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In March of 2023, OpenAI released GPT-4, an autoregressive language model that uses deep learning to produce text. GPT-4 has unprecedented ability to practice law: drafting briefs and memos, plotting litigation strategy, and providing general legal advice. However, scholars and practitioners have yet to unpack the implications of large language models, such as GPT-4, for long-standing bar association rules on the unauthorized practice of law ('UPL'). The intersection of large language models with UPL raises manifold issues, including those pertaining to important and developing jurisprudence on free speech, antitrust, occupational licensing, and the inherent-powers doctrine. How the intersection is navigated, moreover, is of vital importance in the durative struggle for access to justice, and low-income individuals will be disproportionately impacted. In this Article, we offer a recommendation that is both attuned to technological advances and avoids the extremes that have characterized the past decades of the UPL debate. Rather than abandon UPL rules, and rather than leave them undisturbed, we propose that they be recast as primarily regulation of entity-type claims. Through this recasting, bar associations can retain their role as the ultimate determiners of 'lawyer' and 'attorney' classifications while allowing nonlawyers, including the AI-powered entities that have emerged in recent years, to provide legal services--save for a narrow and clearly defined subset. Although this recommendation is novel, it is easy to implement, comes with few downsides, and would further the twin UPL aims of competency and ethicality better than traditional UPL enforcement. Legal technology companies would be freed from operating in a legal gray area; states would no longer have to create elaborate UPL-avoiding mechanisms, such as Utah's 'legal sandbox'; consumers--both individuals and companies--would benefit from better and cheaper legal services; and the dismantling of access-to-justice barriers would finally be possible. Moreover, the clouds of free speech and antitrust challenges that are massing above current UPL rules would dissipate, and bar associations would be able to focus on fulfilling their already established UPL-related aims., Article Contents Introduction I. ChatGPT Meets UPL .... A. What is UPL? B. UPL's Existential Problems C. UPL in Action (Causing Inaction) II. Human-AI Legal Collaboration III. The Case for [...]

Racial disparities in conviction and incarceration have been lamentable features of legal systems for a long time. Research has addressed the attitudes and decisions of police, prosecutors, jurors, and judges in contributing to these disparities, but very little attention has been paid to defendants' own team members—i.e., criminal defense attorneys. Researchers have specifically identified this as a "scholarly gap". To address this, we conducted an empirical study of criminal defense attorneys practicing in forty-three U.S. states (N = 327). The attorneys completed both an implicit measure designed to capture racial bias (a race Implicit Association Test) and an explicit measure designed to capture interpersonal regard for clients. The results provided support for longstanding, but previously speculative, assertions of bias in criminal defense. [ABSTRACT FROM AUTHOR]

The article focuses on how neuroscientific evidence is impinging or threatening to impinge the U.S. legal system. Topics include the areas in which there have been developments in the use of neuroscientific evidence, namely, brain defense and testimony, neuroscientific evidence in U.S. law and the false distinctions drawn from inconsistent use of neuroscience.