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.

Xiaoming Zhai (The U Georgia) and Joseph Krajcik (Michigan State U CREATE STEM Institute) have posted “Pseudo Artificial Intelligence Bias” on SSRN. Here is the abstract:

Pseudo artificial intelligence bias (PAIB) is broadly disseminated in the literature, which can result in unnecessary AI fear in society, exacerbate the enduring inequities and disparities in access to and sharing the benefits of AI applications, and waste social capital invested in AI research. This study systematically reviews publications in the literature to present three types of PAIBs identified due to (a) misunderstandings, (b) pseudo mechanical bias, and (c) overexpectations. We discuss the consequences of and solutions to PAIBs, including certifying users for AI applications to mitigate AI fears, providing customized user guidance for AI applications, and developing systematic approaches to monitor bias. We concluded that PAIB, due to misunderstandings, pseudo mechanical bias, and overexpectations of algorithmic predictions, is socially harmful.

Zihan Chen (Tsinghua U) has posted “The Algorithmic Curtain: Geopolitical Polarisation and the Fragmentation of Global AI Governance” on SSRN. Here is the abstract:

This article investigates the new ethical, legal, and geopolitical challenges that the rapid proliferation of artificial intelligence presents to international law. The central argument is that the current fragmentation of AI governance is not an incidental outcome, but a deliberate manifestation of competing visions for digital sovereignty. The analysis examines several core dimensions of this phenomenon, including the rise of geopolitical “walled gardens” driven by regional restrictions, creating an “algorithmic curtain”. It also analyzes the emergence of three distinct and competing governance models led by the European Union, the United States, and China, each rooted in different legal philosophies and strategic priorities. Furthermore, the article explores the profound sociological consequences of this divergence, such as the exacerbation of the global “North-South” AI divide and the erosion of a universal digital commons. Drawing a historical analogy from the commercialization of outer space, the analysis shows how differing approaches to data governance, national security, and innovation are erecting this algorithmic curtain, challenging the universality of human rights and hindering global cooperation. The article concludes by proposing a polycentric governance architecture focused on interoperability and harmonization of baseline standards to mitigate the most severe consequences of this geopolitical division.

Klaudia Klonowska (T.M.C. Asser Institute) and Taylor Kate Woodcock (T.M.C. Asser Institute) have posted “Rhetoric and Regulation: The (Limits of) Human/AI Comparison in Legal Debates on Military AI” (Forthcoming in Boutin B., Woodcock T. K. & Soltanzadeh S. (eds.), Decision at the Edge: Interdisciplinary Dilemmas in Military Artificial Intelligence, Asser Press (2025)) on SSRN. Here is the abstract:

The promise of artificial intelligence (AI) is ubiquitous and compelling, yet can it truly deliver ‘better’ speed, accuracy, and decision making in the conduct of war? As AI becomes increasingly embedded in targeting processes, legal and ethical debates often compare who performs better, humans or machines? In this Chapter, we unpack and critique the prevalence of comparisons between humans and AI systems, including in analyses of the fulfilment of legal obligations under International Humanitarian Law (IHL). We challenge this binary framing by highlighting misleading assumptions that neglect how the use of AI results in complex human-machine interactions that transform targeting practices. We unpack what is meant by ‘better performance’, demonstrating how prevailing metrics for speed and accuracy can create misleading expectations around the use of AI given the realities of warfare. We conclude that holistic but granular attention must be paid to the landscape of human-machine interactions to understand how the use of AI impacts compliance with IHL targeting obligations.

Rangita De Silva De Alwis (U Pennsylvania Carey Law) has posted “”Because We Take Our Values to War” Analyzing the Views of UN Member States on AI-Driven Lethal Autonomous Weapon Systems” (Chicago Journal of International Law, forthcoming) on SSRN. Here is the abstract:

Paragraph 2 of the UN General Assembly Resolution 78/241 requested the Secretary-General to solicit the views of Member States and Observer States regarding lethal autonomous weapons systems (LAWS). Specifically, the request encompassed perspectives on addressing the multifaceted challenges and concerns raised by LAWS, including humanitarian, legal, security, technological, and ethical dimensions, as well as reflections on the role of human agency in the deployment of force. The Secretary-General was further mandated to submit a comprehensive report to the General Assembly at its seventy-ninth session, incorporating the full spectrum of views received and including an annex containing those submissions for further deliberation by Member States.

In implementation of this directive, on 1 February 2024, the Office for Disarmament Affairs issued a note verbale to all Member States and Observer States, drawing attention inviting their formal input. This paper for the first time analyzes the positions of Member States on AI- driven LAWS. Using a qualitative coding matrix, the paper examines Member States’ positions in relation to human centric approaches to AI- driven LAWS, and compliance with international humanitarian law. Moreover, it argues that the standard for autonomous weapons systems’ compliance with the laws of war should not only be whether they follow the principles of international humanitarian law of distinction, proportionality, and precaution but whether they can be free of data, algorithmic, and programmer bias.  Although much has been written about algorithmic bias, an “algorithmic divide” can create an AI- driven weapons asymmetry between different nation states depending on who has access to AI.

The article raises the question whether Yale Law’s Oona Hathaway’s recent arguments on individual and state responsibility for the patterns of “mistakes” in war may also apply to the pattern of biases in AI- driven LAWS. In current and future disputes, machines do and will continue to make life-and-death decisions without the help of human decision-making. Who will then be responsible for the “mistakes” in war?

During the 2017 testimony to the US Senate Armed Services Committee, then-Vice Chairman of the Joint Chiefs of Staff General Paul Selva stated, ….“because we take our values to war …. I do not think it is reasonable for us to put robots in charge of whether or not we take a human life.” The laws of war are rapidly advancing to a critical crossroads in war’s relationship with technology.