AI, Bias, and Employment Law: Mitigating Discrimination Risks in the Workplace

As Artificial Intelligence (AI) becomes increasingly embedded in workplace systems- including hiring tools, performance evaluations, and decision-making platforms- attorneys must be prepared to understand and address the legal, ethical, and professional implications of these technologies.

This program is designed to help attorneys understand how AI bias may impact workplace equity, employee rights, and employer liability. Participants will explore landmark cases, potential mitigation strategies, and the relevant obligations under the ABA Model Rules of Professional Conduct, including Model Rules 1.1 (Competence), 1.6 (Confidentiality), 5.1 & 5.3 (Responsibilities regarding nonlawyer assistance), 8.4(g) (Discrimination), and 2.1 (Advisor).

This course is designed for attorneys seeking to gain awareness of how implicit and systemic bias can be embedded in AI algorithms used in employment contexts, understand ethical obligations related to AI, and prepare for AI’s increasing functions and role in the legal profession.

Learning Objectives:

1. Evaluate the historical, current, and emerging uses of AI in the workplace and how they relate to Model Rule 1.1 (Competence).
2. Identify sources and forms of bias in AI systems and the ethical duty to address such bias under Model Rule 8.4(g) (Discrimination).
3. Analyze real legal cases involving AI-related discrimination or workplace harms, and evaluate legal theories for accountability.
4. Develop a mitigation guide for identifying and minimizing AI bias, integrating duties under Model Rules 1.6 (Confidentiality), 2.1 (Advisor), and 5.3 (Nonlawyer Assistance).

Course Time Schedule:

Eastern Time: 7:00 PM - 8:00 PM
Central Time: 6:00 PM - 7:00 PM
Mountain Time: 5:00 PM - 6:00 PM
Pacific Time: 4:00 PM - 5:00 PM
Alaska Time: 3:00 PM - 4:00 PM
Hawaii-Aleutian Time: 2:00 PM - 3:00 PM

This course is also being presented on the following dates:

Tuesday, February 10, 2026
Tuesday, March 10, 2026
Tuesday, April 7, 2026
Tuesday, May 5, 2026
Tuesday, June 2, 2026