LIMITS OF ALGORITHMIC FAIR USE

In this article, we apply historical copyright principles to the evolving state of text-to-image generation and explore the implications of emerging technological constructs for copyright’s fair use doctrine. Artificial intelligence (“AI”) is frequently trained on copyrighted works, which usually involves extensive copying without owners’ authorization. Such copying could constitute prima facie copyright infringement, but existing guidance suggests fair use should apply to most machine learning contexts. Mark Lemley and Bryan Casey argue that training machine learning (“ML”) models on copyrighted material should generally be permitted under fair use when the model’s outputs transcends the purpose of its inputs. Their arguments are compelling in the domain of AI, generally. However, contemporary AI’s capacity to generate new works of art (“generative AI”) presents a unique case because it explicitly attempts to emulate the expression copyright intends to protect. Jessica Gillotte concludes that generative AI does not illicit copyright infringement because judicial guidance requires adherence to the constitutional imperative to promote the creation of new works when technological change blurs copyright’s boundaries. Even if infringement does occur, Gillotte finds that fair use would serve as a valid defense because training an AI model transforms the original work and is unlikely to damage the original artist’s market for the copyrighted work. Our paper deviates from prior scholarship by exploring specific generative AI use cases in technological detail. Ultimately, we argue that fair use’s first factor, the purpose of the use, and its fourth factor, the impact on the market for the copyrighted work, both weigh against a finding of fair use in generative AI use cases. However, even if text-to-image models aren’t found to be transformative, we argue that the potential for market usurpation alone sufficiently negates fair use. There is presently little specific guidance from courts as to whether using copyrighted works to build generative AI models constitutes either infringement or fair use, although several related lawsuits are currently pending. Text-to-art generative AIs present several scenarios that threaten substantial harm to the market for the copyrighted original, which tends to undercut the case for fair use. For example, a generative AI trained on copyrighted works has already enabled users to create works “in the style of” individual artists, which has allegedly caused business and reputational losses for the emulated copyright holder. Furthermore, past analyses have ignored the potential for a model to be non-transformative when its intended output has the same purpose and is of the same nature as its copyrighted inputs. This article contributes to the discussion by shining a technical light on text-to-art AI use cases to explore whether some uses normatively fail to qualify as fair uses. First, we examine whether text-to-image models present a prima facie infringement claim. We then distinguish text-to-image generative AIs from non-image focused AIs. In doing so, we argue that when the nature of the copyrighted work and the purpose of the infringing use are the same, it is more likely that the original artist will experience market harm. This tilts the overall analysis against a finding of fair use

The Past and the Future of IP Law

This panel will discuss important changes in each of the areas of intellectual property law over the past twenty years—copyright, patent, and trademark law. Our panelists will then forecast the future of IP law in light of recent events and technologies such as the exponential growth of artificial intelligenc

In the Midst of Bankruptcy: How Cryptocurrency\u27s Classification Affects Creditors Who Were Once Customers

In 2022, Congress proposed the Digital Commodities Consumer Protection Act to amend the Commodity Exchange Act and define a new type of commodity: digital commodity. The definition of digital commodity encompasses cryptocurrency and provides the Commodity Futures Trading Commission with jurisdiction over digital asset transactions. This definition of digital commodity has two important implications. First, it signals the lawmakers’ tendency to generalize cryptocurrency as a commodity. Second, it brings complications into how creditors—especially individual crypto account holders—can recover in the recent bankruptcy cases involving prominent crypto companies. This Comment contains four components. First, it provides a brief explanation of cryptocurrency and its underlying mechanism. Second, it reviews the debate over cryptocurrency’s classification as a commodity versus as a security. Third, it presents an overview of the bankruptcy system and the effect of a bankruptcy discharge. Finally, this Comment argues that generalizing cryptocurrency as a commodity limits the ability of creditors—especially cryptocurrency account holders, who are often individual consumers—to seek recovery outside of bankruptcy. This Comment aims to bring the interests of consumer creditors to the attention of judicial and legislative bodies

Dual-Systems and Fuzzy-Trace Theory Predictions of COVID-19 Risk Taking in Young Adults

