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  • Meet the Author: Nikolas Bowie

    Meet the Author: Nikolas Bowie

    Nikolas Bowie is a leading constitutional scholar and legal historian whose work challenges conventional understandings of judicial power, democratic governance, and the role of the Constitution in public life. As the Louis D. Brandeis Professor of Law at Harvard Law School, Bowie brings a critical historical perspective to some of the most contested questions in American law, from judicial supremacy and separation of powers to immigration and political membership. In this interview, he reflects on how critical legal history can reframe entrenched legal narratives, the democratic implications of an increasingly powerful Supreme Court, and why constitutional meaning should not be left solely to the judiciary. His insights offer a compelling vision of constitutional interpretation rooted in democratic engagement and historical contingency. 

    Q: Your work often reframes canonical constitutional narratives through a historical lens. How do you see critical legal history reshaping the way scholars and the public understand constitutional authority today? 

    A: I studied history as an undergraduate, and my advisor, Jennifer Klein, taught me that one of history’s most powerful uses is to denaturalize present‑day injustices. When we understand that today’s institutions however fixed they seem were created through human choices, it becomes easier to imagine how they might be remade or even undone. 

    Critical legal history, for me, is about tracing those choices and the contexts that shaped them. It reveals the alternative paths our legal structures could have taken and opens space to consider different possibilities going forward. By showing that constitutional authority is neither inevitable nor immutable, history helps us see how the world might look if we choose differently now. 

    Q: In your testimony to the Presidential Commission on the Supreme Court, you argue that judicial review has undermined political equality. What do you see as the most urgent democratic reforms to recalibrate the Court’s power? 

    A: I’m currently writing a book with my Harvard colleague Daphna Renan, Supremacy: How Rule by the Court Replaced Government by the People. We trace the history of judicial supremacy, the idea that the Supreme Court has the final word on constitutional meaning. Seen only from today’s vantage point, that structure can look inevitable. But historically, it has been contested from the start. 

    Our research shows that abolitionists, civil‑rights leaders, labour organizers, suffragists, and other democratic movements long championed an alternative: democratic constitutionalism. They argued that the people, acting through their elected representatives, should have the ultimate authority to interpret the Constitution rather than an unelected judiciary presiding over one of the hardest constitutions in the world to amend. 

    The reforms we propose build on that tradition. We argue that Congress already has significant constitutional power and should be encouraged to use it to articulate its own constitutional judgments even when the Court disagrees. After the Court’s recent decision weakening the Voting Rights Act, for example, we wrote in the New York Times that Congress should not only reenact protections but also shield them from judicial invalidation. That could include requiring courts to defer to Congress’s constitutional interpretation, limiting the Court’s jurisdiction, adding members, or adopting other tools that reinforce democratic, rather than judicial, supremacy. 

    Q: In The Imaginary Immigration Clause, you challenge the conventional reading of the Chinese Exclusion Case. What do you think has allowed this misinterpretation to persist for so long in both doctrine and scholarship? 

    A: That article, which I co‑wrote with my former research assistant Norah Rast, started with a simple question: why do so many people assume the Constitution gives the federal government unlimited power over immigration? Many scholars argue that the Supreme Court has long recognized this plenary power and trace it back to the Chinese Exclusion Case of the 1880s. 

    What we found, though, is that before that decision, Congress spent decades fiercely debating whether it even had constitutional authority to deport or exclude people. The first attempt a deportation provision in the Alien and Sedition Acts was so controversial that voters elected a new Congress to let it expire. Throughout the 19th century, lawmakers and the public remained deeply reluctant to endorse broad federal immigration power. 

    After the Chinese Exclusion Case, that debate largely disappeared. Restrictionists pointed to the decision as if it settled the constitutional question, reading it to mean that Congress could exercise whatever “sovereign powers” it deemed necessary over immigration. That interpretation stuck, even though it didn’t reflect the actual constitutional arguments of the time. 

    Our broader point is that when the Supreme Court declares what the Constitution means, it doesn’t just matter when the Court strikes down good laws. It also matters when the Court upholds harmful ones, because it can shut down democratic debate and limit our ability to imagine constitutional alternatives. 

     Q: Your paper argues that Congress historically lacked a freestanding immigration power. How would acknowledging this history change today’s debates about federal immigration authority? 

    A: One thing we wanted contemporary readers to see is that there was never a single moment when Americans collectively decided the federal government should have sweeping, uncontrollable authority over immigrants. If anything, there has long been a strong tradition of critics who doubted that Congress had this power at all. Those critics included figures like James Madison, who challenged Congress’s first deportation act as unconstitutional. 

    Today, many immigration advocates focus almost entirely on persuading the federal judiciary to impose limits on Congress. Part of our argument is that this may be a misguided strategy. Instead, it may be more effective to persuade members of Congress themselves that the Constitution protects immigrants and gives them similar rights to live and work in the United States as citizens. When Congress regulates non‑citizens, it is exercising the same powers it exercises over citizens, and treating immigrants as an unprotected category risks everyone’s liberty and dignity. 

    What we hoped to offer with this article is a different narrative—one that rejects the idea that “it has always been this way.” Recognizing that Congress historically lacked a freestanding immigration power should change our expectations of what our representatives think the federal government ought to be doing in this area. 

    Q: You trace modern separationofpowers doctrine to a reactionary postReconstruction project. How should this history inform current debates about presidential power?

    A: This was another article I wrote with Daphna Renan, and it’s something we develop further in Supremacy. The piece looks at how the Supreme Court inserted itself into debates over the constitutionality of federal laws regulating the president. For much of early U.S. history, these questions were worked out politically: Congress passed statutes regulating the executive branch, and the president signed, debated, or vetoed them. That push‑and‑pull created what we call a Republican separation of powers, where constitutional boundaries were provisional and negotiated through representative government. 

    Since 1926, though, the Court has reframed separation of powers as a legal not political domain, governed by implied constitutional rules that only judges can identify. That shift has effectively given presidents a kind of escape hatch: when Congress imposes limits, the executive can go to the Court and argue that those limits interfere with its implied constitutional authority. 

    Our view is that the judiciary’s role should be to enforce federal law, not to help presidents evade it. This argument predates cases like Trump v. United States, which granted broad immunity from federal criminal regulation, but that decision underscores the problem. Instead of a president who can treat statutes as optional, we should have a judiciary that enforces those statutes unless and until a future Congress and president repeal them. 

    Q: You contrast a “republican” conception of separation of powers with a “juristocratic” one. What would governance look like if Congress reclaimed its constitutional role in structuring the executive branch? 

    A: I think it would look much more like what people imagine the Constitution requires than what we have now. Over just the past six months, we’ve seen a president initiate a war and violate a range of statutory limits laws protecting civil servants, universities, law firms, tariff rules, and even birthright citizenship. In several of these areas, the president has asked the Supreme Court to say that, despite violating federal law, he has “conclusive and preclusive” constitutional authority that Congress cannot regulate. 

    As long as the judiciary is willing to validate those claims, we end up with a system where the president can effectively stand above the law. A more republican separation of powers would instead treat statutes as binding until Congress repeals them. If a president has constitutional objections, the place to raise them is in vetoing a bill or urging Congress to change the law not in asking courts to intervene after the fact. 

    That kind of system would mean Congress decides whether we go to war. Congress determines how to protect the public from a corrupt executive branch. Congress structures independent agencies and ensures a Justice Department that pursues justice rather than the president’s political enemies. In short, governance would reflect the choices of our elected representatives, not the dictates of an unelected judiciary. 

    Q: Your research includes the history of noncitizen voting. What lessons from early American practice do you think are most relevant to today’s polarized debates about political membership? 

