The Legal Imitation Game: Generative AI’s Incompatibility with Clinical Legal Education

In this Essay, we briefly describe key aspects of [generative artificial intelligence] that are particularly relevant to, and raise particular risks for, its potential use by lawyers and law students. We then identify three foundational goals of clinical legal education that provide useful frameworks for evaluating technological tools like GenAI: (1) practice readiness, (2) justice readiness, and (3) client-centered lawyering. First is “practice readiness,” which is about ensuring that students have the baseline abilities, knowledge, and skills to practice law upon graduation. Second is “justice readiness,” a concept proposed by Professor Jane Aiken, which is about teaching law students to critically assess the social and political implications of legal work and the legal system, as well as making space for students to confront systemic injustices and the role of lawyers in perpetuating them. Third is “client centered lawyering,” which at its root is about client empowerment and autonomy, teaching students to recognize the power imbalances present in the attorney-client relationship and the importance of ensuring client agency in decision-making. Although these are by no means the only goals of clinical education, they provide key perspectives and criteria for GenAI assessment. Finally, we examine whether GenAI is pedagogically compatible with each of these three goals. We conclude that although GenAI does present some de minimis learning opportunities for practice readiness, it is largely incompatible with justice readiness and client-centered lawyering, especially when considering the serious concerns that the development, deployment, and use of GenAI raise for those clinical programs with public interest missions

Federalism, Foreign Affairs, and State Courts: The Habeas Corpus Act of 1842 and the Permanent Debate Over the Status of International Law

There is today an ongoing debate over the status of customary international law within U.S. law. Proponents of both “modern” and “revisionist” positions advance absolutist arguments based on vague constitutional provisions, conflicting theories of constitutional structure, cherry-picked statements of favorite Founding Fathers, historical practice of the relevant branches of government, and Supreme Court precedent. Unsurprisingly, consensus around constitutional meaning remains elusive. This Article demonstrates that the current scholarly stalemate is neither novel nor unique to post-Erie debates over federal common law and the power of the federal judiciary. Indeed, Congress addressed many of the same constitutional arguments nearly two centuries ago, when a series of international incidents involving the United Kingdom prompted passage of the Habeas Corpus Act of 1842, an unprecedented expansion of federal judicial power in the antebellum United States. This largely forgotten but formative episode in our nation’s early history suggests that when it comes to the status of international law, and the authority of Congress or the federal courts to incorporate it into U.S. law, there may not be one true constitutional meaning that we have lost along the path of American constitutional history

The Legal Imitation Game: Generative AI’s Incompatibility with Clinical Legal Education

In this Essay, we briefly describe key aspects of [generative artificial intelligence] that are particularly relevant to, and raise particular risks for, its potential use by lawyers and law students. We then identify three foundational goals of clinical legal education that provide useful frameworks for evaluating technological tools like GenAI: (1) practice readiness, (2) justice readiness, and (3) client-centered lawyering. First is “practice readiness,” which is about ensuring that students have the baseline abilities, knowledge, and skills to practice law upon graduation. Second is “justice readiness,” a concept proposed by Professor Jane Aiken, which is about teaching law students to critically assess the social and political implications of legal work and the legal system, as well as making space for students to confront systemic injustices and the role of lawyers in perpetuating them. Third is “client centered lawyering,” which at its root is about client empowerment and autonomy, teaching students to recognize the power imbalances present in the attorney-client relationship and the importance of ensuring client agency in decision-making. Although these are by no means the only goals of clinical education, they provide key perspectives and criteria for GenAI assessment. Finally, we examine whether GenAI is pedagogically compatible with each of these three goals. We conclude that although GenAI does present some de minimis learning opportunities for practice readiness, it is largely incompatible with justice readiness and client-centered lawyering, especially when considering the serious concerns that the development, deployment, and use of GenAI raise for those clinical programs with public interest missions

Furthering genome design using models and algorithms

Highlights Models can investigate many more genome designs than laboratory research. Algorithms can search for genomes that optimise specific criteria. Together, models and algorithms can help engineers to design genomes. Algorithm-driven whole-cell model in silico designs could be viable in vivo. The genome design ecosystem needs improved modelling and design tools. Abstract Large-scale in silico genome designs are on the brink of being engineered in vivo, offering a potential paradigm shift for cellular research (previous designs relied on fractured available knowledge and in vivo engineering iteration) by integrating computational design, in silico models and algorithms, with laboratory construction. However, several challenges remain. If in vivo engineering is successful, designing genomes can be used to gain new understanding of cellular life, improve the metabolite production process and reduce the risk of unintended genetic modification and release. Here, we review the progress so far. We suggest improvements on recent models and algorithms, illustrate the next steps for integrating computational and laboratory engineering and offer our opinions on the future of the field