IEEE P3462

New IEEE Standard - Active - Draft.
This recommended practice follows a Safety by Design approach by providing recommendations for developing, deploying, and maintaining generative artificial intelligence models with adequate safeguards against child sexual abuse and exploitation. The document provides a framework that expands child safety measures related to sexual abuse and exploitation to the entire lifecycle of generative artificial intelligence: development, deployment, and maintenance stages. Each part in the process includes opportunities to prioritize child safety, regardless of modality (including, but not limited to text, image, video, audio) or whether an organization releases its technology as closed source or open source, or some release option between these two.

This recommended practice follows a safety by design approach by providing recommendations for developing, deploying, and maintaining generative artificial intelligence models with adequate safeguards against child sexual abuse. The document provides a framework that expands child safety to the entire lifecycle of machine learning (ML): development, deployment, and maintenance stages. Each part in the process includes opportunities to prioritize child safety, regardless of data modality (i.e. text, image, video, audio) or whether an organization releases its technology as closed source or open source, or some release option between these two.
This standard provides tools to expand child safety to the entire lifecycle of machine learning (ML) from the earliest stages: development, deployment, and maintenance. Each part in the process includes opportunities to prioritize child safety, regardless of data modality (i.e. text, image, video, audio) or if an organization releases its technology as closed source or open source, or some release option between these two. When considering the ecosystem of ML/AI technology players, there exist multiple points of opportunity to prioritize child safety via Safety by Design.