    A: One fact about U.S. history that many people don’t appreciate is how common non‑citizen voting once was. Throughout the 19th century especially west of the Mississippi many states allowed non‑citizens to vote. They did so for a range of reasons: to attract immigrants whose contributions they valued, because they believed expanding the franchise was democratic, and because they thought more inclusive electorates produced better governance. The same logic that later expanded voting rights to women and people of colour also supported extending the vote to immigrants. 

    Since the 1920s, however, every state has prohibited non‑citizens from voting in state legislative elections. But under Article I of the Constitution, if a state did allow non‑citizens to vote for its legislature, those voters would also be eligible to vote for members of Congress. That’s important context for today’s debates, including proposals to require passports at the polls based on the assumption that only citizens have ever been eligible to vote. 

    This history helps denaturalize the idea that voting and citizenship must always go hand in hand. And as more local governments experiment with non‑citizen voting—New York City recently attempted it, and several jurisdictions in Maryland and Vermont already allow it , it’s useful to remember that these efforts fit within a long American tradition. They’re not novel experiments but revivals of a practice that once shaped the country. 

    Q: You’re deeply involved in local governance in Cambridge. How does your academic work on democracy and constitutional structure inform your approach to zoning, planning, and municipal decisionmaking?   

    A: One concrete way that participating in state and local government has shaped my scholarship and teaching is by highlighting the importance of teaching theories of change beyond litigation. Most doctrinal law‑school classes revolve around Supreme Court or appellate cases, training students to argue before judges or imagine themselves as future judges. 

    But when ordinary people think about laws reviewing them, proposing them, trying to change them, they’re usually thinking about statutes, ordinances, and the kinds of legislation they ask their representatives to pass. Local government gives students, scholars, and lawyers the chance to engage in making law through legislation and administration, rather than only interpreting it. 

    That kind of work is essential for meaningful change and for improving how the country is governed. And I think it’s especially important for law students to gain exposure to how local and state governments operate, not just how the judiciary works in the classroom. 

    Q: You’ve been recognized for teaching excellence. What do you see as the biggest challenge in teaching constitutional law to students who are entering a moment of profound institutional distrust?   

    A: A core responsibility in teaching constitutional law is helping students understand what the law is particularly how the US Supreme Court approaches major issues. For students who may go on to work in Congress or in state legislatures, it’s essential to be able to anticipate how courts will interpret and enforce statutes. Crafting legislation with judicial response in mind is simply part of being an effective practitioner. 

    That means continually updating the syllabus as the Court’s doctrine evolves. But that aspect, while important, is relatively straightforward. 

    Equally if not more important is teaching students to think critically about what the law should be. Here I’m inspired by Charles Hamilton Houston, who taught constitutional law at Harvard in the early 20th century. At a time when child labor laws and minimum wage protections were often struck down, Houston urged his students not to treat the law as fixed, but as something that could be reshaped. 

    His students went on to challenge segregation in Brown v. Board of Education and to help advance landmark civil rights legislation. Their work transformed constitutional law. That’s the model I hope to pass on: students who understand current doctrine, but who also feel empowered to imagine and help build a better legal framework. 

    Q: Much of your work critiques the Court’s historical and structural power. What responsibility do you think legal scholars have in shaping public understanding of constitutional democracy?   

     A: I think the most important thing is helping people understand that the Constitution was written by and for the public. It is an accessible document, and its basic principles can be grasped simply by reading it and reflecting on what its terms mean. 

    That accessibility matters in practice. When Congress or state legislatures act in ways that seem misguided or when constitutional values aren’t being upheld, people should feel empowered to raise constitutional objections. If, for example, government actions appear inconsistent with fundamental rights, individuals can and should question whether those actions align with the Constitution. 

    Ultimately, the Constitution is not self-enforcing, and no single actor has a monopoly on its meaning. Its vitality depends on public engagement. Legal scholars, therefore, have a responsibility to help people see that they, too, play a role in interpreting and demanding fidelity to constitutional principles. 

    Q:  What do you think SSRN contributes to the world of modern research and scholarship?  

    A: What SSRN does best is provide scholars with a free, accessible way to see what others in their field are working on and how ideas are evolving in real time. For legal academics in particular, the traditional publication process can be slow sometimes so slow that the issue an article addresses has already faded from immediate relevance. 

    SSRN helps close that gap. It allows scholars to share work in progress and receive feedback quickly, while the topic is still fresh and actively being debated. That kind of early engagement not only strengthens individual pieces but also deepens the broader scholarly conversation. 

    In that sense, SSRN offers a real public service: it creates a space for collaboration, critique, and intellectual exchange, helping scholars refine their work and learn from one another before formal publication. 

    MORE ABOUT NIKOLAS BOWIE

    Nikolas Bowie’s scholarship explores the deep historical roots of American constitutional law, with a particular focus on how legal doctrines have shaped and constrained democratic governance. His work critically examines the rise of judicial supremacy, the development of separation-of-powers doctrine, and the historical debates surrounding federal authority over immigration and political rights. Across his research, Bowie highlights the contingency of legal institutions, emphasizing that today’s constitutional arrangements are the product of debates, struggles, and choices rather than inevitabilities. His writing has appeared in leading publications such as the Harvard Law Review, Yale Law Journal, Stanford Law Review, Law and History Review, and The New York Times.  

    In addition to his academic work, Bowie is deeply engaged in public life. He serves on multiple nonprofit boards, including the American Association of University Professors, Lawyers for Civil Rights, and the People’s Parity Project, and participates in local governance as a member of the Cambridge planning board. His contributions to legal education have been widely recognized, including the Sacks-Freund Award for Teaching Excellence from Harvard Law School. Through his teaching, scholarship, and public engagement, Bowie continues to advance a vision of constitutional law as a dynamic, participatory project shaped not just by courts, but by the people themselves. 

  • A Closer Look: Instability Risks from Programmable AI Load Ramping in Low‑Inertia Grids by Prof. Aoife Foley and Dr. Dlzar Al Kez 

    A Closer Look:                                                    Instability Risks from Programmable AI Load Ramping in Low‑Inertia Grids by Prof. Aoife Foley and Dr. Dlzar Al Kez 

    The global surge in AI development is transforming not only how we compute, but how our power systems behave. In their 2025 study, Instability Risks from Programmable AI Load Ramping in LowInertia Grids, Dr Dlzar Al Kez and Professor Aoife Foley reveal a challenge hiding in plain sight: AI data centres are no longer passive electricity consumers. They are fast acting, logic‑driven electrical loads capable of reshaping grid dynamics in real time. 

    Drawing on detailed time‑domain simulations of a modified New England test system, the authors show how rapid AI training ramps especially in grids dominated by inverter‑based resources can trigger voltage depressions, RoCoF spikes, and system‑wide instability even without a generator fault. Their work exposes a new class of demand‑driven disturbance and argues that without visibility, coordination, and storage, AI loads could become a critical stressor on tomorrow’s low inertia grids. 

    This “Closer Look” unpacks the paper’s insights, the authors’ motivations, and the implications for utilities, policymakers, and the rapidly expanding AI sector. 

    Q: What motivated you to study AI data centers as a potential risk to power grids?

    A: What motivated this work was a clear gap between how we model demand and how it behaves today. Traditionally, loads have been treated as something passive, slow-moving, and largely predictable.

    But what we’re seeing with AI data centers is very different. These are large, power-electronic loads driven by software, not by system conditions. They can ramp rapidly, and at large scale those ramps can reach system-relevant magnitudes, often without sufficient visibility for system operator.

