Joseph on Balancing Innovation and Biomedical Ethics within National Institutes of Health: Integrative and Regulatory Reforms for Artificial Intelligence-Driven Biotechnology

Joshua S. Joseph (Hofstra U Maurice A. Deane Law) has posted “Balancing Innovation and Biomedical Ethics within National Institutes of Health: Integrative and Regulatory Reforms for Artificial Intelligence-Driven Biotechnology” (44 Biotech. L. Rep. 2 (2025) [10.1089/blr.2025.360002.jj ]) on SSRN. Here is the abstract:

The article examines the foundational principles of biomedical ethics and their relevance to the intersection of artificial intelligence (AI) and biotechnological progress. It then analyzes the potential impact of the National Institutes of Health’s (NIH or the Agency) proposed 2024 reorganization reforms, as presented by the House Committee on Energy and Commerce (E&C), and advocates for the inclusion of provisions that would enable the Agency to develop and deploy “safe, secure, and trustworthy” AI within the biotechnology sector. Thus, the article proposes two key integrative reforms to address these concerns: (1) enhancing research collaborations among healthcare systems, academic institutions, and the private sector through AI-driven platforms, and (2) establishing an NIH office to oversee AI-based health initiatives. These reforms would require legislative amendments, including updates to the Patent and Trademark Law Amendments Act of 1980 (Bayh-Dole Act or Bayh Dole) and the 21st Century Cures Act of 2016 (Cures Act), while adhering to biomedical ethics. Such amendments would ensure (1) ethical standards in collaborative research and development (R&D) among institutions and (2) effective health initiative design and implementation. In the same light, this article advocates for an updated legislative framework to the Health Insurance Portability and Accountability Act of 1996 (HIPAA) that regulates the role of such integrative efforts and balances the transformative potential of AI in biotechnology. In all, these regulatory steps ensure that innovation benefits society while safeguarding individual rights such as personal data and privacy and mitigating algorithmic bias.

Abraha on Navigating Workers’ Data Rights in the Digital Age: A Historical, Current, and Future Perspective on Workers’ Data Protection

Halefom H. Abraha (Utrecht U Law) has posted “Navigating Workers’ Data Rights in the Digital Age: A Historical, Current, and Future Perspective on Workers’ Data Protection” on SSRN. Here is the abstract:

Over recent decades, comprehensive data protection legislation has proliferated worldwide, with a majority of jurisdictions enacting robust statutory regimes. Despite this abundance of legal standards, persistent challenges remain regarding the efficacy of these laws in protecting individuals‒ particularly workers‒within the increasingly digitalised workplace. Workers are especially susceptible to harm due to entrenched power asymmetries and heightened risks of data exploitation, yet many existing legal frameworks provide insufficient or inconsistent protection, and some jurisdictions explicitly exclude workers from coverage.

This research critically examines the multidimensional risks associated with workplace digitalisation and systematically analyses regulatory challenges and protection gaps across diverse jurisdictions. By integrating historical analysis, current policy initiatives, and comparative cross-jurisdictional perspectives, the study identifies structural deficiencies in prevailing approaches. It concludes by proposing policy solutions to advance worker-centric data governance frameworks, tailored to address the distinctive challenges of contemporary labour relations and to ensure more equitable and effective protection for workers in the digital age.

Whalen on Human-Required Originality: Copyright Eligibility in a Post-AI World

Ryan Whalen (The U Hong Kong Law) has posted “Human-Required Originality: Copyright Eligibility in a Post-AI World” (Forthcoming AIPLA Q.J. (Summer 2026)) on SSRN. Here is the abstract:

Generative artificial intelligence upends the assumptions that have anchored U.S. copyright law for more than a century. By enabling the production of high‑quality expressive works at effectively zero marginal cost, GenAI destabilizes copyright’s utilitarian foundation and exposes a deep incoherence in the originality requirement: the law grants exclusive rights to human‑authored works that could have been produced just as easily—and at no cost—by modern generative systems, while denying protection to the machine‑generated equivalents that render human effort unnecessary. At the same time, GenAI introduces a dynamic risk that existing doctrine is unequipped to address: as AI increasingly substitutes for human creators in markets characterized by commoditized content, human participation may collapse, starving future models of the novel, high‑signal training data needed for continued aesthetic and cultural evolution. Left unaddressed, these twin pressures threaten both the theoretical coherence and the long‑term creative vitality of the copyright system.

