Artificial Intelligence Integrity & Public Access: A Federal Legislative Framework

This publication presents a proposed federal legislative framework for artificial intelligence governance developed and proposed by Terrance J. Chisolm. The framework seeks to protect humanity from dangerous uses of artificial intelligence while preserving meaningful lawful public access to transformative AI technologies. The proposal establishes four central principles: protect people, preserve public access, require evidence-based attribution when artificial intelligence is alleged to have caused serious harm, and maintain accountability for the humans and institutions that direct, deploy, compromise, or misuse AI systems. The framework proposes standards for AI incident attribution; privacy-protective forensic accountability for designated high-risk AI deployments; independent technical investigation of catastrophic AI incidents; safeguards against deliberate fabrication of AI-attribution evidence; and consideration of competition and public access when federal regulation materially restricts artificial intelligence technologies. The proposal does not grant artificial intelligence legal personhood or constitutional rights. Rather, it seeks to protect the integrity of evidence concerning what AI systems actually did while preserving public access, civil liberties, innovation, competition, safety, and human accountability. This publication serves as the public policy paper accompanying the proposed Artificial Intelligence Integrity, Accountability, and Public Access Act of 2026. Original Author & Proposer: Terrance J. ChisolmVersion: 1.0First Publication: September 11, 2026DOI: 10.5281/zenodo.22710643