    Within the Avantern Group at The University of Manchester, Professor Foley and my broader work on resilient net-zero infrastructure and low-inertia power systems made it increasingly clear that this emerging form of demand could not be treated using legacy assumptions.

    At the same time, system inertia is decreasing as solar and wind replace conventional generators. That combination, faster, less predictable demand and lower system resilience, is what really raised the concern. It’s not just a scaling issue; it’s a change in demand itself. This builds on my continuing research on data centers, fast frequency response, and high-IBR power systems.

    Q: In simple terms, what is the main takeaway about how AI workloads could affect grid stability?

    A: The main takeaway is that AI workloads can behave more like disturbances than traditional demand.

    Instead of slowly varying, they can change rapidly and autonomously, which can trigger frequency and voltage deviations even when no conventional fault or generator trip has occurred.

    In low-inertia systems, those fast changes become harder to absorb, meaning instability can emerge purely from how demand behaves, with no generator fault required. That’s a new kind of risk, and one that current grid codes and protection systems weren’t designed with in mind.

    Q: Why is battery storage important in your scenarios, and what does it mean when you say “coordinated” storage across sites?

    A: Battery storage helps by absorbing or smoothing those rapid changes in demand.

    In our scenarios, uncoordinated storage, where each data center acts independently based only on its own conditions, only partially mitigates the problem. But when sites share timing signals or respond to a common grid frequency threshold, the combined effect is much more stabilizing. That alignment is what we mean by coordination: not just having storage, but having it act as part of a coherent system response.

    So it’s not just about having storage, it’s about how that storage behaves as part of a wider system response rather than acting independently.

    Q: How feasible is it today for data centers to share timing or load information with grid operators, and what are the practical challenges?

    A:Technically, it’s feasible. The challenge isn’t really the technology; it’s the framework around it.

    A lot of these facilities operate behind the meter, and their internal processes are driven by commercial and operational priorities. There’s often limited obligation to share real-time information about how workloads behave.

    So the issue is less about capability and more about incentives, standards, and regulatory structures. Until there’s a clear framework requiring or enabling that visibility, sharing will remain voluntary and inconsistent, which is precisely where policy has a role to play.

    Q: If a regulator asked for practical steps, what would be the easiest first actions to reduce the risk you identified?

    A: The first step is improving visibility. If operators can’t see how demand is behaving, they can’t plan or respond effectively. Even basic transparency requirements around ramp rates and operational patterns, potentially through extensions to existing grid codes, would be a significant step forward.

    From there, introducing soft limits on ramp rates, or requiring facilities above a certain size to demonstrate local mitigation capability such as storage or demand controllability, would bring large AI loads closer into alignment with system needs. These don’t require new institutions; they require existing ones to extend their scope.

    Q: What are the main limitations or uncertainties in your study that readers should keep in mind?

    A: Like any modeling study, this is based on representative scenarios rather than exact real-world behavior.

    At the Avantern Group we’ve tried to capture realistic dynamics based on available data and reported events, particularly in how we represent fast-ramping load profiles and system conditions, but actual data center behavior can vary depending on design, workload type, and control strategies, and granular operational data from live facilities remains difficult to access, which itself is a limitation worth noting.

    The results should therefore be seen as indicative of risk and system sensitivity, rather than precise predictions of specific events.

    Q: How generalizable are your findings to other grids, regions, or configurations beyond the specific case you analyzed?

    A: The specific numerical results will vary by system, but the underlying mechanisms are broadly applicable. Any grid with growing inverter-based generation and large, fast-ramping loads will face similar pressures, and that increasingly describes systems across GB, Ireland, parts of Europe, and data center-dense regions globally.

    The severity depends on system strength, inertia levels, and whether these loads are geographically concentrated, as clustering amplifies their local impact on the grid.

    So while our case study is specific, the dynamics it captures are not.

    Q: What roles should different stakeholders (data centers, utilities, policymakers) play in addressing these risks?

    A: Data centers need to recognize that at scale, they are no longer just consumers, they are active participants in system behavior. That comes with a responsibility for transparency and some level of coordination with system operators.

    Utilities and system operators need updated tools to model and monitor these new demand profiles in real time; current frameworks weren’t built with this kind of load in mind.

    And policymakers need to update connection standards and market structures, so these risks are properly accounted for, rather than sitting outside the regulatory perimeter. The message is not that AI data centers cannot be connected, but that they need to be treated as active infrastructure, not ordinary passive demand.

    Q: What future research directions do you see as most valuable to pursue next?

    A: The most immediate gap is real-world load data. High-resolution behavioral data from operating facilities would significantly improve how we model and anticipate these risks, and right now that data is largely inaccessible. This is a core research and innovation direction for the AVANTERN Group: modeling emerging infrastructure risks before they become operational failures.

    Beyond that, a key direction is developing coordination mechanisms that are practical enough to implement, not just theoretically optimal. How do you design something that works within commercial constraints, varying ownership structures, and existing market rules?

    More broadly, both questions point to the same underlying need: rethinking how demand is represented in planning and operational models. That’s a research agenda that extends well beyond AI data centers.

    Q: If you had to summarize the study for a broad audience in one or two sentences, what would you say?

    A: Large AI data centers are changing how electricity demand behaves, making it faster, less predictable, and more dynamic and system active. If we continue to treat that demand as passive, we risk missing a growing source of instability in modern power systems. Our study shows that, unless these fast-ramping loads are made visible, coordinated, and supported by storage or grid-forming capability, they could become a new source of instability in low-carbon power systems.

    Q: What do you think SSRN contributes to the world of modern research and scholarship?

    A: For research like ours, at the Avantern Group, where the policy implications are immediate and the field is moving quickly, SSRN’s value is in closing the gap between when work is done and when it reaches the people who need it. Peer review is important, but it takes time, and decisions about grid codes, data center connections, and energy policy aren’t waiting.

    More broadly, SSRN encourages the kind of cross-disciplinary conversation this problem genuinely requires. It sits at the intersection of power systems, computer science, and policy, and having a platform where researchers across those fields can engage with the work early makes a real difference.

    ABOUT THE AUTHORS

    Professor Aoife Foley

    Professor Aoife Foley is Chair in Net Zero Infrastructure at The University of Manchester, with a joint appointment to the Departments of Electrical and Electronic Engineering and Civil and Engineering Management, and is Chief Executive Officer (CEO) of the AVANTERN Group. Her work focuses on the resilience, operation, financing, and future planning of net-zero infrastructure systems across the energy, transport, telecommunications, and digital sectors, examining how emerging technologies, AI-driven demand, renewable integration, and increasingly complex infrastructure networks interact within rapidly decarbonising societies.

    With more than 30 years of experience spanning engineering, infrastructure delivery, policy, and academia, Professor Foley’s work consistently bridges engineering, policy, and real-world system operation. Before moving into academia full-time in 2011, she worked across major infrastructure, telecommunications, and energy projects in both public and private sectors. She later became Professor in Energy Systems Engineering at Queen’s University Belfast and previously served as Editor-in-Chief of Renewable and Sustainable Energy Reviews (Elsevier). Ranked among the Stanford/Elsevier Top 2% of Scientists globally, her work continues to influence international discussions on resilient infrastructure, future electricity systems, and the wider challenges associated with net-zero energy transitions.

