome Challenges With Sentiment Analysis

$297.00

This course is $0 with these passes:

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Description

To urge or oppose certification or prove damages, class action lawyers on both sides routinely need to pinpoint and quantify changes (or lack of) in attitude or conduct, i.e., "sentiment," towards a particular subject (person, product, company, geography, value, etc.) before and after some pivotal event. That event might be a marketing campaign, a label, an allegedly defamatory statement, an environmental accident, or market information.

Machine learning and other variations of AI have the potential to overcome many of the challenges presented by traditional sentiment analysis approaches because they offer highly efficient conceptual analysis with limited influence from human subjectivity or strict and incomplete rules. This same type of AI can also provide "sentiment analysis" results using real time facial or vocal expressions and with or without the subject's knowledge or consent, creating significant privacy and "intimate" knowledge concerns.

Listen as our expert panel offers litigation insights about the potential uses of sentiment analysis and explores some of the ethical minefields to be avoided.

Presented By

Mike DeCesaris
Vice President, Data Science Center
Cornerstone, Inc

Mr. DeCesaris leads the Data Science Center at Cornerstone Research and coheads the firm’s technology, digital economy, and artificial intelligence practice. He plays a leading role in Cornerstone Research’s initiatives related to artificial intelligence (AI), generative AI (GenAI), and Large Language Models (LLMs). Mr. DeCesaris has more than twenty-five years of experience consulting on all phases of commercial litigation, internal investigations, and regulatory matters. He focuses on complex, data-intensive empirical projects at the intersection of economics, law, technology, and data science. Mr. DeCesaris works with counsel to efficiently manage the production and analysis of large, proprietary databases. His expertise includes issues related to healthcare, pharmaceuticals, the False Claims Act, antitrust and competition, merger reviews, intellectual property (IP), and allegations of consumer fraud, product misrepresentation, and breach of contracts. Mr. DeCesaris works on matters in numerous industries, notably airlines, consumer products, energy and commodities, financial services, healthcare, life sciences, technology, and telecommunications and media.

Ernest Kim Song
Director, Data Science Center
Cornerstone, Inc

Mr. 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. Kim 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. He consults on issues at the intersection of law, technology, big data, and data science. In these contexts, he has analyzed algorithmic trading platform code, interfaced with cryptocurrency network infrastructures, and conducted text analyses of information disseminated through social media and other communications platforms. Mr. Kim Song plays a leading role in Cornerstone Research’s initiatives to incorporate AI and Large Language Model (LLM) capabilities into litigation support.

Kiriaki Tourikis
Attorney
Reed Smith

Ms. Tourikis is an associate in the Tech & Data group, focusing her practice on enterprise data risk management and information governance, data privacy, records and e-discovery. She advises large financial services institutions on enterprise data risk management strategies to manage legal and regulatory issues associated with both traditional and innovative techonologies and data sources.Ms. Tourikis' practice involves advising clients on lowering risk associated with using and storing information across jurisdictions. In her role, she counsels large financial institutions, on all aspects of data risk management and the discovery and management of electronic data, including: data migrations; technology implementation and management; the policies and procedures regarding the governance of information in various technologies; eDiscovery strategy and management; the use of data analytics for eDiscovery, compliance and risk management; the development of data source catalogues, disclosures, and responses relating to electronically stored information; and the remediation of legacy data (both paper and electronic).

Credit Information
  • This 90-minute webinar is eligible in most states for 1.5 CLE credits.


  • Live Online


    On Demand

Date + Time

  • event

    Monday, May 13, 2024

  • schedule

    1:00 p.m. ET./10:00 a.m. PT

  1. Overview of sentiment analysis and its uses in litigation
  2. Sentiment analysis approaches
  3. Applying machine learning or other AI to conduct sentiment analysis
  4. Evidentiary issues

The panel will discuss these and other key issues:

  • What are the various approaches or forms of sentiment analysis?
  • How can machine learning overcome some of the challenges to various forms of sentiment analysis?
  • What are the evidentiary challenges to sentiment analysis derived from machine learning?