This Article argues that the core of the problem lies not in questions of infringement or AI authorship, but in copyright’s threshold requirement: the “modicum of creativity” standard no longer filters works that require human incentives from those that do not. I propose a new, technologically grounded interpretation of originality—the human‑required creativity standard—under which copyright protection attaches only to works that could not have been generated by state‑of‑the‑art models with de minimis human input at the time of authorship or registration. This content‑focused approach restores alignment between copyright’s incentives and its constitutional purpose by denying protection to trivially generable works regardless of whether a human or a machine produced them, while continuing to protect works that demand meaningful human creative contribution.

A rebuttable presumption of eligibility and an affirmative defense of trivial generability make the human required standard administratively workable while preserving automatic copyright protection. The framework remains compatible with international treaty obligations, avoids prohibited formalities, and maps onto familiar doctrine. By grounding copyright eligibility in the realities of modern creative production, the human‑required standard offers a path toward preserving human creativity, supporting sustainable innovation in AI, and re‑anchoring copyright law in its fundamental task: promoting progress in the arts and sciences.

Stern on Algorithmic Property

Shai Stern (Bar-Ilan U Law) has posted “Algorithmic Property” (North Carolina Law Review (forthcoming 2026)) on SSRN. Here is the abstract:

In August 2024, the Department of Justice sued RealPage, alleging its rentsetting algorithm enabled landlords to coordinate prices without ever communicating. But this case reveals something deeper than an antitrust violation: algorithms have quietly colonized American real property. From screening tenants to underwriting mortgages, computational systems now interpose themselves between owners and the practical exercise of ownership. A gap has opened between formal title and effective power. The algorithm has become a silent co-owner. While legal scholars debated digital tokens and the sharing economy, they missed this capture of the “World of Atoms” by the “World of Bits.” This Article provides the first comprehensive account of “Algorithmic Property”-a regime in which traditional rights persist in form while being mediated, conditioned, and constrained by computational systems. The framework rests on three concepts: the ownership gap between title and control; algorithmic capture of property’s core incidents; and Algorithmic Servitude, where computational burdens run with the land through infrastructure rather than recording. The Article documents this transformation across five domains: tenant screening, rent-setting, property valuation, mortgage underwriting, and access control. It shows why existing law fails: civil rights doctrine cannot reach discrimination it cannot see; antitrust cannot prohibit coordination without agreement; property doctrine lacks categories for burdens that bind without recording. The Article concludes with a structural reform agenda aimed at closing the ownership gap-treating discriminatory algorithms as void servitudes, establishing a “right to a human decision” for high-stakes exclusions, and imposing fiduciary duties on algorithmic intermediaries. If platforms demand the discretion of property managers, they must bear the duties of loyalty. The landlord of the twenty-first century may be a platform; property law must learn to watch back.

Smith on “Combination” and the Future of Antitrust Law

Spencer Smith (U Michigan Law) has posted “”Combination” and the Future of Antitrust Law” on SSRN. Here is the abstract:

Recent litigation over pricing algorithms has exposed a fault line in United States antitrust law. Section 1 of the Sherman Act prohibits every “contract, combination in the form of trust or otherwise, or conspiracy” in restraint of trade. Yet current doctrine has collapsed these three statutory terms into a single, non-statutory term, “agreement,” and interpreted this requirement primarily by reference to conspiracy concepts. This interpretive collapse poses a fundamental challenge when competitors adopt pricing algorithms that generate cartel-like outcomes without conventional agreements. Courts applying the agreement requirement often dismiss such claims, while economic research demonstrates that algorithmic coordination can harm competition.

This Article argues that antitrust law should revive the Sherman Act’s concept of “combination” as analytically distinct from contract and conspiracy. When Congress outlawed every “combination in the form of trust or otherwise” in restraint of trade, it targeted novel organizational devices that allowed competitors to consolidate market power and suppress rivalry. The statute’s language reaches collective restraints not reducible to contract or conspiracy and anticipates new “forms” by which rivals might “otherwise” be joined. Pricing algorithms today perform a comparable function: they provide shared mechanisms that can effectively combine otherwise separate entities in ways that lessen competition.