    Dr Dlzar Al Kez 

    Dr Dlzar Al Kez, CEng, MIET, FHEA, is a Research Associate in Net-Zero Infrastructure at The University of Manchester and Chief Technology Officer (CTO) of the AVANTERN Group. His research focuses on the stability and resilience of low-carbon electricity systems with high penetrations of inverter-based resources, battery energy storage, and rapidly changing electrical demand. He works across power system modelling, IBR integration, low-inertia grid behaviour, frequency stability, grid-code compliance, and the operational impacts of emerging digital infrastructure such as AI data centres. Using DIgSILENT PowerFactory, Python, MATLAB, and system-level modelling approaches, he supports technical assessment of grid connection risks, dynamic performance, system strength, and stability challenges in renewable-dominated networks. Dr Al Kez has authored more than 50 peer-reviewed publications and serves as Associate Editor for IET Smart Grid and Smart Grids and Sustainable Energy, alongside roles as Publishing Ethics Advisor and Subject Matter Expert for Elsevier. His work bridges academic research, industry-facing power system studies, and advisory support for utilities, infrastructure developers, regulators, and organisations managing complex grid integration challenges.

  • The Latest Research on Climate Finance 

    The Latest Research on Climate Finance 

    This list includes a selection of the latest research on Climate Finace posted to SSRN in 2026. 

    • Climate Regulatory Exposure and the Stock Market: Evidence from the Trump Elections by Salim Baz (Lebanese American University), Lara Cathcart (Imperial College Business School), Alexander Michaelides (Imperial College Business School; Centre for Economic Policy Research (CEPR)), Liying Wang (University of Liverpool) and Yi Zhang (Hong Kong University of Science and Technology (Guangzhou); Imperial College Business School) 
    • A Term Structure Framework for Green Bond Spreads and Portfolio Strategies by Mohammad Hadi Sehatpour (Finance Department, UTS Business School), Marta Campi (University of Zurich) Christina Sklibosios Nikitopoulos (University of Technology Sydney – Business School; Financial Research Network (FIRN)), Gareth Peters (University of California Santa Barbara; University of California, Santa Barbara) and Kylie-Anne Richards (University of New South Wales (UNSW) – School of Mathematics and Statistics; University of Technology Sydney (UTS) – UTS Business School) 
    • Mandatory Sustainability (ISSB) Reporting: Early Evidence from Türkiye by Amir Amel-Zadeh (University of Oxford – Said Business School), Thomas Bourveau (University of Oxford, Saïd Business School), Furkan M. Cetin (London School of Economics & Political Science (LSE)) and Jeroen Koenraadt (London School of Economics & Political Science (LSE)) 
    • What Firms Actually Lose (and Gain) from Extreme Weather Event Impacts by Tobias Schimanski (University of Zurich), Glen Gostlow (University of Zurich – Department Finance), Malte Toetzke (Technical University Munich; Max Planck Institute for Innovation and Competition) and Markus Leippold (University of Zurich; Swiss Finance Institute)  
    • What Is the Carbon Premium a Premium On? * by Marcin T. Kacperczyk (Imperial College London – Accounting, Finance, and Macroeconomics; Centre for Economic Policy Research (CEPR); European Corporate Governance Institute (ECGI))  
    • Powering AI: How Do Data Centers Affect Renewable Energy Investment? by By Alexander Heiss (Technische Universität München (TUM) – TUM School of Management0, Zacharias Sautner (University of Zurich – Department of Finance; Swiss Finance Institute; European Corporate Governance Institute (ECGI)) and Thomas Schmid (The University of Hong Kong – Faculty of Business and Economics) 

    To read more research on Climate Finance, subscribe to SSRN’s Climate Finance alerts or view other papers here. 

  • The Latest Research on Central Banks

    The Latest Research on Central Banks

    This list includes a selection of the latest research on Central Banks posted to SSRN in 2026. 

    • The Global State of Open Banking and Open Finance Report by Sanya Juneja (University of Cambridge – Cambridge Centre for Alternative Finance), Bill Roberts (University of Cambridge – Cambridge Centre for Alternative Finance), Dana Salman (University of Cambridge – Cambridge Centre for Alternative Finance), Pavle Avramovic (University of Cambridge – Cambridge Centre for Alternative Finance), Alan Ainsworth (Cambridge Centre for Alternative Finance (CCAF)) and Bryan Zheng Zhang Cambridge Centre for Alternative Finance, Cambridge Judge Business School, University of Cambridge; University of Cambridge – Cambridge Centre for Alternative Finance) 
    • The Moneyness of Stablecoins by Christopher K. Odinet (Texas A&M University School of Law), Andrea Tosato (Southern Methodist University – Dedman School of Law) and Yesha Yadav (Vanderbilt University – Law School; European Corporate Governance Institute (ECGI)) 
    • Bank Governance: Lessons Still Not Learned by Marco Becht (Solvay Brussels School of Economics and Management (ULB); European Corporate Governance Institute (ECGI); Centre for Economic Policy Research (CEPR)), Patrick Bolton (Imperial College London; Centre for Economic Policy Research (CEPR); National Bureau of Economic Research (NBER); European Corporate Governance Institute (ECGI)) and Ailsa Roell (Imperial College Business School; European Corporate Governance Institute (ECGI)) 
    • Climate Change, Bank Liquidity and Systemic Risk by Margherita Giuzio (European Central Bank (ECB)), Bige Kahraman (University of Oxford – Said Business School; Centre for Economic Policy Research (CEPR)) and Jasper Knyphausen (University of Oxford – Said Business School)  
    • Bank Runs With and Without Bank Failure by  Sergio Correia (Board of Governors of the Federal Reserve System), Stephan Luck (Federal Reserve Bank of New York) and Emil Verner (Massachusetts Institute of Technology (MIT) – Sloan School of Management; National Bureau of Economic Research (NBER)  

    Discover more research on Central Banks in SSRN’s Banking & Insurance alerts here. 

  • Top Papers on AI in Law Q1 2026 

    Top Papers on AI in Law Q1 2026 

    This list includes the top downloaded papers on AI in Law posted in Q1 2026.  

    • The Artificial in “Artificial Intelligence”: How Imagination Shapes AI Regulation by Claudio Novelli (Yale University – Digital Ethics Center), Luciano Floridi (Yale University – Digital Ethics Center; University of Bologna- Department of Legal Studies), Stefan Larsson (Lund University – Department of Technology and Society) Mariarosaria Taddeo (University of Oxford – Oxford Internet Institute) and Steven L. Winter (Wayne State University Law School)  
    • Code Is Not Law by Carla Reyes (Southern Methodist University – Dedman School of Law), Andrea Tosato (Southern Methodist University – Dedman School of Law) and Andrew Hinkes (New York University School of Law) 
    • Legal Alignment for Safe and Ethical AI by Noam Kolt (Hebrew University of Jerusalem), Nicholas Caputo (Oxford Martin School), Jack Boeglin (University of Pennsylvania Law School), Cullen O’Keefe (Institute for Law & AI; Centre for the Governance of AI), Rishi Bommasani (Stanford University), Stephen Casper (Massachusetts Institute of Technology (MIT) ), Mariano-Florentino Cuéllar ( Carnegie Endowment for International Peace; Stanford Law School), Noah Feldman (Harvard Law School), Iason Gabriel (School of Advanced Study University of London), Gillian K. Hadfield (University of Toronto; Vector Institute for Artificial Intelligence; OpenAI; Center for Human-Compatible AI), Lewis Hammond (University of Oxford), Peter Henderson (Princeton University – Program in Law & Public Policy), Atoosa Kasirzadeh (Carnegie Mellon University), Seth Lazar (Australian National University (ANU)), Anka Reuel (Stanford University), Kevin Wei (RAND Corporation; Harvard University – Harvard Law School), Jonathan L. Zittrain (Harvard Law School; Harvard School of Engineering and Applied Sciences; Harvard University – Harvard Kennedy School (HKS); Harvard University – Berkman Klein Center for Internet & Society)  
    • Questioning the Digital Markets Act’s Legality by Thibault Schrepel (Vrije Universiteit Amsterdam; Stanford University’s Codex Center) and Godefroy de Boiscuille (University of Côte d’Azur; Paris-Panthéon-Assas University & CRED) 
    • Liberal AI by Cass R. Sunstein (Harvard Law School; Harvard University – Harvard Kennedy School (HKS)) 

    To read more about AI in Law, subscribe to SSRN’s Artificial Intelligence – Law, Policy, & Ethics Research Updates or view other papers here.  

