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LIMITS OF ALGORITHMIC FAIR USE
Get PDFIn 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
PRIVACY’S NEXT ACT
Get PDFThis Article identifies and describes three data privacy policy developments from recent legislative sessions that may seem unrelated, but which I contend together offer clues about privacy law’s future over the short-to-medium term.
The first is the proliferation, worldwide and in U.S. states, of legislative proposals and statutes referred to as “age-appropriate design codes.” Originating in the United Kingdom, age-appropriate design codes typically apply to online services “directed to children” and subject such services to transparency, default settings, and other requirements. Chief among them is an implied obligation to conduct ongoing assessments of whether a service could be deemed “directed to children” such that it triggers application of the codes.
The second development is a well-documented push for responsible artificial intelligence (“AI”) practices in the form of new transparency and accountability frameworks. The most comprehensive such framework is the European Union’s AI Act, although similar reforms in Canada, as well as nascent reforms here in the United States, address analogous topics. Among these are requirements for AI developers to assess, document, and, in some instances, report to regulators the existence of potential harms and plans to mitigate them prior to launching a new AI-driven product or service.
The third development, certain reforms to competition policies, is least likely to be traditionally counted among “privacy” laws. However, I argue that two recent reforms in Europe—the Digital Services Act and the Digital Markets Act—implicate data privacy concerns and should be viewed as imposing privacy-related compliance obligations. For instance, these frameworks address the use of personal data, including sensitive personal information, for online advertising purposes.
My argument is that common threads across these developments underscore the dynamism of privacy law at a critical moment in its development and highlight the increased public awareness of the benefits––and risks––of a data-driven economy and society. To that end, I identify three specific trends among these developments that I anticipate recurring in data privacy policy proposals over privacy’s “next act.” First, legislators and regulators alike appear increasingly focused on age verification technologies as a mechanism for distinguishing between internet users and determining to whom they must provide certain protections. Second, there is a growing appetite for shifting assessment obligations onto regulated entities, albeit with guidance, and requiring that the results of such assessments are affirmatively disclosed to regulators. Third, privacy obligations are no longer limited to data privacy laws. They are increasingly found in other types of policy proposals––and detecting them will require a broader view of what constitutes a “privacy” law than typical among privacy professionals
Client Confidentiality as Data Security
Get PDFThe duty of confidentiality has been a cornerstone of the attorney-client relationship for more than four centuries. Historically, this duty was not difficult to discharge. All a lawyer had to do to comply was not affirmatively share client information in public without consent. But that has all changed. The same technologies that provide unprecedented benefits of authorized access by lawyers and their clients create unprecedented risks of unauthorized access by others. As a result, although the duty of confidentiality was once synonymous with a duty to keep client confidences secret, today the duty necessitates that lawyers keep client confidences secure as well.
This critical shift did not go entirely unnoticed by the legal profession. In 2012, the American Bar Association adopted Model Rule of Professional Conduct 1.6(c) which requires lawyers to “make reasonable efforts to prevent the inadvertent or unauthorized disclosure of, or unauthorized access to,” client confidences. This new rule had good intentions and was eventually adopted in some form by every state bar. Yet it has proven ineffective at protecting clients and difficult, if not impossible, to execute for lawyers. Worse, in the more than a decade since its adoption there has not been a single published disciplinary action for violating this duty in the digital context. Not one.
After telling the story of the legal profession’s adoption of a duty of data security and the shortcomings with the current approach to that duty, this Article seeks to outline its next chapter. Specifically, it argues that the lawyer’s duty of data security should not focus exclusively on the regulation of technological safeguards to prevent breaches and should focus instead on regulating the processes that lawyers must take to mitigate harm from potential breaches and the people that lawyers must consult when making data security decisions. This approach draws inspiration not only from professional responsibility scholarship but also from data security best practices from outside the legal profession that can help guide lawyers, protect clients, and incentivize enforcement by state bars despite constant technological innovation
Reimagining Law and Policy Affecting Individuals with Substance Use Disorders Disability Discrimination by Clinical Algorithm
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How Detrimental is Transunion v. Ramirez, Really? Understanding the Impact on Environmental Law
Get PDFIn 2021, the United States Supreme Court issued a controversial opinion with the potential to constrict the standing doctrine. TransUnion v. Ramirez appeared to alter standing’s “concrete harm” requirement, which would significantly restrict plaintiffs’ ability to invoke the jurisdiction of federal district courts. Building off its 2016 case, Spokeo v. Robins, the Court declared that intangible harms are only concrete when “plaintiffs have identified a close historical or common-law analog[] for their asserted injury.” The “common-law analog[]” required a “close relationship to harms traditionally recognized as providing a basis for lawsuits in American courts.” The Court mandated this requirement even for statutory harms, despite Congress’s long-held power to elevate harms to the level of concrete. The Court applied this rationale to a statutory right to information, implying that such informational harms are not concrete on their own, absent adverse effects. Subsequently, the circuits split on the status of informational harms, and prominent scholars warned of the holding’s detrimental effects on important areas of law. One area of impact is environmental law, where cases can involve harm to statutory rights created by modern pollution control and natural resources statutes.
This Comment addresses the implications of TransUnion, detailing how a broad reading of the case would drastically limit standing in environmental lawsuits. It argues that the broad interpretation of TransUnion would fundamentally conflict with historical precedent and the separation of powers, and offers a more natural approach to understanding TransUnion that would not seriously affect environmental law. This Comment concludes that a narrow reading of TransUnion better reconciles the case’s essential holding with prior precedent and Congress’s powers
Let Sleeping Dogs Lie: A Comparative Analysis of the Dormant Commerce Clause and Internal Trade Barrier Mitigation
Get PDFThe Dormant Commerce Clause jurisprudence of the United States has been one of the most widely criticized doctrines of American constitutional law. However, most of these criticisms fail to consider the economic implications of the Dormant Commerce Clause, namely the benefits this doctrine has provided in facilitating internal free trade amongst the states. This Comment argues that the Dormant Commerce Clause has given American courts an effective tool to promote interstate free trade by removing state regulations that create non-tariff barriers to trade. To support this assertion, this Comment utilizes a comparative constitutional analysis to examine how the constitutional systems of the United States and Canada promote internal free trade. Under their constitutional system, Canadian courts have been unable to remove interprovincial barriers to trade, leading to billions in lost revenue and increased costs for businesses and consumers. In contrast, courts in the United States have been able to remove barriers to trade using the Dormant Commerce Clause, decreasing costs to consumers and businesses. Therefore, the Dormant Commerce Clause has allowed courts to establish more robust internal free trade. Thus, the Supreme Court of the United States must be wary of displacing the Dormant Commerce Clause from American constitutional jurisprudence because of its positive impact on interstate free trade