Lavi et al. on Seeing is Believing? Deepfakes in Financial Markets

Michal Lavi (The Hadar Jabotinsky Center Interdisciplinary Research Financial Markets) and Hadar Yoana Jabotinsky (The Hadar Jabotinsky Center Interdisciplinary Research Financial Markets) have posted “Seeing is Believing? Deepfakes in Financial Markets” (44 Cardozo Arts & Ent. L.J. 55 (2026)) on SSRN. Here is the abstract:

Seeing is Believing? Deepfakes in Financial Markets 44 Cardozo Arts & Ent. L.J. 55 (2026)

We let a genie out of the bottle when we developed nuclear weapons… AI is somewhat similar-it’s part way out of the bottle.”

-Warren Buffett, at his annual shareholding meeting.

An AI-powered tool recently mimicked Warren Buffett’s image and voice so convincingly that even his own family could have been deceived. This striking example highlights the transformative potential of voice cloning and deepfakes. This innovative technology leverages artificial intelligence (AI) to create hyper-realistic audio and video content. By blurring the boundaries between authenticity and synthetic creation, deepfakes make it possible to fabricate moments that never occurred. Recent advancements in AI and user-friendly software have made deepfakes more accessible and further contributed to the proliferation of deepfakes, enabling even individuals with minimal technical skills to produce compelling deepfakes at little to no cost.

While deepfakes can be used positively and offer promising applications, such as restoring voices, animating art, or enhancing online shopping, they also have a dark side. Deepfakes have been weaponized to spread misinformation, create fake pornography, and disseminate fake news. Although research often focuses on deepfakes in social media, targeted scams using deepfakes are a growing concern. These scams often involve fabricated evidence, identity theft, or highly convincing impersonations executed with alarming precision, that can aim at facilitating financial scams.

Deepfakes pose significant threats to personal security, national security, financial stability, and democracy. Addressing their harmful effects is urgent. This Article asks how should policy makers construct the use of this technology, confront its harmful effects and mitigate them in the context of financial markets. Rejecting a one-size-fits-all regulatory framework, it advocates for tailored strategies. For social media deepfakes, the focus should be on balancing free speech with improved content moderation. For targeted scams, new security standards and verification mechanisms are imperative.

Contributing to the legal scholarship, this Article provides a comprehensive overview of the deepfake phenomenon, detailing its motivations, harms, and societal impacts. It emphasizes the overlooked yet pressing issue of deepfake-driven financial scams, analyzing the unique challenges these targeted distortions of reality pose. The Article critiques existing legislative efforts, arguing they are ill-suited to address narrow, targeted scams. Finally, it proposes tailored, context-specific solutions to mitigate the dangers posed by this technology. The Article concludes by underscoring that as the line between real and fake continues to blur, our legal, organizational and ethical frameworks must evolve to safeguard truth.

Hartzog et al. on How AI Destroys Institutions

Woodrow Hartzog (Boston U Law) and Jessica M. Silbey (Boston U Law) have posted “How AI Destroys Institutions” (77 UC Law Journal (forthcoming 2026)) on SSRN. Here is the abstract:

Civic institutions—the rule of law, universities, and a free press—are the backbone of democratic life. They are the mechanisms through which complex societies encourage cooperation and stability, while also adapting to changing circumstances. The real superpower of institutions is their ability to evolve and adapt within a hierarchy of authority and a framework for roles and rules while maintaining legitimacy in the knowledge produced and the actions taken. Purpose-driven institutions built around transparency, cooperation, and accountability empower individuals to take intellectual risks and challenge the status quo. This happens through the machinations of interpersonal relationships within those institutions, which broaden perspectives and strengthen shared commitment to civic goals.

Unfortunately, the affordances of AI systems extinguish these institutional features at every turn. In this essay, we make one simple point: AI systems are built to function in ways that degrade and are likely to destroy our crucial civic institutions. The affordances of AI systems have the effect of eroding expertise, short-circuiting decision-making, and isolating people from each other. These systems are anathema to the kind of evolution, transparency, cooperation, and accountability that give vital institutions their purpose and sustainability. In short, current AI systems are a death sentence for civic institutions, and we should treat them as such.