  • The Latest Research on Cryptocurrency 

    The Latest Research on Cryptocurrency 

    This list includes a selection of the latest research on cryptocurrency posted to SSRN in 2026. 

    Cryptoasset Ecosystem in Latin America and the Caribbean by Roman Proskalovich (University of Cambridge, Judge Business School, Cambridge Centre for Alternative Finance), Christopher Jack (University of Cambridge – Cambridge Centre for Alternative Finance), Alex Zarifis (University of Cambridge – Cambridge Centre for Alternative Finance), Diego Montes Serralde (University of Cambridge – Cambridge Centre for Alternative Finance) and Damaris Njoki (University of Cambridge, Judge Business School, Cambridge Centre for Alternative Finance) 

    When Markets Never Sleep:  Intraday Liquidity Patterns and Volatility Effects in Cryptocurrency Trading by Aleksander R. Mercik (Wroclaw University of Economics and Business), Barbara Bedowska-Sojka (Poznań University of Economics and Business) 

    OmniFormer: A Patch Transformer for Joint Long-Term Multi-Dimensional Cryptocurrency Time Series Forecasting by Trung Nam Nguyen (Ho Chi Minh City University of Economics and Finance), Nguyen Quoc Anh (Hitachi Digital Services), Son Ha (RMIT University), Phien N. Nguyen (Ton Duc Thang University), Trung Phan Hoang Tuan (FPT University), Anh N. Le (FPT University) and Nguyen Gia Chan (FPT University) 

    Code Is Not Law by Carla Reyes (Southern Methodist University – Dedman School of Law) Andrea Tosato, (Southern Methodist University – Dedman School of Law) and Andrew Hinkes (New York University School of Law) 

    Tokenized Gold by Campbell R. Harvey (Duke University – Fuqua School of Business; National Bureau of Economic Research (NBER)), Chen Lin (The University of Hong Kong – Faculty of Business and Economics) Daniel Rabetti (National University of Singapore (NUS); Harvard Business School) and Che Zhang (Tsinghua University) 

    The Moneyness of Stablecoins by Christopher K. Odinet (Texas A&M University School of Law), Andrea Tosato (Southern Methodist University – Dedman School of Law) and Yesha Yadav (Vanderbilt University – Law School; European Corporate Governance Institute (ECGI) ) 

    Pairs Trading in Crypto by Sasha Stoikov (Cornell Financial Engineering Manhattan), Dora Xu (Cornell University – Cornell Financial Engineering Manhattan), Shijie Shao (Cornell University), Yourui Wang (Cornell University) Tongshu Zhang (Cornell University) and Jinxuan Hu (Cornell University)  

    Stablecoins and Banking: Deposit Dynamics, Financial Stability, and Regulatory Design by Lin William Cong (Nanyang Technological University; Cornell University)  

    Regulating Decentralized Stablecoins: Comparing MiCAR and the GENIUS Act by Christopher K. Odinet (Texas A&M University School of Law) and Andrea Tosato (Southern Methodist University – Dedman School of Law) 

    The Contest Between Central Bank Digital Currencies, Stablecoins and Tokenised Deposits: Which Will Likely Win, and Why?  by Ross P. Buckley (University of New South Wales (UNSW) – UNSW Law & Justice) 

    Discover more research on cryptocurrency in SSRN’s Cryptocurrency Research Updates here

  • SSRN Strategic Update: Renewed Focus on Core Research Sharing Mission 

    SSRN Strategic Update: Renewed Focus on Core Research Sharing Mission 

    At SSRN, our mission is to rapidly share preprints and other early-stage research, empowering global scholars to help shape a better future. Today we are announcing an important change that reflects where we believe we can make the greatest contribution to that mission. 

    We have decided to focus entirely on SSRN’s core function as a free, world-class preprint platform. As a result, we will be closing our commercial products (Research Paper Series, Sponsored Networks and Site Subscriptions, paid Conference Proceedings, Data Analytics Dashboards, Partners in Publishing, Jobs and Announcements, and Data Feeds) by the end of December 2026. 

    This is not a decision we have taken lightly. These products have supported thousands of researchers and institutions over many years, and we are extremely grateful to all our institutional partners. However, running a commercial operation alongside a free research platform has required very tough trade-offs in technology investment, operational focus, and our ability to keep pace with what researchers actually need from a preprint server in 2026. Stepping back from commercial products will allow us to us put everything into what SSRN does best. 

    We hope that in the future this will mean  free Research Alert subscriptions, faster posting times, improved CrossRef metadata, stronger transparency through versioning, ORCID integration, and clear links between preprints and published versions. SSRN will remain publisher-neutral and committed to serving researchers across all disciplines. 

    What this means for existing customers 

    Every existing contract will be honored in full through its term or until December 31, 2026, whichever comes first. Our team will be in touch with each customer directly to talk through the timeline, answer questions, and plan the transition, including any applicable refunds. We will not be onboarding new customers for these commercial products, and automatic renewals will not proceed. 

    If you have questions about your contract, data, or transition planning in the meantime, please reach us at ideas@ssrn.com

    Looking ahead 

    SSRN has been part of the research ecosystem for over 25 years. This change is about making sure it will survive and thrive for the next 25. We’d like to thank all our commercial partners for their support for SSRN over the last two decades: we really wouldn’t be here without you. However, we now look forward to building an SSRN that is completely focused on the needs of researchers around the world. As always, we’d love to hear your thoughts at ideas@ssrn.com

    FAQ: 

    Why are SSRN’s commercial services being discontinued, and why now? 

    The preprint landscape has changed significantly. Expectations around posting speed, licensing, metadata transparency, and discoverability have all increased, and SSRN has had to make difficult trade-offs to maintain commercial products alongside its free platform. Stepping away from our commercial products will allow us to simplify our model and prioritize investing properly in the things that researchers tell us matter the most to them. The timing reflects both the strategic direction set by our parent organization and a genuine belief that now is the right moment to make this shift. 

    When will the process be complete? 

    We will complete the transition by the end of December 2026. 

    Will SSRN continue to operate after the transition? 

    Yes. SSRN will continue as a free, world-class preprint platform. Sunsetting our commercial products is about sharpening our focus, not shrinking our ambition. We intend to strengthen SSRN’s platform with faster posting, better licensing options, improved discoverability, and higher research integrity standards. We hope that many of our commercial customers will transition and make full use of our ongoing free services.  

    Which services are being discontinued? 

    Research Paper Series (RPS), Sponsored Networks and Site Subscriptions, paid Conference Proceedings, Data Analytics Dashboards, Partners in Publishing, Jobs and Announcements, and Data Feeds will all close by the end of December 2026. All existing content will remain permanently archived and accessible. 

    What happens to my existing contract? 

    All existing contracts will be honored through their term or until December 31, 2026, whichever comes first. A SSRN manager will be in touch to confirm the details for your specific arrangement and to work through the transition with you. 

