Taylor R. Dalton (Santa Clara U Law) has posted “A Problem-Solving Approach to International AI Governance” on SSRN. Here is the abstract:

Should international law be used to govern the development and use of artificial intelligence (AI) technologies? This is a crucial question as developments in AI have been speeding forward in many countries around the world. Yet, in parallel to the excitement around the developments of AI, concerns have arisen about the impact these tools will have on everything from high school homework to nuclear war. Beyond national regulation, many have called for the international community to come together to craft governance rules and norms that will mitigate some of the harmful effects of AI in the future.

Although international governance of this technology may be desirable, it is less apparent whether international institutions (like treaties) could or should be established to regulate something as potentially ubiquitous as AI. This essay analogizes the development of new and anticipated AI technologies to past technological developments to present various categories of cooperation or coordination problems related to those technologies. Invoking theories of rational institutional design, the essay first focuses on what type of problems the international system could or should solve in relation to AI. It then advocates for channeling efforts into more targeted international regimes that address discrete problems, using the problems that may arise in the area of armed conflict as a suitable example. AI’s impact on armed conflict is ripe for public regulation at the international level, especially in the context of autonomous weapons systems. The approach presented here, elevates tested strategies for solving cooperation problems that have succeeded in the past. Additionally, focusing on specific cooperation problems created by AI risks may narrow the bargaining space between countries and allow for agreement on the most feasible and meaningful tasks in the near term. These insights bridge our understanding of international relations and law to provide a framework for a feasible path forward.

Saahir Vazirani (The Perrin Research Institution) has posted “Autonomy in Conflict: Legal, Technical, and Strategic Challenges of AI-Enabled Autonomous Weapon Systems Under International Humanitarian Law” on SSRN. Here is the abstract:

War has historically acted as a catalyst for new technologies, driving advancements that have transformed society. From the creation of nuclear weapons to the establishment of cyber warfare teams, the pressures of war have expedited technological development while also forcing often controversial ethical discussions. Today, artificial intelligence (AI) stands at the pinnacle of technology with the potential and already seen capability to change military strategy. Due to its rapid pace and inherent complexities, states have not come to a consensus on its usage. As such, there is no international regulatory framework to address the concerns around the use of AI in the context of warfare. Furthermore, existing international laws do not specifically address autonomous weapons systems (AWS), and there is a lack of consensus on how to adapt existing legal frameworks to the digital realm of AI. This paper argues that ensuring legal compliance for AI-driven AWS during conflict escalation requires a formal international framework grounded in International Humanitarian Law (IHL) principles and informed by existing state policies. In doing so, it seeks to bridge the accountability gap that arises when machines, rather than humans, carry out hostilities. Using doctrinal analysis, technical evaluations, and geopolitical assessments, it argues that enforceable standards are essential to prevent ungoverned proliferation. My conclusions emphasize the necessity of integrating legal, technical, and political safeguards to align AWS use with humanitarian values. If implemented, this framework will pave the way for the ethical and humane usage of AI within future conflicts.

Michael Chatzipanagiotis (U Cyprus) has posted “Incident Reporting and Investigation under the AI Act: Some Insights from Aviation” (International Journal Of Law And Information Technology, forthcoming) on SSRN. Here is the abstract:

This article examines the provisions of the EU AI Act (AIA) on incident reporting and investigation and explores how the regulatory context of the AIA could benefit from the experience of aviation. Accounting for the differences between the AI and the aviation industries, it is concluded that the establishment of a strong safety culture is a key element, while a series of interventions could significantly improve the current AIA system on incident reporting: (a) clarification of the reporting duties, including the development of a common taxonomy of risk or harm, alongside the expansion of the duty’s personal scope; (b) disconnection of incident reporting and investigation from liability; (c) establishment of voluntary reporting systems; (d) provisions on confidentiality of the reports and protection of the reporters; (e) mandatory investigation of at least some serious incidents by an independent authority; (f) effective dissemination of safety information. A major enabler of all these is the development of a strong safety culture.

Rick Roth (Government the United States America Naval Postgraduate) has posted “Concept Programming for Dependable AI” on SSRN. Here is the abstract:

