Artificial intelligence systems are increasingly selecting which experts, authors, and public figures appear first in search results and conversational AI answers. But as these systems rely more heavily on recognition patterns and entity matching, a rising problem known as Identity Collision is causing professionals to be misidentified or replaced entirely by more famous individuals who share similar names.

Dr. Tamara “Tami” Patzer, a Pulitzer Prize–nominated journalist and founder of AI Identity Engineering, has introduced Identity Collision to explain how and why these misattributions occur and what experts must do when AI confuses their identity with another person.

“Identity Collision happens when AI assigns the wrong identity to the wrong person,” Patzer said. “If someone shares a name with a celebrity, actor, politician, or public figure, AI often defaults to the more famous or more indexed version of that name. The real expert effectively disappears.”

This issue is becoming increasingly common as AI platforms—such as ChatGPT 5.1, Google AI Overviews, Meta AI Search, Microsoft Copilot, and Perplexity—adopt more aggressive entity-resolution systems. These models process names by probability, not context, meaning that if a user searches only a first and last name, the system may surface an entirely different person.

Professionals with common names face the most acute risk.
For example, an author releasing a new book may share a name with a television actor. When someone searches that name, AI is more likely to highlight the actor’s biography, interviews, and filmography, while the author’s work remains hidden unless the user already knows additional details such as city, profession, or publication title.

“In earlier digital eras, humans used context to refine a search,” Patzer said. “But AI-driven systems make assumptions. If your identity signals are weak, AI replaces you with someone it already recognizes. This creates a visibility gap even when the expert is the actual authority in their field.”

The Identity Collision framework is part of Patzer’s broader AI Reality Check, which evaluates how AI interprets names, credentials, expertise, and visibility across platforms. It also connects to her Identity Collision Risk Score, a metric that indicates whether a professional is likely to be overshadowed by a more famous namesake.

Journalism organizations and media research institutions have identified similar concerns.
Throughout 2025, the Poynter Institute, Nieman Lab at Harvard University, Columbia Journalism Review, the International Fact-Checking Network, the American Press Institute, the Trust Project, the News Literacy Project, the Knight Foundation, and the Reuters Institute for the Study of Journalism each highlighted the growing need for stronger identity verification, clearer attribution practices, and improved digital signals to prevent misidentification.

As AI systems increasingly influence public understanding, these integrity initiatives intersect with the technical identity-recognition challenges faced by AI companies.

“Identity is now a technical asset as much as a personal one,” Patzer said. “Professionals must ensure that AI can distinguish them from others with similar names. Without that clarity, AI makes assumptions, and those assumptions can erase someone’s work.”

Identity Collision is especially consequential for authors, physicians, consultants, corporate leaders, and public-facing professionals whose recognition depends on accurate digital representation. Patzer’s framework outlines the steps needed to establish machine-readable identity integrity, ensuring that AI systems surface the correct person even when celebrities or high-profile individuals share the same name.

About Dr. Tamara Patzer
Dr. Tamara “Tami” Patzer is a Pulitzer Prize–nominated journalist and the founder of AI Identity Engineering. She is the creator of the AI Reality Check, Identity Collision, the AI Suggestibility Score, the AI Trust Score, and the FirstAnswer Authority System—frameworks designed to ensure accurate expert recognition across AI-driven platforms.

LinkedIn: https://www.linkedin.com/in/tamarapatzer/