  • Meet the Author: Alicia Solow-Niederman

    Meet the Author: Alicia Solow-Niederman

    Alicia Solow‑Niederman is a leading scholar at the forefront of algorithmic accountability, data governance, and information privacy. As an Associate Professor at George Washington University Law School, she examines how emerging technologies, especially artificial intelligence, expose the limits of existing legal frameworks and reveal deeper questions about power, governance, and the values embedded in our regulatory systems. In this interview, Alicia discusses the challenges of governing AI across overlapping legal regimes, the politics of technical standards, and the evolving role of courts, agencies, and private actors in shaping the digital landscape. Her insights illuminate the tensions among privacy, transparency, and innovation, offering a nuanced view of how law can adapt to technological change while remaining grounded in democratic principles.

    Q: Your recent work discusses the concept of “inter-regime doctrinal collapse” in data governance. Could you explain what this phenomenon entails and its implications for AI regulation?

    A: Broadly speaking, my article explores how AI is challenging existing legal frameworks. Not in a literal sense, more fundamentally, by revealing that the doctrines and structures governing AI are not operating in clear, consistent, or principled ways. This has significant political, economic, and rule-of-law implications.

    To make this concrete, I focus on data, especially data acquisition, since AI systems today require vast amounts of data to function effectively. How we regulate data directly impacts AI regulation. Because multiple legal fields like copyright law and privacy law apply to data, the regulatory landscape becomes complex. These fields have different rules and underlying goals. When the boundaries between copyright and privacy law blur, and their rules and rationales no longer remain distinct, the legal regimes can start to lose their structural integrity and effectively collapse into one another. I refer to this phenomenon as inter-regime doctrinal collapse.

    A key point about the term: the word “collapse” might sound alarming, like a bridge falling down. But in this context, I use it descriptively. Whether this collapse is ultimately beneficial or harmful depends on what it enables, who gains power from it, and the broader political and legal consequences. So, it’s a phenomenon worth understanding.  Moreover, it matters for AI regulation because data is a key input to develop and deploy AI systems, and we’re seeing doctrinal collapse with two regimes that govern data—information privacy law and copyright law.  

    Because I’m a big believer in showing, not just telling, I want to offer a concrete illustration that connects this point to AI regulation. Suppose a company scrapes data from the internet to train an AI model. Initially, the company claims that the data it uses is public and therefore not subject to privacy restrictions, because users voluntarily shared it. Simultaneously, it asserts that it’s not liable for copyright infringement because the data was publicly available. The term “publicly available” isn’t a legal term of art, but it appears frequently in AI disputes, litigation briefs, and public rhetoric. At the same time, the company refuses to disclose its training data, citing confidentiality and proprietary interests.  Subsequently, the same company argues during litigation that user privacy requires non-disclosure despite previously denying privacy concerns because the data was shared voluntarily with a third party.

    This example highlights how legal boundaries become fuzzy, even though copyright law and privacy law have very different doctrines and normative goals. In practice, what’s considered public versus proprietary, in copyright law, and public versus personal, in privacy law, become blurred as companies toggle between them.  It becomes extremely difficult to determine which legal doctrine applies at any given moment. This is what I refer to as doctrinal collapse on the ground: the boundaries between privacy law and copyright law become indistinct, and the legal system’s structure begins to weaken. That connects back to AI regulation because the choices we are making about privacy law and copyright law regulate data—and because data acquisition is required for AI development, these choices about data regulation will affect AI governance. 

    Q : As you’ve just explained, your research suggests that the legal regimes governing data, privacy law, and copyright law are becoming increasingly blurred. What challenges does this pose for effective regulation of AI systems, and what institutional responses do you propose?

    A: Here, I want to sharpen the political economy and rule of law stakes, and then consider some potential institutional responses. 

    First, collapse enables companies to manipulate the legal and social meanings of “public.”  Not all companies are equally well-positioned to exploit the domains.  Collapse tends to favor the “haves” – the well-resourced incumbents – by allowing them to acquire data at the expense of ordinary people or less well-resourced actors.  In some cases, well-resourced incumbents have the money and influence to execute and leverage licensing agreements.  In other cases, dominant platform firms are best-positioned to rely on broad user consent through privacy policies and terms of service.  

    Second, from a rule of law perspective, this fluidity allows private actors to switch between conflicting claims depending on what serves their interests, undermining legal predictability, coherence, and legitimacy. When laws become ambiguous and actors can switch between different legal regimes, it erodes public trust and the legitimacy of the legal system itself. This toggling creates a situation where the law no longer functions as a clear, predictable framework for accountability and justice, especially in the rapidly evolving context of AI.

    Now, what might we do about this? I don’t believe that collapse itself is a problem we can solve. We can’t, and shouldn’t, try to create perfect clarity in the law or impose  artificial boundaries between different fields of law. Instead, we should recognize that collapse becomes a problem when it undermines the law’s ability to govern effectively.

    In the paper, I suggest some institutional responses. Some are incremental, focusing on adapting the current legal framework. In particular, we might draw on conflict of laws and empower courts as managers of collapse. For example, if a court is resolving a dispute where parties raise both privacy and copyright claims, the judge might insist on a rebuttable anti-switching presumption, saying, “You can’t assert mutually incompatible claims at different points in the lawsuit unless you provide a compelling reason to justify the switch.” These are strategies to manage the collapse without overhauling the entire legal system.

    Other responses are more reformist, aiming to change the legal structure itself and make it harder to manipulate the lines between domains.  Notably, we might adopt stronger privacy laws that make the initial relationship between copyright and privacy law less asymmetrical. I believe that reducing or eliminating the underlying weaknesses that lower the legal and social costs of privacy violations compared to copyright would decrease incentives for companies to exploit privacy loopholes and avoid copyright obligations.

    Q: Let’s turn next to some of your other projects. In your paper on AI standards, you argue that standards are not neutral but have embedded politics. How can policymakers and regulators ensure that AI standards promote fairness and accountability rather than reinforce existing power structures?

    A: Standards are tricky regulatory devices. One problem with standards, as I discuss in the paper, is that they tend to work much better in purely technical settings, like whether an outlet must have two or three prongs. But when we start dealing with socio-technical contested issues, such as fairness, accountability, or transparency, standard setting becomes much more difficult. I begin there because I think it’s important to be honest about that from the start. We can talk about how to improve standard setting, but standards are always political artifacts, in Langdon Winner’s sense of politics. They will inevitably reflect the power structures that create them.

    For AI standards, one key step is to carefully consider whether a particular issue should be addressed through standard setting at all, or if it should be handled via public legislation or more binding legal procedures. If we decide that standard setting is appropriate, then we need to think about the relative influence of public versus private actors.

    Another important aspect is ensuring ongoing deliberation and rethinking over time. I believe standards can sometimes be better than hard law because they can adapt more quickly and be nimbler. Building space for contestation and re-contestation is crucial. Without that, I worry that a powerful private actor could entrench a standard through market dominance, locking it in without democratic oversight or opportunities for re-evaluation. These chances to rethink things are vital, especially if what fairness means turns out to harm certain populations more than others, or if the initial requirements for transparency aren’t sufficient for outside parties to contest decisions made with the AI system, or if other problems with the standard emerge.

    Q: Your work often emphasizes the importance of understanding the political and social context of technological standards. How can stakeholders ensure that AI governance frameworks are inclusive and reflect diverse societal values?

    A: If I had a one-shot answer, I’d be selling it to the highest bidder, there’s no silver bullet. But I do think recognizing that technology is not a shiny, isolated object is a crucial first step. These tools are shaping our democracy and social future, and we must see that.

