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Restoring Trust in Science: Storytelling, AI, and Integrity in Scholarly Publishing
March 26, 2026 at 2:00pm - 3:00pm ET

Registration coming soon

At a time when misinformation spreads faster than evidence, restoring trust in science has never mattered more. How can editors, authors, and researchers better connect with audiences who increasingly question scientific authority? And how can we tell the story of science in ways that invite engagement without sacrificing accuracy or rigor?

 

This webinar brings together leading voices to examine how trust can be rebuilt across scientific communication and the publication ecosystem. Holden Thorp (Editor-in-Chief, Science), Megan Ranney (Yale School of Public Health), and Ivan Oransky (Retraction Watch) will explore three critical challenges:

* Storytelling and public engagement: How can scientists and communicators build credibility with audiences who have learned to distrust expertise?

* AI in peer review: Where does AI genuinely help, and where does it threaten the integrity mechanisms we depend on?

* Malfeasance and integrity: What do paper mills, predatory publishers, and research fraud tell us about systemic vulnerabilities, and what can be done?

 

The format balances brief scene-setting presentations with open dialogue and audience Q&A. Participants will leave with practical strategies for strengthening trust in their own editorial, publishing, or medical communications work.

 

Learning Objectives:

By the end of this session, participants will be able to:

* Describe how storytelling principles can rebuild public confidence in scientific findings

* Evaluate the benefits and risks of AI tools in peer review and editorial workflows

* Identify forms of publishing malfeasance and their impact on trust in science

* Apply principles of trust and engagement to their own roles in editing, publishing, or research communication

This webinar is presented jointly by the Council of Science Editors (CSE) and the International Society for Medical Publication Professionals (ISMPP).