Introduction
The rapid rise of artificial intelligence (AI), deepfakes, and automated decision-making systems has made it increasingly difficult for people to tell whether they are communicating with another human being, an AI, or a blend of both. This poses serious questions about trust, security, and social cohesion, especially in high-impact domains like healthcare, finance, and media.
IEEE 3152—officially titled Standard for Transparent Human and Machine Agency Identification — addresses this growing concern by defining clear markers and guidelines for identifying who (or what) we’re interacting with. It builds on the principle that individuals should never be misled into thinking they are speaking to, or receiving content from, an entity whose nature is concealed. By offering structured audio, visual, and metadata-based “marks,” this standard lays the foundation for greater transparency and fosters trustful AI-human collaboration.
Overview of the Standard
Purpose and Scope
At its core, IEEE 3152 provides a transparent framework that ensures people can identify whether they are communicating with:
A real human being (unfiltered or unaltered). An autonomous AI system. A hybrid entity operating under human oversight. A human whose actions are steered by AI. Media that has been significantly altered by AI (synthetic or deepfaked).
Instead of focusing on how an AI system makes decisions “behind the curtain,” the standard zeroes in on who or what is in control of content or communication. This clarity is critical for mitigating confusion and deception in online chats, phone calls, social media posts, videos, or any other platform.
Relevant Domains and Applications
IEEE 3152 can be implemented across a wide variety of use cases:
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