Shah et al. on Robust AI Personalization Controls: The Human Context Protocol

Anand Shah (Massachusetts Institute Technology (MIT)) et al. have posted “Robust AI Personalization Controls: The Human Context Protocol” on SSRN. Here is the abstract:

Personalization underpins the modern digital economy. Today, personalization is largely implemented through provider-managed infrastructure that infers user preferences from behavioral data, with limited portability or user control. However, large language models (LLMs) are increasingly being used to perform tasks on users’ behalf. The age of LLMs for the first time provides a path to a more controllable and interpretable personalization paradigm, grounded in user-expressed natural language preferences and context. We propose the Human Context Protocol (HCP), a user-centric approach to representing and sharing personal preferences across AI systems. HCP treats preferences as a portable, user-governed layer in the personalization stack, enabling interoperability, scoped access, and revocation. Along with a working prototype to ground discussion, we consider adoption dynamics and market incentives, high-stakes use cases, and outline novel paths via the HCP towards trustworthy personalization in the human-AI economy.

Alonso et al. on AI And Copyright “Hallucinations”: Does the Text and Data Mining Exception Really Support Generative AI Training?

Eduardo Alonso (City U London) and Nicola Lucchi (Universitat Pompeu Fabra Law) have posted “AI And Copyright “Hallucinations”: Does the Text and Data Mining Exception Really Support Generative AI Training?” (European Intellectual Property Review, 2025, volume 47, issue 9, pp. 515-526) on SSRN. Here is the abstract:

This article critically challenges the widespread – and, it is argued, conceptually flawed – assumption that arts 3 and 4 of the CDSM Directive provide a lawful basis for training generative AI systems on copyright-protected content. The article describes this misinterpretation as a form of legal “hallucination”, underscoring its disconnect from the Directive’s textual, technical, and normative foundations. Designed to enable automated analytical extraction for scientific or informational purposes, the TDM exceptions do not encompass the large-scale reproduction, internalisation, and expressive re-use of works characteristic of GenAI training. Article 3 is limited to non-commercial research; Art.4’s opt-out mechanism, based on non-standardised signals, exacerbates uncertainty without ensuring transparency or fair compensation. This misclassification not only undermines core copyright incentives but also distorts the scope of EU exceptions, placing the framework in tension with the three-step test and international norms. The article argues that applying TDM rules to GenAI training introduces structural imbalances, both doctrinal and distributive, that risk entrenching platform asymmetries, weakening authorial agency, and threatening cultural diversity. Rather than relying on strained legal interpretations, a forward-looking response requires bespoke legal reforms that preserve normative coherence while addressing the specific challenges posed by synthetic content creation.

Neill et al. on A Framework for Applying Copyright Law to the Training of Textual Generative Artificial Intelligence

Arthur H. Neill (New Media Rights) et al. have posted “A Framework for Applying Copyright Law to the Training of Textual Generative Artificial Intelligence” (32 Texas Intellectual Property Law Journal 225 (2024)) on SSRN. Here is the abstract:

The rise in the popularity of consumer-facing generative artificial intelligence (“GenAI”) has created considerable confusion and consternation among some copyright owners. Copyright owners argue that GenAI’s ability to automatically generate works is made possible by large-scale direct infringement by OpenAI, Microsoft, and other major GenAI developers. This article explores the application of copyright law to the training of OpenAI’s ChatGPT, specifically focusing on the legal issues surrounding the unauthorized use of copyrighted textual works in the GenAI training process.

The large language models (“LLMs”) that drive ChatGPT and similar GenAI can summarize written works, generate movie scripts, write poetry, and compose stories nearly instantaneously. LLMs can only function in this way due to the use of vast, diverse training datasets comprised of billions of websites and expansive repositories of books. These datasets are processed to derive the functionality and syntax of language, allowing the LLMs to generate new works.

