hold on there's just too much going on in Congress lmao...
Requires AI developers to report registered copyrighted works used in training.
Senators Schiff (D, CA) and Curtis (R, UT).
Introduced in Senate, in committee, no vote yet.
This bill, called the Copyright Labeling and Ethical AI Reporting Act (CLEAR Act), requires companies that develop generative AI models to notify the U.S. Copyright Office about any registered copyrighted materials they use for training. It was introduced by Senators Adam Schiff (D-CA) and John Curtis (R-UT) and is currently under review by the Senate Judiciary Committee, which must approve it before it can be voted on by the full Senate.
Introduced Feb 10, 2026
The CLEAR Act was introduced in the Senate on February 10, 2026, and has been assigned to the Senate Committee on the Judiciary. For the bill to become law, it typically needs to pass out of this committee, be approved by the full Senate, then pass the House of Representatives, and finally be signed by the President.
You could potentially access a new public online database maintained by the Copyright Office that lists the registered copyrighted materials used to train various generative AI models. This would provide more insight into the content AI systems learn from. Companies developing AI would need to submit detailed notices for their training datasets and could face significant fines or lawsuits if they fail to do so, which may impact their operations.
Supporters Say
Supporters say this bill provides essential transparency and strengthens protections for creators' registered works in the AI era.
Critics Say
Critics might argue the bill creates an excessive burden for AI developers, potentially slowing innovation.
Those in favor emphasize that creators deserve to know when their registered works are used to train AI, allowing for better accountability and potential compensation. Concerns from opponents may center on the logistical challenges and costs for AI companies to meticulously track and report every registered copyrighted work within vast training datasets, suggesting it could hinder the rapid development of AI technologies.