    Technology is not neutral. Design choices, what data to use, how to define goals, how to align AI systems, or even whether to use AI at all, are deliberate decisions with real outcomes. The structure of our legal system also reflects choices; for example, how we regulate privacy or how lightly we regulate tech companies are policy decisions that shape society. My hope, and what I aim to help others see, is that these are choices and recognizing that empowers us. It means we’re not stuck; even without a perfect, one-size-fits-all solution, understanding these choices allows us to advocate for more inclusive and equitable governance.

    Q: Your research also speaks to other aspects of AI regulation, such as  the role of judicial decisions in shaping AI governance. How do you see the courts influencing the development of AI law, and what are the risks and benefits of relying on litigation as a form of regulation?

    A: I see courts affecting AI law in many ways. It’s happening quite a lot. Sometimes, it’s direct, like the copyright lawsuits I discuss in AI and Doctrinal Collapse. Other times, it’s less direct, such as a First Amendment case that influences what policymakers believe is possible for AI regulation. Additionally, a company’s decision to settle a case rather than litigate can shape the regulatory landscape, depending on the outcome.

    Whether this is good or bad is the key question. That’s why my piece is titled, “Do Cases Generate Bad Law?,” with a question mark. It’s a real question. Cases involve adversarial parties and concrete issues, where a harm that has already occurred. This can be beneficial because it helps focus attention on specific legal issues and concerns. Judges are often well-positioned to uncover detailed facts and understand how AI companies operate, which can be valuable.

    However, whether courts make good or bad AI law depends a great deal on their interaction with legislators and regulators. AI cases are more likely to produce beneficial outcomes if they prompt legislative action to fill regulatory gaps and provide remedies for harms.

    For example, there’s a recent lawsuit alleging a violation of Illinois’ Biometric Information Protection Act (BIPA). To establish a violation, there must be collection of biometric data, like faceprints or voiceprints, without prior informed consent. In this case, the complaint alleges that an AI company collected voiceprints to develop its system. If the case proceeds to discovery, it could reveal how these systems work, informing other plaintiffs, the public, and policymakers. It might lead Illinois legislators to update the law, if it turns out that they wanted to cover the data at stake and it is not covered. Or, if the voiceprints are covered by BIPA, the disclosures about how biometric information is used to create AI systems might inspire similar legislation elsewhere.

    That said, there are risks to case-made AI law. Relying on litigation can mean that less tangible or emergent harms go unrecognized, either because there’s no legal cause of action or because the harm isn’t understood as such. It can also lead to spurious or costly litigation, which is especially problematic for startups.

    Another concern is the concentration of cases in a few jurisdictions, which can result in a limited number of courts deciding complex social issues with nationwide implications. This lack of diversity in outcomes and limited access for plaintiffs is troubling. Therefore, I see litigation as an access-to-justice issue for individuals who may be left without a remedy for the negative impacts of AI systems. That is also why we need both litigation and legislation. It’s essential for functioning legislatures at both the state and federal levels to recognize how AI systems are affecting people, to realize the issues that cases are not likely to address, and to take action on these vital issues.

    Q: Your research covers both AI law and information privacy law. In your view, how can the concept of the “Overton Window” be applied to improve the enforcement of privacy protections in the rapidly evolving digital landscape? 

    A: I’m not sure the Overton Window directly improves privacy enforcement. Instead, it helps reveal the range of actions that regulators believe are feasible at any given moment. What agencies do depend on internal norms, political will, resources, and external pressures from courts, industry, and social movements.

    Applied to privacy, this means that privacy‑minded regulators can and should use broad legal authorities, like unfair and deceptive trade practices laws, to address emerging privacy and AI harms. They’re most likely to act where social consensus is already strong, such as with location tracking or children’s privacy. But the scope of enforcement ultimately turns on politics. Some FTC administrations have embraced a more expansive view of “unfairness,” enabling more aggressive interventions; others have taken a narrower approach.

    Two additional points matter. First, enforcement requires resources. Privacy investigations are complex and time‑intensive. Without adequate funding and staffing, even strong legal tools won’t translate into meaningful action. Second, institutional design is crucial. When lawmakers create new privacy or AI rules, they need to consider whether agencies are insulated from political pressure and whether they have the capacity to act. Otherwise, even well‑intentioned laws risk being symbolic rather than effective.

    Q: The broader question of how to regulate emerging technologies runs through your work. Given your expertise in algorithmic accountability and data governance, what are some practical steps that governments or organizations can take to enhance transparency and fairness in AI systems?

    A: Much of my work is about reframing the problem rather than offering a single, crisp solution. It’s crucial to identify the assumptions underlying policymakers’ proposed paths forward. For example, governments or organizations should ask: What am I assuming about the law and the values I want to uphold? What about human actions, both by developers and end users? What assumptions are being made about the technology itself? By posing these threshold questions, we can reveal our underlying assumptions and develop interventions that better address complex human and technical interactions in specific contexts.

    In addition, we should not latch onto just transparency, or just fairness, as the key to AI regulation.  Although transparency and fairness are vital, focusing solely on those concepts can be limiting. Ultimately, the core issue is power, who controls the means to produce, refine, and deploy AI systems, and who has the voice to influence their governance. Overemphasizing just one aspect risks missing the relational dynamics at play. Recognizing these power relations is essential for meaningful accountability and equitable governance.

    Q: As a member of the EPIC Advisory Board, a faculty affiliate at Harvard’s Berkman Klein Center, and an affiliated fellow at Yale Law School’s Information Society Project, how do collaborations across academia, civil society, and government influence the development of effective AI regulation?

    A: The intersection of public and private sectors is vital for AI governance and my scholarship. First, collaborative governance, meaning active engagement between public and private actors is valuable in theory, but in practice, it carries risks. Without a strong, well-funded, and influential state, private actors can overshadow public voices, effectively replacing regulation with private interests. I believe that public regulation and democratic accountability are essential. They help ensure that AI development aligns with societal values, and contrary to some fears, regulation can foster innovation by guiding technology in lawful and ethical directions.

    Second, the traditional public-private divide is increasingly strained by how social and governmental uses of AI are evolving. For example, in a forthcoming essay, called Clickwrap Accountability, I discuss how government agencies are using generative AI chatbots to provide non-binding advice on matters such as the Supplemental Nutrition Assistance Program (SNAP).   This guidance often replaces in-person visits, government pamphlets, or static FAQ pages about benefits programs. These chatbot interactions are often mediated through private tools and rely on third-party terms of service, with limited accountability. When a chatbot provides advice, and there’s no formal government decision or official process involved, it doesn’t fall neatly within existing procedural due process frameworks. The current legal doctrine offers limited redress.  At best, there are contract disclaimers or clickwrap style “agree” buttons for the end user.  And because the state has sovereign immunity, the forms of contract or tort law redress that might be available in private law are not generally available in this public law context.

    This example highlights a broader trend: the blurring of lines between public and private as AI tools become embedded in public services. Developments like this challenge our existing legal and regulatory frameworks and underscore the need for scholarship and policymaking to adapt to these new realities. We must rethink how accountability, transparency, and oversight are structured when government functions are mediated through private AI systems, and I’m working on these questions in several future projects.

    Q: Looking ahead, what do you see as the most pressing legal or regulatory challenges in AI governance, and how should scholars and policymakers prepare to address them? 

    A: I see two major sets of challenges in AI governance. First, the friction between different legal regimes and values. AI systems sit at the crossroads of copyright, privacy, discrimination, consumer protection, and more. These areas don’t always align, and the trade-offs like balancing privacy with goals such as reducing bias or increasing transparency rarely have clean answers. Scholars can help by stepping outside disciplinary silos and examining how their preferred doctrines interact with others. Policymakers, meanwhile, should resist the urge for simple narratives. Effective regulation starts with mapping which bodies of law are implicated, where the gaps are, and what values are being traded off. There’s rarely a perfect solution, but there can be a principled one.