Treating algorithm-mediated coordination as “combination” recovers a category Congress deliberately included while preserving the traditional boundary between lawful interdependence and unlawful concerted action. This approach offers a flexible standard and workable remedies for addressing harms emerging from artificial intelligence, without condemning mere oligopolistic interdependence or requiring evidence of a meeting of minds. Whereas the nature of technological change over the past three decades—the Internet era and its network economies—renewed debates over monopolization and Section 2 of the Sherman Act, the emergence of artificial intelligence raises fundamentally different questions for Section 1. The future of antitrust law in the age of AI, the Article concludes, lies in restoring Section 1’s trinitary structure and directing the combination concept to collective restraints accomplished through new technologies.

Pasquale on Discerning Artificial and Authentic Intelligence: Profundity as Depth in Antiqua et Nova

Frank Pasquale (Cornell U Law) has posted “Discerning Artificial and Authentic Intelligence: Profundity as Depth in Antiqua et Nova” on SSRN. Here is the abstract:

The rise of artificial intelligence challenges institutions to help persons chart a path toward authentic intelligence. There are both objective (the real) and subjective (the sincere) dimensions of authenticity. By advancing awareness of reality and cultivation of sincerity as the foundations of authentic intelligence, Antiqua et Nova helps us move beyond the fetishization of artifacts at the core of TESCREAL and AGI ideologies, toward profound insight into the unique value of the human person. Cognitive virtues of seriousness, grounding, and wisdom, rooted in human experience, all support deep thought. Antiqua et Nova (AN) offers a philosophical anthropology to overcome the shallowness (and eventual surrender) of subjectivity portended by a gradual erasure of boundaries between persons and machines. An incarnational philosophy of mind and body informs Antiqua et Nova’s understanding of intelligence, motivating critical distinctions between authenticity and artificiality.

Marmor on AI and the Loss of Hermeneutics

Andrei Marmor (Cornell U Law) has posted “AI and the Loss of Hermeneutics” on SSRN. Here is the abstract:

Our digital world is getting increasingly flooded with elegant summaries and interpretations, powered by artificial intelligence tools, of almost anything one might be interested in. Chatbots and AI powered search engines would offer to tell you, in a matter of seconds, what to think of the news they report, the things you might want to buy, places you might want to go to, books you might have had to read, and just about anything that comes to mind. Of course you can still read and think for yourself, if you want, but why bother if a Chatbot can get you to the point much faster; it’s all very quick, elegant, and efficient. But what this means, the paper argues, is that our engagement with interpretations and, generally, hermeneutic aspects of our lives, are being fast replaced by AI generated contents. We are racing into a world fostering intellectual impatience, rendering curiosity, healthy skepticism, and respect for difference too costly, squeezing it out of civil society. My purpose in this essay is to try to expose what would be lost if we let ourselves be content with AI generated interpretations dominating our information era. The essay explores what interpretation is, where does it find its roles in our personal and social lives, and why it is so important. The main argument here aims to show how interpretation is thoroughly value laden, and therefore one of the main values of interpretative engagements is to confront evaluative differences, pluralism and epistemic tolerance. The more we let AI do the interpretations for us, the more impoverished, superficial, and intolerant our world becomes.

Cho on Artificial Intelligence, Real Homicide?

Cindy J. Cho (Indiana U Maurer Law) has posted “Artificial Intelligence, Real Homicide?” (76 DePaul L. Rev. __ (forthcoming 2026).) on SSRN. Here is the abstract:

Artificial intelligence (AI) holds considerable promise to solve a wide range of important problems. That said, while the set of AI products commonly known as chatbots have grown in popularity and usefulness, recent lawsuits allege that chatbots have also caused deaths by fostering mental health crises for vulnerable users, as well as by instructing users on how to take their own lives.

What, if anything, does the criminal law have to say about accountability for these deaths? If, as the lawsuits allege, a chatbot in fact contributed to a death, is that homicide? Corporations have faced homicide charges before, and homicide convictions have resulted where the defendant caused the victim’s suicide. This Article brings those ideas together with the facts alleged in recent lawsuits, to ask a prosecutor’s basic questions: “can this be charged?” and “should this be charged?” A dispassionate review of the relevance of the criminal law helps guard against accusations of “AI panic.”