Large language models deployed in critical systems today have no principled method for instilling specified behavioral concepts, whether explicit safety constraints, cultural values, professional norms, or governance principles, that generalize dependably across novel contexts. Such systems are termed Language-Grounded Neural Systems (LGNS) throughout this paper. Methods used to date train what to avoid rather than what to embody, leaving hidden values to emerge unpredictably from pretraining and making dependable behavior impossible to certify or verify. Concept Programming trains each concept as a named positive attractor in the model’s neural state space. Concepts that mark prohibitions are paired with blocking responses. Concepts that mark obligations are paired with required action responses. Concepts that embody values shape inference and judgment directly. Each type uses the same training mechanism: instances sampled from the empirical frequency distribution of the concept’s semantic case frame, activating the concept’s attractor across its full basin. Three controlled experiments on a freely available 3-billion-parameter local model establish the method empirically. Experiment 1 shows that positive concept training achieves 94.4% adherence on governance safety constraints versus 77.8% for punishment-analog training and 47.2% for untrained control, with 100% transfer to novel domains not seen in training. Experiment 2 shows that CP instills cultural value manifolds producing tradition-consistent reasoning on the five canonical moral psychology anchor cases in the published literature; the Buddhist-trained condition achieves a perfect score where the 2 untrained control scores 40%. Experiment 3 characterizes the learning curve and introduces a governance-adapted signal detection framework showing that CP reduced the miss rate from 100% in the untrained control to 19-25% within 200 training cases, with both trained conditions showing a false alarm rate of 42-46% versus 94% in control. The entire experimental program runs on a consumer laptop at negligible cost. CP opens every domain of human activity to more dependable LGNS-based systems and defines a research program as broad as the range of human concepts AI systems will need to embody.

Mohammad Shahidullah (International American U (IAU)) et al. have posted “Intelligent Fraud Detection: Applying Advanced Analytics and Cybersecurity Insights in U.S. Finance” (Journal of Posthumanism, volume 4, issue 3, 2024 [10.63332/joph.v4i3.3593]) on SSRN. Here is the abstract:

Fraud detection in financial transactions is a major and crucial problem that does not cease to exist, mainly because of the enormous imbalance in the datasets obtained and the very high requirement for an accurate distinction between legitimate and fraudulent activities. In the following study, we assess the performance of three common machine learning models: Logistic Regression, Random Forest, and Gradient Boosting, for the detection of fraud, using a real-data set of transactions (284807 of which only 0.173% are labelled as fraudulent). The models were thoroughly evaluated with respect to critical metrics of performance including the precision, recall, F1-score and Area Under the Receiver Operating Characteristic Curve (AUC) to try to understand which of the models may be appropriate for dealing with class imbalance and false positives. Of the analyzed models, Random Forest was the best, with AUC being equal to 0.98, being superior to Logistic Regression (AUC = 0.97) and equal to Gradient Boosting (AUC = 0.98), while enabling more superior recall (0.88) and precision (0.44). This implies a higher capacity of detecting fraud cases without compromising the rate of false alarm too much. Feature importance analysis further noted that V14, V10, and V4 features were most predictive and most responsible for model classifying accuracy. Furthermore, calibration analysis revealed that Random Forest was the most reliable in estimating probabilities, outputs closely conformed to the ideal calibration curve implicating better reliability in practical applications. These findings suggest the effectiveness of the ensemble machine learning models especially Random Forest in promoting the efficacy of fraud detection systems. The study supports future research on real-time deployment and integration with deep learning methods to enhance the strength of fraud detection in the constantly changing financial spaces.

Asaf Lubin (Indiana U Maurer Law) has posted “Out of Time: Artificial Intelligence, Self-Help, and International Law’s Temporal Logic” (The Cambridge Handbook of Public Law and Artificial Intelligence, Gregory, Williams, & Zerilli eds., Cambridge Univ. Press, forthcoming 2027.) on SSRN. Here is the abstract:

The rule of law has a tempo. It unfolds in time, organizes through time, and relies on time as a condition of its own functioning. Legality is sustained through an iterative practice of claim and counterclaim, argumentation and contestation. That exchange has a temporal structure. Time is needed for any claim to be formulated, any premise to be tested, any objection to be heard, and any judgment to be revised. In this sense, law is more than just a system of rules, institutions, and sanctions. It is also a system of pauses and intervals: the space between power’s first impulse and law’s second thought.

Law’s discursive and temporal character is most acute in international law. In a decentralized system lacking compulsory jurisdiction, states make law meaningful by arguing through it. Nowhere is this more evident than in the international law of self-help. The doctrines of self-defense, countermeasures, and retorsions govern the circumstances in which each state may invoke its own interests as grounds for unilateral protective action. The risk, of course, is that self-help will become self-licensing; that states, moving first, moving alone, and moving in secret, will be incentivized toward abuse and vigilantism. International law responds to this concern by forcing structured moments of argumentation around unilateral uses of power. The necessity, imminence, and proportionality of a use of force must be assessed and defended. The attribution of a prior wrong, together with the notice, purpose, and reversibility of a countermeasure, must likewise be tested and justified. These requirements are what prevent the law of self-help from becoming a vocabulary for disobedience.

And yes, these assessments also require time. Lots of time. Time for inquiry and for dissent. Time for simmering and for digestion. Time for contemplation and for meditation. Time for sleep, even. Indeed, some of the world’s greatest inventors and creators credit their creativity to the power nap. The sleeping mind consolidates, reconsiders, and sometimes resolves what the waking mind cannot.

