1-Hour Program

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Overview

Please note that this program may not offer Bias or Diversity & Inclusion credit in every jurisdiction. Check the credit information box to the right for the credit breakdown in your jurisdiction.

Artificial Intelligence and machine learning are becoming ubiquitous in business, the workplace and society. However, as businesses make decisions based on black box algorithms or data that is gathered or used without thorough analysis by legal and compliance teams, companies raise their risk of lawsuits, including employment lawsuits, and discrimination lawsuits and risk damage to their reputations. Moreover, use of AI that may create bias in the legal workplace can damage diversity, inclusion and perpetuate bias. Could decisions to provide financial products or services, or the making of employment decisions such as advancing associates in a law firm based on data such as gender correlation without fully understanding the bias underlying the data or the disparate impact give rise to liability? Could sampling demographic data that does not appropriately describe a population, or not properly aggregating data lead to faulty decisions? In this discussion, we will focus on common areas where data biasing may occur, including types of AI techniques that may be prone to algorithmic bias.

Katharine J. Liao and Huu Nguyen of Squire Patton Boggs LLP will be joined by Wendy Callaghan from American International Group, Inc. for a discussion that will also focus on the legal issues that may arise from the use of these systems, including:

  • Promoting diversity, inclusion and eliminating bias in the legal workplace using AI
  • Understanding how to use and not to use AI to make employment decisions, and understanding and accounting for biases in these tools.
  • Minimizing risks of discriminations and enabling reporting of harassment using AI tools that aim to be free of biases
  • FCRA claims based on AI usage, including providing transparency in the usage of AI background checks across diverse populations
  • Recent law in AI transparency, including discovering bias in the usage of AI in making legal decisions by agencies and businesses
  • ABA Model Rule 8.4(g)

 

Credit Details