By: 

Dr. Aaron Poynton, Chairman, American Society for AI and Professor Hossein Rahnama, MIT Media Lab, Distinguished Member, American Society for AI 

The rapid integration of generative artificial intelligence into creative and professional workflows has outpaced the development of clear standards for disclosing AI involvement in authored work. Existing approaches to attribution are fragmented, inconsistent, and often unenforceable. To address this gap, this white paper introduces the Transparent Authorship Standard (TAS-5)—a five-level, media-agnostic disclosure system applicable to text, imagery, audio, video, software code, strategic planning, and academic references.

TAS-5 provides both narrative and abbreviated disclosure formats to support transparency for public audiences and operational precision for institutions. By offering a flexible and standardized method of attributing AI contribution, TAS-5 is intended to support ethical governance, credibility, and alignment across education, publishing, media, commerce, and government.