Learning Collaborative on Artificial Intelligence (AI) for Unions and Union Leaders

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Learning Collaborative on Artificial Intelligence (AI) for Unions and Union Leaders

The Learning Collaborative is an effort to help inform healthcare unions and union leaders AI and its impact on healthcare workers and workplaces.

The Collaborative provides several ways for healthcare union leaders and activists to learn more about AI.  They include a webinar series, a resource webpage, a collection of contract language from healthcare contracts, and a conference planned for Spring 2026.

Upcoming Webinars on AI for Healthcare Unions and Union Leaders

Fall 2025,
First Webinar, October 23, 4:00 p.m.

Open to all Union Leaders and Activists

Information and Resources on AI for Healthcare Unions and Union Leaders

  • News Articles
  • Reports
  • Videos

Model Collective Bargaining Language on AI in Healthcare Contracts

Conference on AI and its impact on Unions and Workers

Spring 2026

Open to all Union Leaders and Activists

Penn State’s LABOR School has organized a Learning Collaborative on AI for Healthcare Unions and Union Leaders.  The Learning Collaborative is a network of healthcare union leaders and union activists who wish to learn more about artificial intelligence (AI) in healthcare and experts and facilitators who wish to share their knowledge and expertise on this critical issue.

The Learning Collaborative’s goal is to provide accurate, useful, and practical information to healthcare union leaders and activists on critical questions such as:

  • What is AI?
  • How can AI impact healthcare workplaces and workers?
  • How can healthcare unions prepare themselves to ensure that the introduction of AI in their workplaces will improve patient care and the experience of workers and just not be used to cut jobs?
  • How can healthcare union leaders and members have a voice in how AI is introduced in their workplace? 
  • What strategies (including contract language) have healthcare unions and union leaders developed to have a positive impact on the introduction of AI in their workplaces?

The Learning Collaborative will initially focus on AI in healthcare workplaces.  But the resources on AI for workers and unions on our website will include information of interest to unions in any industry.

The Learning Collaborative will offer three major opportunities for union leaders and activists to learn about artificial intelligence:

  1. The Learning Collaborative is in the process of establishing a website that offers articles, videos, reports, websites, and other sources of information on AI and its impact on work, workers, and workplaces.  The website is public and union leaders and activists can access these resources whenever they want.  The website will be updated with new information each week.
  2. The Learning Collaborative will offer three webinars on AI in healthcare and its impact on workers in the Fall and Winter of 2025-2026. The first webinar will be held on October 23 at 4:00 pm EST.  Each webinar is designed to focus on a practical and specific issues related to AI that are relevant to union leaders and activists in healthcare.  The webinars will include presentations by technical experts on AI and workers, as well as by union leaders and other who have successfully dealt with the introduction of AI in their workplaces.  And each webinar will include breakout sessions where union people can speak with one another and exchange ideas and questions about how to effectively deal with AI. There is no charge to attend the webinars.  To register for the October 23 webinar, click this link—https://forms.office.com/r/greFpjULE5
  3. The Learning Collaborative will offer a Conference on AI for Unions and Union Workers in early 2026.  Information will be available later in the Fall.

For more information on the Learning Collaborative on AI for Healthcare Unions and Union Leaders, please contact:

Paul Clark
Professor, Labor and Employment Relations and Criminology
(814) 865-0752
Peter Lazes
Peter Lazes
Affiliate Faculty Member