By The United States Government Accountability Office

Agencies at the federal, state, and local levels have facilities capable of analyzing emerging street drugs—psychoactive substances newly circulating in the drug market. For example, the Drug Enforcement Administration and U.S. Customs and Border Protection have forensic laboratories that can analyze seized drugs and identify emerging substances. Current laboratory-based technologies can detect and identify emerging street drugs when appropriate methods (protocols) and reference standards are available. Portable technologies can detect drugs at the point of seizure but face accuracy challenges due, in part, to user error. Technology manufacturers told GAO they are developing more lay-friendly user interfaces and operational methods.

From fiscal year 2019 through 2024, the Departments of Justice and Health and Human Services awarded a combined total of about $12.5 million in grants for the development of new methods and technologies for analyzing emerging street drugs. New methods and technologies may make laboratory processes more consistent, among other enhancements. Method development can be done on faster timelines than technology development.

While new methods and technologies could enhance some capabilities, forensic scientists face key challenges with analyzing emerging street drugs, including:

• Lack of resources. Laboratories GAO spoke to consistently referenced insufficient staffing and time.

• Unstandardized reporting. According to stakeholders, varying reporting requirements at thestate and local levels can lead to gaps in data.

• Limited information sharing. Law enforcement may not always share up-to-date information about emerging drugs with medical examiners and hospitals.

If these challenges could be addressed, laboratories could be in a better position to meet the nation’s needs for emerging drug analysis. However, GAO is not making recommendations to address these challenges because they are primarily faced by state and local laboratories.

By Katrina Geddes

The “substantial similarity” between a copyrighted work and an unauthorized derivative has formed the bedrock of copyright infringement jurisprudence since the mid-nineteenth century. Recent technological developments, however, are destabilizing these conceptual foundations. In May, the Copyright Office suggested that the use of copyrighted works to train AI models may constitute infringement even if model outputs are not “substantially similar” to model inputs if they nevertheless “dilute the market” for similar works. One month later, Judge Chhabria of the Northern District of California argued that AI outputs do not have to be “substantially similar” to copyrighted training data in order to be infringing. The plaintiff’s incentives are sufficiently harmed, Judge Chhabria argued, when the market is flooded with “similar enough” AI-generated works.

These developments should be read as early warning signs of a disturbing doctrinal shift from “substantial similarity” to a new and dubious threshold for actionable infringement: “substitutive similarity”, where the substitutability of the defendant’s work, rather than the similarity of protected expression, provides the cause of action. This novel theory of harm, if widely adopted, would impose dangerous restrictions on downstream creativity. Any new work that was “similar enough” to existing works would be treated as potentially infringing, despite the absence of substantially similar expression. This would corrupt what is essentially a question of fact – whether the defendant copied “enough” of the plaintiff’s work to constitute unlawful appropriation – with deontic considerations of the wrongfulness of free-riding.

At the same time, artists are understandably rattled by the speed and scale of AI generation. AI models can produce “new” works in the style of established artists in a matter of seconds, dramatically undercutting the market for their work. AI style mimicry makes it difficult for artists to control their personal brands and for consumers to locate authentic works by their favorite artists. Copyright is responsible for protecting artists’ creative incentives, but its legal tests were not designed to handle the scale of imitation enabled by AI.

This Article offers a way out of this jurisprudential morass. Instead of lowering the burden of proof for infringement, Congress should strengthen the attribution rights of existing creators. Low-protectionists have long advocated for attribution rights as a way of protecting authors’ interests without expanding the scope of their economic entitlements. Proper attribution allows creators to capture the full reputational benefits of their labor without stifling downstream creativity. For example, Congress could enact an AI-specific attribution right that requires the disclosure of copyrighted training data in output metadata. This would mitigate the labor-displacing effects of generative AI by directing consumers to the original creators of a popular style or aesthetic.

Generative AI places copyright jurisprudence at a critical crossroads. Indulging Judge Chhabria’s novel theory of harm would effectively inaugurate a new standard for infringement – “substitutive similarity” – that would stifle not just AI innovation but human creativity more broadly. The stakes for protecting free expression through careful guardianship of longstanding doctrine could not be higher. This Article guides readers through this critical inflection point with new terminology for the jurisprudential lexicon as well as practical proposals for reform.