    Second, we’re dealing with both known unknowns and unknown unknowns. Technological shifts like changes in data needs or the limits of so-called “scaling laws” could reshape incentives and alter how existing laws function. And then there’s the Collingridge dilemma: intervene too early, and policymakers won’t have the information they need, or too late, and harmful practices will already be entrenched. I tend to favor precaution where human interests are at stake, but with humility, flexibility, and mechanisms for revision as evidence evolves.

    A final challenge is institutional capacity. Technology doesn’t inherently outpace law, yet knowing when to adapt and ensuring that agencies have the expertise to understand it  is extraordinarily difficult. Robust governance will depend as much on institutional design as on the substance of any particular rule.

    Q: What are some of the most exciting upcoming projects or research initiatives you are currently involved in or looking forward to?

    A: I have several writing projects and initiatives that I’m looking forward to.  First, I’m very excited about my forthcoming Clickwrap Accountability piece, as well as a forthcoming essay called The Supply Chain as a Circle: AI, Privacy, and People. These projects both focus on the users of technology and what existing law says or doesn’t say about these interactions. In the Supply Chain piece, I argue that the AI supply chain often overlooks the interactions between users and generative AI systems. If we don’t account for this interaction, then harm and responsibility tend to fall on end users, who often lack the knowledge or ability to prevent bad outcomes. They should be part of the regulatory calculus. In addition, I am developing a few other pieces, including one that examines relationships between administrative agencies and platform companies.  All of these projects reflect my conviction that technological developments expose weaknesses in legal and regulatory frameworks and offer opportunities to re-examine institutions and doctrines.

    Beyond scholarship, I’m engaging in conversations with EPIC about regulating AI chatbots and companion AI. I’m also involved in a Uniform Law Commission project on mental privacy, neural data, and cognitive biometrics, exploring potential model state legislation. This is a vital area that links privacy and AI.  Think of the potential health benefits, like restoring hearing—but also the privacy risks of accessing our brains and most personal data.

    Finally, I’m developing a new seminar called “Frontiers and Flashpoints in Tech Law,” which will cover cutting-edge issues like agentic AI, neural tech, and robotics. It aims to help students understand both the technology and the legal challenges. I am really excited for this course, and for all that undoubtedly lies ahead in tech law in the year to come.

    Q: What do you think SSRN contributes to the world of modern research and scholarship?

    A: I appreciate SSRN as a centralized place to find recent research. With so much information available online, it can be difficult to know where to look, so it’s incredibly helpful to have an open‑source community that makes research accessible and provides a clear, reliable destination for new work.

    In addition, as a junior scholar, I’m especially grateful for SSRN as a research platform and as a way for my work to reach other scholars. I see it as a privilege to participate in these scholarly conversations, and I’m thankful for the infrastructure that makes it possible to sustain and expand them.

    MORE ABOUT ALICIA SOLOW‑NIEDERMAN

    Alicia’s scholarship explores how digital technologies disrupt traditional legal categories and institutional assumptions. Her influential work on inter‑regime doctrinal collapse shows how AI blurs the boundaries between privacy, copyright, and other legal domains, creating both regulatory challenges and opportunities for reform. She has written extensively on the politics of AI standards, the role of courts in shaping AI governance, the ways that inferences challenge privacy law on the books, and the need for institutional designs that can withstand uncertainty and technological evolution.  

    Her research has appeared or is forthcoming in leading journals, including the Stanford Law Review, Northwestern University Law Review, Harvard Journal of Law & Technology, Journal of Law & Innovation, and Southern California Law Review. A graduate of Harvard Law School, where she served as Forum Editor of the Harvard Law Review, Alicia has held fellowships at UCLA Law’s PULSE program and Harvard Law School, clerked on the U.S. District Court for the District of Columbia, and worked at the Berkman Klein Center for Internet & Society. She also serves on the EPIC Advisory Board and is a faculty affiliate at the Berkman Klein Center as well as an affiliated fellow at the Yale Law School Information Society Project, where she contributes to cutting‑edge conversations on AI regulation, mental privacy, and the future of public‑private governance.

  • The Latest Research on Medical Law

    The Latest Research on Medical Law

    This list includes a selection of the latest research on medical law posted to SSRN in 2025-2026.

  • SSRN’s New Advanced Search Makes Finding Papers Easier and Faster

    SSRN’s New Advanced Search Makes Finding Papers Easier and Faster

    Michael Parsons

    SSRN’s search has been sorely in need of some love for a while, so we’re very happy to share that we’ve recently been able to make some improvements to how it works. Previously, if you wanted papers about corporate governance but not banking, you couldn’t say so. If your search terms were slightly off, you may have struggled to find what you’re for. We’ve now added two new search modes to the Advanced Search page: Fuzzy Search and Boolean Search. They solve different problems, and you can choose between them depending on what you need.

    Fuzzy Search: A Broader Net

    Fuzzy Search is more forgiving than a traditional keyword search. It will tolerate slight variations in your search terms, typos, and near-matches, returning results that a strict search might miss. You’ll typically see a larger set of results, which makes it useful for exploratory searches, when you’re not yet sure of the precise terminology a field uses.

    It works across all three search field options (Title Only, Title Abstract & Keywords, and Title Abstract Keywords & Full Text) and applies to the Author(s) field as well. If your query is in the right neighbourhood, fuzzy matching will try to get you there. A word of calibration: fuzzy search broadens your results, but it isn’t a spell-checker. It works best when your terms are close to the target. The further off your query, the noisier the results

    Boolean Search: A Precise Tool

    Boolean Search lets you build structured queries using standard logical operators:

    AND requires both terms to appear. "corporate governance" AND disclosure returns only papers addressing both topics.

    OR broadens to either term. "machine learning" OR "deep learning" captures papers using either framing.

    NOT excludes a term. cryptocurrency NOT bitcoin finds crypto papers that aren’t specifically about Bitcoin.

    Parentheses let you group expressions. (fintech OR regtech) AND regulation finds regulation papers that mention either fintech or regtech, without running two separate searches.

    As with Fuzzy Search, Boolean mode works with all three advanced search options. Narrow your scope to Title Only for precise results; expand to Full Text to surface papers where the terms appear anywhere in the document.

    Two Modes, One Search

    These are separate modes, not features you stack. You choose one via the radio buttons on the Advanced Search page. The right choice depends on where you are in your research.

    Use Fuzzy Search when you’re exploring a new area, working from partial memory, or casting around for how a topic is discussed in the literature. It’s the mode for early-stage discovery, when missing a relevant paper is worse than wading through some noise.

    Use Boolean Search when you know what you’re after and want to carve out a specific slice of the literature. It’s the mode for systematic reviews, targeted citation searches, and any query where you need to include or exclude particular terms with confidence.

    Both modes share the same controls: scope selection, the Author(s) field, date filters, and sort options. The only difference is how the search engine reads your query.

    A Two-Step Workflow for Search

    You can now think in terms of a two-step workflow for Search on SSRN. Start in Fuzzy Search with a broad query. Scan the first page of results to pick up the vocabulary the literature actually uses. Then switch to Boolean Search and build a precise query with those terms and operators. The two modes complement each other when used in sequence.

    In Boolean mode, wrap multi-word phrases in quotes: "corporate governance" behaves differently from the two words searched separately. And remember that the Author(s) field is independent of the main search box. If you want a specific person’s work on a specific topic, use both fields rather than cramming everything into one query.

    These changes are live now on papers.ssrn.com, and we really hope you find them useful. Send us your feedback at ideas@ssrn.com, we’d love to hear from you.