Jiaxin Chen, LL.M. Class of 2026
On January 22, 2026, U.S. Representatives Madeleine Dean and Nathaniel Moran introduced the Transparency and Responsibility for Artificial Intelligence Networks Act (“TRAIN Act”). The bill would grant copyright-holders unprecedented rights to access AI training data, allowing them to verify whether their works were used without authorization. Though still early in the legislative process, it has already sparked debate in the tech and creative industries, highlighting potential reforms in AI transparency and accountability.
- Core of the Bill: Lowering the Threshold for Rights Protection
The central feature of the TRAIN Act is its proposal to add a new Section 514 to the U.S. Copyright Act (U.S. Code Title 17), establishing a novel, streamlined mechanism for copyright holders to obtain information about AI training data. Rather than proceeding through full litigation and discovery, a claimant may request a subpoena issued by the clerk of a federal district court—without prior judicial review—upon a certification of a “good-faith belief” that their copyrighted work has been used in AI training.
This approach draws on existing legal frameworks for combating online piracy in the US, such as the subpoena mechanism under the Digital Millennium Copyright Act (“DMCA”), but shifts the focus from identifying alleged infringers to compelling the disclosure of training data, fundamentally changing the current black box situation.
- Impact on Developers: Compliance Costs and Transparency Challenges
If the TRAIN Act is passed, compliance costs will increase significantly. Developers will be legally required to establish and maintain a comprehensive, complete, and traceable training data record system. This means that every step—from data collection, cleaning, and annotation to model training—must be clearly documented. This not only increases technical and managerial complexity, but also incurs additional manpower and material costs.
Additionally, business modes may face reshaping. The transparency requirement and potential infringement litigation risks brought by the Act may force developers to use explicitly authorized, licensed datasets or invest more in developing data masking or source tracking technologies. This will directly impact the cost structure and profit model of AI companies.
Finally, the black box will be forced open. The Act’s mandatory disclosure will promote industry transparency, making developers more cautious in selecting and using data, thereby reducing the risk of infringement from the beginning. At the same time, it raises trade secret concerns, as datasets and processing methods are often proprietary. Although the draft bill includes sanctions for bad-faith requests, questions remain whether these safeguards prevent strategic misuse.
- Impact on Copyright-holders: Bargaining Power and New Licensing Market
First, for content creators, the TRAIN Act offers a potentially powerful new enforcement tool. Its most immediate impact lies in providing a mechanism to obtain information about AI training data without first initiating full-scale litigation. Although a copyright holder must still form a “good-faith belief” that their work has been used—meaning some degree of informal investigation remains necessary—the Act allows them to request a subpoena to gather relevant evidence without filing a lawsuit and engaging in formal discovery.
Second, this will greatly improve the position of copyright-holders in commercial bargains. With concrete evidence that AI models use their works, copyright-holders or their representative organizations will have stronger bargaining power when negotiating licensing agreements with AI companies. This will help to form a fairer profit distribution mechanism. Collective Management Organizations such as Broadcast Music Inc. and Copyright Clearance Center support the bill.
Third, the Act may facilitate more structured licensing markets for AI training data. CMOs could offer standardized blanket licenses covering large repertoires for model training. Alternatively, publishers, stock image platforms, or music licensors could provide pre-cleared datasets with defined rights and pricing. Greater transparency would make it easier to verify use, supporting enforcement and pricing, and shifting the market from uncertain ex post disputes toward predictable, ex ante licensing arrangements.
- Bill Prospects: A Fierce Battle between Regulation and Innovation
The future of the TRAIN Act depends not only on the power struggle between the creative and technological industries, but also on deeper obstacles posed by the White House. This includes the conflict between the Trump administration’s AI policy and the legislative spirit of the Act.
Since taking office in 2025, the Trump administration has established an AI national strategy centered on deregulation for innovation. Executive orders such as “Removing Barriers to American Leadership in Artificial Intelligence” and “Ensuring a National Policy Framework for Artificial Intelligence” show clear support for the AI industry and aim to reduce regulatory barriers to maintain U.S. leadership. The December 11, 2025 executive order states: “To win, United States AI companies must be free to innovate without cumbersome regulation. But excessive State regulation thwarts this imperative.” It even authorizes the federal government to challenge or cut funding to prevent states from enacting burdensome AI laws, showing that legislation increasing compliance burdens or slowing the pace of innovation, would conflict with national AI policies.
Additionally, obstacles may be encountered in the legislative process. Even if the Act gains sufficient support in the House and Senate and a unified version is passed through bipartisan coordination, it will face the hurdle of presidential action. Given the Trump administration’s stance on deregulation, the Act faces the risk of being vetoed. Overriding it requires a two-thirds majority in both houses—a difficult task in a politically divided Congress on an issue as complex as AI, involving technological, economic, and ideological disputes.
Nevertheless, this does not render the bill insignificant. The TRAIN Act brings the issue of training data transparency to the forefront of national legislative debate, forcing both industry and policymakers to confront it directly. Moreover, it is not an isolated effort: parallel proposals, such as the Copyright Labeling and Ethical AI Reporting Act, suggest a broader legislative trend toward increasing oversight of AI training practices. Even if not enacted, the bill itself exerts regulatory pressure, potentially prompting AI companies to adopt more proactive compliance strategies—such as expanded data licensing or more conservative interpretations of fair use—thereby reshaping industry norms in advance of formal legal change.