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Course Details

Course Information
  • smart_display Format

    Live Online with Live Q&A

  • signal_cellular_alt Difficulty Level

    Intermediate

  • work Practice Area

    Class Action and Other Litigation

Date & Time
  • event Date

    Wednesday, May 21, 2025

  • schedule Time

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

  • timer Program Length

    90 minutes

Credit Information

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

$297.00
CLE
ALL

This CLE webinar will discuss how class action lawsuits alleging data privacy violations arising from AI technology could be at odds with the Federal Trade Commission's ability to order algorithmic disgorgement, also known as algorithmic destruction or model destruction, when resolving enforcement actions asserting that a company's deployment of AI or quasi-AI tools amounted to deceptive or unfair business practices that harmed consumers. The panel will help both plaintiffs and defendants in privacy class actions to understand algorithmic disgorgement, help predict which cases the FTC is most likely to require it in, and suggest the implications of change in U.S. policy that emphasizes primacy in AI development as a matter of national security.

Faculty

Ben Winters
Director of AI and Data Privacy
Parent: Consumer Federation of America

Mr. Winters is the Director of AI and Privacy at the Consumer Federation of America. He leads CFA’s advocacy efforts related to data privacy and automated systems and works with subject matter experts throughout CFA to integrate concerns about privacy and AI in order to better advocate for consumers. Mr. Winters is also an adjunct professor at the University of the District of Columbia David A. Clarke School of Law.

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Description

Privacy class action lawsuits often allege that companies used algorithms, both true AI or quasi-AI technologies, to secretly collect and use consumers' private data in violation of state or federal law causing consumers harm. Although in recent class lawsuits the type of data being collected has often been geolocation data, in prior suits plaintiffs have complained about facial recognition or allegedly biased decision-making software. How these matters end is unpredictable for both sides because the law in this area is evolving, incomplete, and inconsistent.

Also, the FTC could step into the fray. The FTC is charged with protecting the public from deceptive or unfair business practices. Something is "unfair" if it "causes or is likely to cause substantial injury to consumers which is not reasonably avoidable by consumers themselves and not outweighed by countervailing benefits … ." If the FTC considers a company's use of AI or quasi-AI technology is at odds with its privacy obligations to consumers, and therefore "unfair," then the FTC might bring an enforcement action. Resolution of that action might require the defendant to disgorge any computer data, data models, or algorithms that were developed or trained with improperly obtained data. Such a result could be financially ruinous to defendants and financially disappointing to potential class plaintiffs.

Listen as the premier panel of experts discusses algorithmic disgorgement, its role in FTC enforcement, and the impact of the FTC remedy on how to assess and proceed with class litigation alleging privacy violation claims.

Outline

  • Understanding algorithmic disgorgement
  • FTC settlements including algorithmic disgorgement
  • Overlap between FTC enforcement and class action claims
  • Plaintiff strategies
  • Defense strategies
  • Impact of Executive Order Removing Barriers to American Leadership in Artificial Intelligence

Benefits

The panel will review these and other key issues: 

  • What types of cases or types of privacy violations are most likely to provoke the FTC to seek algorithmic disgorgement? 
  • How does one know what data has been "tainted" by originating from some type of unjust action and must, therefore, be disgorged?
  • Is increased use of this remedy likely to curtail privacy class actions?
  • What industries are most at risk?