Despite everyone’s best efforts, things will sometimes go wrong with AI systems, and the parties involved may find themselves in litigation or arbitration. When this happens, the parties will need to peer into the “black box” to reconstruct what the computer did and why it did it. But the usual electronic discovery tools and evidentiary principles for electronic records might not work for an AI system—especially a machine learning system that continuously “learns” based on new data. During our call, we will discuss some of these challenges, including:

What are the unique difficulties that AI systems pose in the discovery context?

What are best practices to minimize discovery risks:

  • When designing a new AI system?
  • When implementing an existing AI system that is already highly complex and customized?
  • At the start of litigation?
  • In the midst of discovery?

How can lawyers assist at the pre-litigation stage (in AI design, documentation, implementation, etc.)—i.e., how can they help better position the company to defend itself when litigation occurs? 

Mayer Brown’s Global Financial Markets Initiative helps clients deal with the legal and business challenges resulting from the ongoing turbulence in worldwide financial markets. By mobilizing the firm’s global resources from multiple practices and offices, the initiative provides clients with knowledgeable and timely counsel on a broad spectrum of their legal needs.

Listen to Looking into the Black Box: Discovery, Evidence, Litigation Holds and Recordkeeping

For additional information, please contact GFM@mayerbrown.com.