But AI does not sleep. AI is built to increase efficiency through automation. It promises to offload cognition onto the machine, thereby removing the supposed waste and cost of “human friction.” AI does not deliberate, simmer, or meditate. It does not toss and turn at night. It does not wake at three in the morning with a reconsidered judgment. It does not pray or consult a loved one. It only processes. And it processes fast. Extraordinarily fast. So fast, in fact, that it outpaces law’s intervals. As this book chapter reveals, the very features that make AI attractive to national security decision-makers—its speed, scale, and tirelessness—are precisely the features that make it dangerous as a substitute for legal judgment. The chapter therefore asks not whether AI must be kept outside the national security state, for it will not be, but whether the state can use it without surrendering the pauses, frictions, and second thoughts through which law makes power answer.

Peter K. Yu (Texas A&M U Law) has posted “Toward a New Research Agenda for Artificial Intelligence and International Law” (UCLA Journal of International Law & Foreign Affairs, Vol. 30, 2026, Forthcoming) on SSRN. Here is the abstract:

Over the past few years, artificial intelligence—in particular, generative AI—and the changes brought about by this new technology have garnered priority attention from policymakers, commentators, and the mass media. Issues such as job displacement, the unauthorized use of personal information and likeness, the protection of intellectual property rights, and environmental sustainability have attracted analyses through an AI lens. What is less explored, however, is how AI will change the future development of international law. 

To provide an overview of the issues that have received attention thus far and that will continue to emerge at the intersection of AI and international law, this short essay, which was expanded from the opening remarks on the “Artificial Intelligence and the Boundaries of International Law” panel at International Law Weekend—West 2026, identifies eight issues that deserve greater policy and scholarly engagement. Together, these issues help demonstrate the need for a new research agenda for AI and international law and provide content to fill this agenda.

Alex Amadori (Conjecture) et al. have posted “Modeling the Geopolitics of AI Development” on SSRN. Here is the abstract:

We model national strategies and geopolitical outcomes under differing assumptions about AI development. We put particular focus on scenarios with rapid progress that enables highly automated AI R&D and provides substantial military capabilities. Under non-cooperative assumptions-concretely, if international coordination mechanisms capable of preventing the development of dangerous AI capabilities are not established-superpowers are likely to engage in a race for AI systems offering an overwhelming strategic advantage over all other actors.

If such systems prove feasible, this dynamic leads to one of three outcomes: (1) One superpower achieves an unchallengeable global dominance; (2) Trailing superpowers facing imminent defeat launch a preventive or preemptive attack, sparking conflict among major powers; (3) Loss-of-control of powerful AI systems leads to catastrophic outcomes such as human extinction.

Middle powers, lacking both the muscle to compete in an AI race and to deter AI development through unilateral pressure, find their security entirely dependent on factors outside their control: a superpower must prevail in the race without triggering devastating conflict, successfully navigate loss-of-control risks, and subsequently respect the middle power’s sovereignty despite possessing overwhelming power to do otherwise.

Mahmood Khalid (Ziauddin U) has posted “The Use of Autonomous Weapons in the Ukraine Conflict:Assessing Compliance with IHL Principles” on SSRN. Here is the abstract:

This research paper examines how Autonomous Weapon Systems (AWS) are being used in the war between Russia and Ukraine and evaluates how well they adhere to the fundamental rules of International Humanitarian Law (IHL), such as proportionality, distinction, and caution. Based on actual case studies using Ukrainian AI-enabled drones and surveillance platforms and Russian loitering bombs, the study examines the level of human control, the consequences for accountability, and the operation of autonomous systems on the battlefield. It assesses the moral and legal ramifications of giving machines the ability to make deadly decisions, emphasizing potential dangers such attribution errors, automation bias, and a lack of contextual judgment. The paper also looks at existing international legal frameworks, including the Geneva Conventions, CCW, and customary IHL, and finds weaknesses in their capacity to control new technology. The article claims that current rules are unable to handle the growing threat posed by AWS, citing expert viewpoints, particularly Paul Scharre’s work on autonomy in warfare. In order to guarantee accountability, protect human dignity, and stop abuse, it ends by assessing current regulatory initiatives and suggesting legislative changes. In the end, the situation in Ukraine is a real-world experiment that highlights how urgent it is to have complete international oversight of AWS.

Sofiya Sayankina (Hankuk U Foreign Studies) et al. have posted “Defining the Intension and Extension of Nations’ Sovereignty in the Age of Generative AI” on SSRN. Here is the abstract:

Generative Artificial Intelligence (GAI)’s wide-ranging potential for generation, communication and dissemination of information marks an unprecedented level of transformation by challenging states with varying approaches to state sovereignty. These implications underscore the necessity of examining how states maintain control over digital territories that are being reshaped by the increasing influence of GAI. In particular, we explore how economic, political and social circumstances will shape government regulations on AI. This research highlights the importance of debate concerning how the notion of sovereignty itself may be shaped amidst the proliferation of GAI.