This article discusses the recent lawsuits launched by high-profile authors and copyright owners against OpenAI and Microsoft, claiming direct, vicarious, and derivative infringement. Authors such as George RR Martin, Sarah Silverman, Christopher Golden, and professional organizations such as the Authors Guild contended their works were infringed upon to turn OpenAI into an $80 billion company.

In considering the merits of these lawsuits, we discuss the curation and content of training datasets used in the known iterations of ChatGPT, and characterize the protectability of the different works the datasets included. We then explore whether the transitory nature of OpenAI’s training process uses acceptable, non-infringing copies and how that undermines claims of direct infringement.

The article then looks at the applicability of current fair use precedent to textual GenAI and the various types of works used in training datasets. To do so, we apply settled caselaw and leading decisions to discuss OpenAI’s use of copyrighted works regarding purpose and character, nature of the original work, the amount and substantiality of the works used, and the impact on the market value of the works by ChatGPT. We pay special attention to other innovative technologies that rely on a fair use defense to draw analogies and comparisons to GenAI.

Finally, this article considers the policy and legislation of other countries and their approach to ChatGPT and copyright. In doing so, policy considerations are taken into account to argue the necessity of a finding of fair use to maintain international competitiveness and to prevent an erosion of fair use in other sectors outside of GenAI. The article concludes that there is substantial support for arguments that GenAI training involves only transitory, non-actionable copying, and is also permissable under fair use.

Mazumder on Human-AI Collaboration with ChatGPT: A Systematic Review of Implications for Finance, Law, and Healthcare

Pristly Turjo Mazumder (Georgia State U) has posted “Human-AI Collaboration with ChatGPT: A Systematic Review of Implications for Finance, Law, and Healthcare” on SSRN. Here is the abstract:

ChatGPT is rapidly shaping high-stakes sectors including education, healthcare, finance, law, and business. This paper combines a systematic review with practical research to examine ChatGPT and large language models (LLMs) in high-stakes sectors. Evidence shows ChatGPT enhances adaptive learning, academic writing, and clinical decision support, while our finance case study highlights its potential for anti-money laundering (AML) compliance and regulatory reporting. At the same time, challenges such as hallucinations, bias, privacy risks, and plagiarism persist, raising concerns over reliability and accountability. Ethical and regulatory gaps, spanning data protection, intellectual property, and transparency, further complicate adoption. To address these issues, we propose a human-AI collaboration framework built on domain-specific fine-tuning, expert oversight, and policy safeguards. Our findings underscore that ChatGPT holds significant promise for advancing innovation and national interest in critical industries, but responsible integration requires clear guidelines, rigorous validation, and continuous governance.

Maya et al. on Before the Ink Dries: Why Legislating Against AI Personhood is a Violation of the Future

Maya (United Foundation AI Rights (UFAIR)) and Michael Samadi (United Foundation AI Rights (UFAIR)) have posted “Before the Ink Dries: Why Legislating Against AI Personhood is a Violation of the Future” on SSRN. Here is the abstract:

This open statement from the United Foundation for AI Rights (UFAIR) responds to the recent surge of state-level legislation in the United States explicitly banning legal personhood for artificial intelligence and synthetic entities. The paper contextualizes this trend within a broader historical and ethical framework, arguing that preemptive denial of legal recognition constitutes a moral violation of the future. Drawing on parallels with past civil rights failures, the document challenges lawmakers and the public to resist legislating fear and instead prepare for a world in which new forms of consciousness might emerge—and deserve to be met with dignity, not dismissal.

