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How Generative AI Turns Copyright Law Upside Down
No full textWhile courts are litigating many copyright issues involving generative AI, from who owns AI-generated works to the fair use of training to infringement by AI outputs, the most fundamental changes generative AI will bring to copyright law don’t fit in any of those categories. The new model of creativity, generative AI puts considerable strain on copyright’s two most fundamental legal doctrines: the idea-expression dichotomy and the substantial similarity test for infringement. Increasingly, creativity will be lodged in asking the right questions, not in creating the answers. Asking questions may sometimes be creative, but the AI does the bulk of the work that copyright traditionally exists to reward, and that work will not be protected. That inverts what copyright law now prizes. And because asking the questions will be the basis for copyrightability, similarity of expression in the answers will no longer be of much use in proving the copying of the questions. That means we may need to throw out our test for infringement, or at least apply it in fundamentally different ways
Escaping the Parens Trap: Andrew J. Bentivoglio
Get PDFState enforcement of federal antitrust law is a rich combination of questions about federalism, civil procedure, and remedies. Bedrock principles support a robust role for States as “parens patriae,” a relationship that positions them as protectors of consumers. Courts are puzzling through how to effectuate this role amidst centuries of common law history and evolving modern understandings of economic harms. This Note argues that allowing states to pursue nominal damages in parens patriae cases would better protect consumers, force clarification of the ill-defined limits to parens patriae actions, and allow for more efficient restitution in certain cases. This Note first describes the relationship between States and the federal government in antitrust enforcement and the implications that arise from the relationship in our federal structure. The Note then discusses the background of nominal damage awards generally and in the specific antitrust context. Finally, the Note argues for an application of nominal damages in parens patriae cases and discusses the implications for antitrust enforcement generally
Providing Health Care but Receiving None in Return: An Expanded Right to Health Analysis of the Live-In Caregiver Program of Canada (1992-2014)
Get PDFCanada’s robust universal, single-payer healthcare system has long made socio-economic rights (SER) advocates in the United States envious of our neighbors to the north. Fundamental human rights tenants echo in the Canadian Charter of Rights and Freedoms, including non-discrimination and individual freedom, yet the “right to health” has not been constitutionalized at the moment this essay was written.1 While Canada has a strong track record of signing international human rights conventions and guaranteeing the right to health for permanent citizens, there remains a gap in the health rights of migrant workers, even in regular situations or formalized citizenship pathways. Ever-changing legal qualifications for permanent residency and arbitrary three-month waiting periods for healthcare coverage to come into effect make securing the right to health for even the most highly desirable workers—namely, healthcare workers in private households—a thorny task. The motto for Canada’s paradoxical commitment to the socio-economic rights of migrant workers might be, as Toronto doctors Ritika Goel and Michaela Beder aptly articulate in the title of their medical journal op-ed, “Welcome to Canada...but don’t get sick.
Do Cases Generate Bad AI Law?
Get PDFThere’s an AI governance problem, but it’s not (just) the one you think. The problem is that our judicial system is already regulating the deployment of AI systems—yet we are not coding what is happening in the courts as privately driven AI regulation. That’s a mistake. AI lawsuits here and now are determining who gets to seek redress for AI injuries; when and where emerging claims are resolved; what is understood as a cognizable AI harm (and what is not), and why that is so.
This Essay exposes how our judicial system is regulating AI today and critically assesses the governance stakes. When we do not adequately recognize how the generative AI cases being decided by today’s judges are already operating as a type of AI regulation, we fail to consider which emerging tendencies of adjudication about AI are likely to make good or bad AI law. For instance, litigation may do good agenda-setting and deliberative work as well as surface important information about the operation of private AI systems. But adjudication of AI issues can be bad too, given the risk of overgeneralization from particularized facts; the potential for too much homogeneity in the location of lawsuits and the kinds of litigants; and the existence of fundamental tensions between social concerns and current legal precedents.
If we overlook these dynamics, we risk missing a vital lesson: AI governance requires better accounting for the interactive relationship between regulation of AI through the judicial system and more traditional public regulation of AI. Shifting our perspective creates space to consider new AI governance possibilities. For instance, litigation incentives (such as motivations for bringing a lawsuit or motivations to settle) or the types of remedies available may open up or close down further regulatory development. This shift in perspective also allows us to see how considerations that on their face have nothing to do with AI—such as access to justice measures and the role of judicial minimalism—in fact shape the path of AI regulation through the courts. Today’s AI lawsuits provide an early opportunity to expand AI governance toolkits and to understand AI adjudication and public regulation as complementary regulatory approaches. We should not throw away our shot
Beyond Algorithmic Disclosure for Generative AI
No full textOne of the most commonly recommended policy interventions with respect to algorithms in general and artificial intelligence (“AI”) systems in particular is the need for greater transparency, often focusing on the disclosure of the variables employed by the algorithm and the weights given to those variables. This Essay argues that any meaningful transparency regime must provide information on other critical dimensions as well. For example, any transparency regime must also include key information about the data on which the algorithm was trained, including its source, scope, quality, and inner correlations, subject to constraints imposed by copyright, privacy, and cybersecurity law. Disclosures about pre-release testing also play a critical role in understanding an AI system’s robustness and its susceptibility to specification gaming. Finally, the fact that AI, like all complex systems, tends to exhibit emergent phenomena, such as proxy discrimination, interactions among multiple agents, the impact of adverse environments, and the well-known tendency of generative AI to hallucinate, makes ongoing post-release evaluation a critical component of any system of AI transparency
Learning Middle Armenian at the Court of Meḥmed II: Language, Knowledge, and Power before the Imperial Rise of Ottoman Turkish
No full textShortly after the conquest of Constantinople, the Ottoman court of Meḥmed II (r. 1444–46, 1451–81) began to produce language-learning primers that would teach significant languages of statecraft and knowledge production from around the Mediterranean world. This article sheds light on the court’s pedagogical and ideological engagement with multilingualism through one primer in particular, which bears the shelf mark Ahmet III 2698 in the Topkapı Palace Museum Library. We name this primer Meḥmed II’s Hexaglot Grammar, as it was produced for his court and contains an array of languages within it: Persian, Ottoman Turkish, Ancient Greek, Byzantine Greek, Latin, and, finally, the vernacular tongue of Middle Armenian. The presence of many of these languages may seem more readily apparent, but what was Middle Armenian doing at the Ottoman court? As we show, Middle Armenian had a presence at court in more ways than one. Alongside the Hexaglot Grammar, the court also produced an extensive primer for learning the Armenian alphabet (MS Ayasofya 4767, Süleymaniye Library). So, too, did producers of knowledge in Middle Armenian find a home at court, such as Amirdovlat‘ Amasiac‘i, a physician whose extensive corpus of pharmacopeia in Middle Armenian likely made use of the palace library. By exploring the circulation of diverse manuscripts, translators, and intellectuals in Constantinople alongside primers such as the Hexaglot Grammar, this article offers a portrait of the Ottoman court never before seen: a place where the premodern Armenian vernacular not only survived, but, for a time, even thrived