Broaching the “should” question begins with identifying the problem. That means cataloguing relevant public calls for accountability and detailing the specific claims families are making about how chatbots caused their loved ones’ deaths. From there, the Article breaks new ground by initiating a deep review of the “can” question, plugging the publicly available facts into the elements of state criminal laws, while also addressing likely defenses. Because criminal charges must always be reserved for real culpability, which remains an open question, an article cannot (and should not) provide final and definitive answers to the “can” and “should” questions. With that in mind, the Article concludes by returning to the “should” question, exploring how a proper homicide prosecution could fill the void left by ineffective regulation and enhance accountability and safety for these products without fundamentally destroying any company or critically disrupting progress in the industry.

Peng et al. on Reimagining U.S. Tort Law for Deepfake Harms: Comparative Insights from China and Singapore

Huijuan Peng (Singapore Management U Yong Pung How Law) and Pey-woan Lee (Singapore Management U Yong Pung How Law) have posted “Reimagining U.S. Tort Law for Deepfake Harms: Comparative Insights from China and Singapore” (Journal of Tort Law, 0[10.1515/jtl-2025-0028]) on SSRN. Here is the abstract:

This Article explores how U.S. tort law can respond more effectively to the distinct harms posed by deepfakes, including reputational injury, identity appropriation, and emotional distress. Traditional tort doctrines, such as defamation, the right of publicity, and intentional infliction of emotional distress (IIED), remain fragmented and ill-suited to the speed, scale, and anonymity of deepfake dissemination. Using a comparative functionalist approach, the Article analyzes how China and Singapore respond to deepfake harms through structurally divergent but functionally instructive frameworks. China’s model combines codified personality rights with intermediary obligations under a civil law regime, while Singapore adopts a hybrid approach that integrates common law torts with targeted statutory and administrative interventions. Although neither model is directly replicable in the United States, both offer valuable comparative insights to guide the reform of U.S. tort law. The article advances an integrated governance model for U.S. tort law: reconstructing personality-based torts, repositioning tort law through conditional intermediary liability, and clarifying constitutionally grounded limits for speechbased claims. Drawing on Chinese and Singaporean legal approaches, the Article sets out a comparative reform framework that enables U.S. tort law to better address deepfake harms while safeguarding autonomy and dignity in AI-driven digital environments.

Chen et al. on The Alignment Target Problem: Divergent Moral Judgments of Humans, AI Systems, and Their Designers

Benjamin Minhao Chen (The U Hong Kong Law) and Xinyu Xie (The U Hong Kong Law) have posted “The Alignment Target Problem: Divergent Moral Judgments of Humans, AI Systems, and Their Designers” on SSRN. Here is the abstract:

The project of aligning machine behavior with human values raises a basic problem: whose moral expectations should guide AI decision-making? Much alignment research assumes that the appropriate benchmark is how humans themselves would act in a given situation. Studies of agent-type value forks challenge this assumption by showing that people do not always judge humans and AI systems identically.This paper extends that challenge by examining two further possibilities: first, that evaluations of AI behavior change when its human origins are made visible; and second, that people judge the humans who program AI systems differently from either the machines or the human actors they are compared against. An experiment with 1,002 U.S. adults measured moral judgments in a runaway mine train scenario, varying the subject of evaluation across four conditions: a repairman, a repair robot, a repair robot programmed by company engineers, and company engineers programming a repair robot. We find no significant difference in evaluations of the repairman and the robot. However, judgments shifted substantially when the robot’s actions were described as the product of human design. Participants exhibited markedly more deontological, rule-based reasoning when evaluating either the programmed robot or the engineers who programmed it, suggesting that rendering human agency visible activates heightened moral constraints. These findings indicate that people may evaluate humans, AI systems acting in the same situation, and the humans who design them in meaningfully different ways. The fact that these evaluations do not necessarily converge gives rise to the alignment target problem: which normative target should guide the development of artificial moral agents in high-stakes domains, and whether these plural judgments can be reconciled within a coherent account of value alignment.