David S. Kemp analyzes the first federal court ruling on AI and attorney-client privilege, United States v. Heppner, examining the court’s reasoning across each element of the privilege test and the work product doctrine. Mr. Kemp argues that while the court reached the correct result on two independent and sufficient grounds, its unnecessary confidentiality analysis was methodologically flawed—treating Anthropic’s broadest contractual reserved rights as dispositive without examining the specific terms, product tier, or training preferences that actually governed Heppner’s use. He warns that this overbroad reasoning, rather than the uncontroversial holdings, is what future courts will most likely cite, with potentially damaging consequences for privilege claims in any professional context involving third-party platforms.
Cornell Law professor Michael C. Dorf discusses the recent standoff between Anthropic and the Pentagon over Anthropic’s refusal to permit its AI tools to be used for mass surveillance or autonomous weapons, leading the Trump administration to designate Anthropic a national security supply-chain. Professor Dorf argues that while both mass surveillance and autonomous weapons deployment may already be unlawful under the Fourth Amendment and customary international law respectively, Anthropic had sound reasons to seek explicit contractual carveouts rather than rely on those legal limits—and that the Pentagon’s unwillingness to accept those carveouts raises the alarming inference that the administration intends to pursue both activities.
UC Davis Law professor Peter Lee discusses the growing use of synthetic data to train AI models and its advantages over real-world data in addressing technical limitations and legal issues like privacy, bias, and copyright infringement. Professor Lee argues that while synthetic data offers promising solutions through unlimited, high-quality training content, it also poses significant risks including model collapse, new biases, and enabling dangerous AI applications, requiring careful regulation and responsible deployment.
NYU Law professor Samuel Estreicher and JD candidate Lior Polani examinee the limitations of artificial intelligence (AI) in legal practice, focusing on the technical constraint of “context windows”—the limited amount of information AI systems can process at once—which hampers their ability to handle complex, interconnected legal documents and tasks requiring nuanced judgment. The authors argue that while AI can boost efficiency in narrow, rule-based legal functions, its fundamental constraints make fully autonomous legal analysis unreliable, and they emphasize that AI should be used as a supportive tool under human oversight rather than a replacement for legal professionals.
Cornell Law professor Michael C. Dorf critiques the Trump administration’s tariff policies and broader economic strategy, arguing that they are misguided in the face of rapidly advancing technology, particularly artificial intelligence (AI). Professor Dorf contends that instead of clinging to outdated protectionist policies, U.S. leadership should focus on preparing for the disruptive impact of artificial general intelligence (AGI) and artificial super intelligence (ASI) on employment and productivity, a challenge for which Donald Trump is uniquely unqualified.
USF Law visiting professor Michele Neitz examines the emergence of DeepSeek R1, a low-cost open-source AI model from China, and its implications for the democratization of AI technology development beyond major tech companies. Professor Neitz argues that while this democratization offers benefits like increased innovation, affordability, and diverse participation, it also presents significant challenges around data privacy, security, and responsible development that require thoughtful regulatory responses rather than outright bans.
Surgeon and bioethicist Charles E. Binkley discusses the ethical implications and potential harms of using artificial intelligence (AI) in healthcare decision-making, particularly focusing on informed consent and physician responsibility. Dr. Binkley argues that patients should be informed when AI is used in their care, and that healthcare providers have a duty not only to inform patients of potential risks but also to mitigate those risks, emphasizing that the use of AI does not absolve physicians of their responsibilities to patients.
Surgeon and bioethicist Charles E. Binkley discusses the ethical implications of using artificial intelligence (AI) models in clinical decision-making, particularly focusing on patient informed consent. Dr. Binkley argues that patients should be fully informed about the use of AI in their healthcare, not only as patients but also as data donors and potential research subjects, to maintain autonomy, transparency, and trust in the physician-patient relationship.
Cornell Law professor Michael C. Dorf considers the implications of ChatGPT and other generative AI tools in law schools. Professor Dorf observes that for now, smart, well-motivated students will outperform AI in most tasks required of law students, but legal educators will soon have to grapple with the reality that banning AI-based tools will make less and less sense as they become more mainstream various ways in legal practice.
Cornell professor Joseph Margulies expresses concern over the ability of ChatGPT—the AI-powered chatbot—to draft increasingly sophisticated and accurate writings that some college students might use instead of putting in the painstaking work of writing on their own. Professor Margulies asked ChatGPT to generate a response to an assignment akin to one he would assign in his own class, and it generated a B-quality essay. He then explores what this means for student learning—particularly in the context of writing.
Charles E. Binkley, director of bioethics at Santa Clara University’s Markkula Center for Applied Ethics, describes some critical ethical issues raised by the use of artificial intelligence (AI) and machine learning (ML) systems for clinical decision support in medicine. Dr. Binkley calls for resolution of these issues before these emerging technologies are widely implemented.
Cornell University law professor Michael Dorf explores the relationship between renewed discussions about artificial intelligence (AI) and the rights of non-human animals. Dorf argues that our current portrayals of AI reflect guilt over our disregard for the interests of the billions of sentient animals we exploit, torture, and kill in the here and now.