Risk-reduction behaviors are the first line of defense in viral epidemics. Choosing to not engage in risk-reduction behaviors produced millions of preventable deaths from COVID-19. Understanding why this happens and how to predict it is important for theory development and public policy. We took four approaches to this problem: experimentally varying theory-driven predictors (social rewards, transmission risk, and mandatory/voluntary regulations) in choice scenarios, further probing choices in specific scenarios predicted to elicit risk taking, conducting hierarchical regressions with demographic and theory-driven predictors for both scenario types, and conducting corresponding regressions for self-reported protective behaviors. The sample consisted of 247 young adults to test highly publicized predictions about how the virus would spread and who would take risks. Results showed that risky choices for scenarios correlated with self-reported behavior and varied with transmission risk and whether regulations were mandatory. Experimentally varying social reward did not elicit greater risk taking as expected by dual-systems theory but risk taking in specific social scenarios was predicted by individual differences in sensation seeking as predicted by dual-systems theory. Sensation seeking predicted social distancing and impulsivity predicted mask wearing. Fuzzy-trace theory’s predictors of categorical thinking about risk and endorsement of simple gist principles of social responsibility (to not hurt other people) consistently predicted choices and behaviors, accounting for significant variance beyond dual-systems predictors. Both controlled experiments and real-world self-reported behaviors converged on similar conclusions, identifying a major gap in influential theories (the omission of gist-based thinking) and challenging pessimistic predictions about motivations and mandates in public health

WHEN AI REMEMBERS TOO MUCH: REINVENTING THE RIGHT TO BE FORGOTTEN FOR THE GENERATIVE AGE

The emergence of generative artificial intelligence (AI) systems poses novel challenges for the right to be forgotten. While this right gained prominence following the 2014 Google Spain v. Gonzalez case, generative AI’s limitless memory and ability to reproduce identifiable data from fragments threaten traditional conceptions of forgetting. This Article traces the evolution of the right to be forgotten from its privacy law origins towards an independent entitlement grounded in self-determination for personal information. However, it contends the inherent limitations of using current anonymization, deletion, and geographical blocking mechanisms to prevent AI models from retaining personal data render forgetting infeasible. Moreover, the technical costs of forgetting—including tracking derivations and retraining models—could undermine enforceability. Therefore, this article advocates for a balanced legal approach that acknowledges the value of the right to forget while considering the constraints of implementing the right for generative AI. Although existing frameworks like the European Union’s GDPR provide a foundation, continuous regulatory evolution through oversight bodies and industry collaboration is imperative. This article underscores how the right to be forgotten must be reconceptualized to address the reality of generative AI systems. It provides an interdisciplinary analysis of this right’s limitations and proposes strategies to reconcile human dignity and autonomy with the emerging technological realities of AI. This Article’s original contribution lies in its nuanced approach to integrating legal and technical dimensions to develop adaptive frameworks for the right to be forgotten in the age of generative AI

Internet Drug Prohibition and the Opioid Overdose Crisis

The Ryan Haight Online Pharmacy Consumer Protection Act (Ryan Haight Act) prohibits controlled substance tele-prescribing when it occurs without a preliminary in-person medical evaluation. This Article details the Ryan Haight Act’s consequences for the practice of telemedicine in general and opioid addiction treatment in particular. In doing so, it builds on literature exploring the tension between the federal criminal regulation of controlled substance prescribing and the management of large-scale public health crises, particularly the opioid overdose crisis. By restricting the tele-prescription of certain controlled substances used for opioid addiction treatment, the Ryan Haight Act limits access to care for a highly vulnerable patient population that surpasses six million people nationwide. This issue has persisted despite telemedicine proving to be as effective as in-person health care for this form of treatment. Furthermore, U.S. telemedicine governance has evolved since the passage of the Ryan Haight Act, with several states adopting their own restrictions on controlled substance tele-prescribing. Using Medicare claims data and a dataset of state telemedicine policies, and leveraging the federal enforcement waiver of the in-person medical evaluation rule during the coronavirus (COVID-19) pandemic, this Article investigates state policymaking behavior and its health service implications. Forty-two states and the District of Columbia affirmatively liberalized controlled substance tele-prescribing during the COVID-19 pandemic, while eight states imposed their own in-person medical evaluation requirements. Patients in states with restrictions were twenty-two percent less likely to start opioid addiction treatment via telemedicine than patients in states without restrictions. Conceptually, these findings illuminate the contours and porosity of states’ autonomy in the regulation of medicine. Practically, these findings reveal that changes in federal controlled substance policy will be insufficient to maximize treatment access if they fail to account for state tele-prescribing restrictions. Against this backdrop, this Article offers a blueprint for controlled substance law that seeks to improve access to opioid addiction treatment, and that accounts for the variation in postures that the federal and state governments have adopted toward controlled substance teleprescribing. It proposes legislative, regulatory, and judicial remedies that share a common purpose: shielding clinicians from law enforcement actions when tele-prescribing opioid addiction medications