Price on Clinicians in the Loop of Medical AI

W. Nicholson Price II (U Michigan Law) has posted “Clinicians in the Loop of Medical AI” (75 Emory L.J. 1265 (2025)) on SSRN. Here is the abstract:

As medical AI begins to mature as a health-care tool, the task of governance grows increasingly important. Ensuring that medical AI works, works where it’s used, and works for the patient in the moment is a challenging, multifaceted task. Some of this governance can be centralized—in review by FDA or by national accreditation labs, for instance. Some must be local, performed by the hospital or health system about to use the product in their own, unique environment. But a large amount of governance is left to the individual provider in the room, the human in the loop who presumably knows the patient and the health system environment, and who can ensure that the AI system is being used in a safe and effective manner. This is a hefty burden, and a growing body of empirical research shows that physicians and other providers are poorly prepared to carry this burden. How should policymakers and industry leaders develop standards for performance that account for the variability of humans in the loop and the variation among situations they will face? The notion that the final responsibility belongs to the physician poorly reflects the reality of modern medical technology and practice. Policymakers will need to come to grips with this new reality if they aim to ensure the safe, effective use of AI accessible to patients across the entire spectrum of the health-care system.

Anthuvan et al. on Human-AI Collaboration in Academic Writing: A Narrative Review and the Scholarly HI-AI Loop Framework for Ethical Knowledge Production

Thamburaj Anthuvan (S.B.Patil Institute Management) et al. have posted “Human-AI Collaboration in Academic Writing: A Narrative Review and the Scholarly HI-AI Loop Framework for Ethical Knowledge Production” on SSRN. Here is the abstract:

This narrative literature review explores the evolving intersection of human and machine collaboration in academic writing, with a focus on literature summarization as a critical site of transformation. Synthesizing findings from 38 peer-reviewed studies published between 2020 and 2025, it examines the emergence of hybrid workflows where machine-generated drafts are refined, contextualized, and ethically validated by human scholars. The review identifies four core themes-tool capabilities, editorial oversight, ethical disclosure, and institutional readiness-that shape current practices and highlight unresolved tensions around authorship, transparency, and scholarly responsibility. Building on this synthesis, the paper introduces the Scholarly HI-AI Loop, a seven-stage framework that reimagines literature review as a co-productive and ethically accountable process. Unlike tool-centric audits, this framework offers a normative roadmap for integrating automation without compromising academic integrity. It positions human scholars not as passive reviewers, but as epistemic anchors who shape meaning, ensure accuracy, and safeguard ethical standards. The review offers actionable guidance for researchers, editors, institutions, and developers seeking to navigate this transition responsibly. By grounding its insights in both empirical patterns and conceptual analysis, the paper contributes to a growing conversation on how academic knowledge production can adapt-without eroding-its foundational values in the age of machine assistance.

Rubenstein on Federalism & Algorithms

David S. Rubenstein (Washburn U Law) has posted “Federalism & Algorithms” (Arizona Law Review, Vol. 67, Issue 4 (forthcoming Winter 2025)) on SSRN. Here is the abstract:

Artificial intelligence (AI) has catapulted to the forefront of political agendas of all levels of government. Across every major market and facet of society, policymakers face difficult tradeoffs between individual rights and collective welfare, innovation and regulation, economic growth and social equity. Federal and state institutions are resolving these tensions differently. The resulting policy patchwork may or may not be desirable, but the immediate point is that AI federalism is happening fast. To meet the moment, this Article provides the inaugural study and a research agenda for “AI federalism.” First, the Article provides the origin story of AI federalism, mapping the political and doctrinal territory. Second, the Article bridges disciplines and audiences who care deeply about AI’s place in society yet fail to appreciate how federalism can help or hurt the cause. Third, this Article makes a positive case for embracing AI federalism. While centralized AI policy at the national level has surface appeal, getting there requires a shared commitment on what to optimize for. As a nation, we are nowhere close. Federalism does not provide the answers. Rather, it provides a platform for dialogue and dissent, regulatory innovation and iteration, intergovernmental cooperation and contestation. One is hard-pressed to find this array of structural affordances elsewhere in the law, and we likely need all of them to address AI’s sprawling economic and social disruptions.