International Law and Artificial Intelligence

国际法和人工智能 (International Law and Artificial Intelligence)

The continuous growth of artificial intelligence (AI) capabilities is poised to transform human lives across various sectors, including health care, agriculture, and scientific research. Nevertheless, certain applications of AI, particularly the development of AI-powered lethal autonomous weapon systems (LAWS), remain highly contentious due to concerns about whether such weapon systems would comply with international law, including international humanitarian law. LAWS, colloquially known as “killer robots,” have emerged at the forefront of contemporary weapons technology and are considered the “third revolution” in warfare, after the invention of gunpowder and nuclear weapons. In the absence of a regulatory framework, the advancement of sophisticated LAWS presents a significant threat to international peace and security. These systems, capable of independently conducting missions, selecting targets, and applying force, are susceptible to errors and miscalculations that could lead to unforeseen and potentially catastrophic consequences. Such errors might trigger dangerous escalations or unintended conflicts due to false alarms or accidents. Additionally, an AI arms race might push states to overlook ethical, legal, and security considerations in their effort to accelerate advancements and surpass their adversaries. In essence, the rapid incorporation of AI into modern military operations presents a multifaceted set of challenges for policy makers, legal experts, scientists, ethicists, military strategists, and the world at large. Against this backdrop, the article first assesses the compatibility of LAWS with international humanitarian law, then examines their impact on international peace and security, and finally analyzes the potential implications of LAWS for the future of peace and security in the Global South.

We introduce the Economic Productivity of Energy (EPE), GDP generated per unit of energy consumed, as a quantitative lens to assess the sustainability of the Artificial Intelligence (AI) revolution. Historical evidence shows that the first industrial revolution, pre-scientific in the sense that technological adoption preceded scientific understanding, initially disrupted this ratio: EPE collapsed as profits outpaced efficiency, with poorly integrated technologies, and recovered only with the rise in scientific knowledge and societal adaptation. Later industrial revolutions, such as electrification and microelectronics, grounded in established scientific theory, did not exhibit comparable declines. Today’s AI revolution, highly profitable yet energy-intensive, remains pre-scientific and may follow a similar trajectory in EPE. We combine this conceptual discussion with cross-country EPE data spanning the last three decades. We find that the advanced economies exhibit a consistent linear growth in EPE: these countries account for a large share of global GDP and energy use and are therefore expected to be most affected by the AI transition. Therefore, we advocate for regular monitoring of EPE: transparent reporting of AI-related energy use and productivity-linked incentives can expose hidden energy costs and prevent efficiency-blind economic expansion. Embedding EPE within sustainability frameworks would help align technological innovation with energy productivity, a critical condition for sustainable growth.

Background and Objectives: Artificial intelligence (AI) has transitioned to an integral part of dermatology in only few years, yet perceptions of its use vary widely, reflecting diverse hopes, concerns, and perceived clinical utility. Materials and Methods: In this study, 300 dermatologists from 13 countries, representing a range of experience levels and AI usage statuses, were surveyed regarding the characteristics and applications of AI in dermatology. Results: Among respondents, 61.33% reported having used AI tools in clinical practice. Adoption of AI was observed across all age groups, countries, and experience levels. Analysis of the types of AI tools used revealed a strong reliance on general-purpose large language models (LLMs), with chatbots being the most frequently cited category, utilized by 58.15% of users. Younger clinicians demonstrated a significant preference for chatbots (p < 0.05). Country-specific patterns in AI adoption were also noted. The most highly rated expected benefit of AI in dermatology was improved diagnostic accuracy, while the primary concern centered on regulatory and ethical limitations, suggesting that the "AI revolution" in dermatology is currently constrained less by technical barriers and more by regulation considerations. Use of consent forms when AI use takes place was more frequently reported as mandatory by dermatologists who had never used AI, reflecting heightened caution among non-users (p = 0.03). Additionally, 75% of respondents agreed that formal training in AI is necessary, highlighting a significant gap in traditional medical education regarding emerging technologies.

It has become a truism that data are now bigger than ever before: that is, information assembled and “used for reference, analysis, and calculation,” especially in digital form, has increased in volume, variety, and velocity to unprecedented levels, “to the extent that [their] manipulation and management present significant . . . challenges” (OED Online, s.v. “data (n.),” and “big (adj., adv.)”). In the international legal field, scholarly interest is growing in the phenomenon of “big data”—used here in the singular, as a mass noun—and what it might mean in and for international legal institutions and international legal work. For the most part, however, that scholarship is being pursued under auspices other than “Big Data and International Law.” Moreover, given the diversity of data types, analytical techniques, and technological settings evoked by the term big data, it is unclear that any subfield so named could acquire or retain coherence. International legal scholarship attentive to the implications of data’s digitization and the impact of digital technologies just as often travels under the rubrics of (international) law and technology, algorithmic governance, or, more recently, artificial intelligence and international law. Nonetheless, this bibliography assembles works representative of this burgeoning scholarship that are likely to be helpful to those interested in big data and international law. It mostly sidesteps areas that are, or are becoming, fields in their own right, to which big data is critical, but which are not especially attentive to its role; for example: the law of privacy; the international law of cyberwarfare and automated weaponry; the international law of distributed ledger technologies (for example, blockchain) and digital currencies; the private international law of smart contracting, digital dispute resolution, and digital risk protection; and international space law. This bibliography includes work by scholars who identify as public international lawyers and writings that cannot be so described, by which international legal scholars interested in big data might nevertheless be usefully informed. Also included are works that take a comparative and/or transnational approach to the analysis of legal issues for big data and big data issues for law. The headings proceed, more or less, from the introductory to the specialized. Earlier references tend to foreground discipline-wide dilemmas and shifts. Later, the bibliography highlights some key subfields in which inquiries surrounding big data and international law are proliferating, including in the conduct of international legal research. This bibliography is indicative, not exhaustive.

The paper explores the challenges posed by the artificial intelligence (AI) industrial revolution and emphasizes the need for responsible and ethical handling of AI technology. It primarily focuses on issues related to data security, privacy, ethics, and societal consequences. The integration of AI into various aspects of society requires strict regulation to prevent unintentional biases, discrimination, and surveillance. Maintaining transparency, fairness, and accountability within AI systems is a challenging task. In addition to discussing these challenges, the paper identifies key research questions concerning the AI revolution's impact on ethics, privacy, workforce disruptions, and societal dynamics. These questions serve as a roadmap for future studies to better understand how AI affects individuals, organizations, and society as a whole. To investigate these challenges and opportunities, the paper outlines a comprehensive research methodology that combines qualitative and quantitative methods, including literature reviews, case studies, surveys, and expert interviews. This approach provides a well-rounded perspective on the AI revolution by synthesizing theoretical foundations, real-world examples, and insights from various stakeholders. The study presents findings highlighting the significant progress AI has brought in terms of automation, innovation, and productivity, enhancing human capabilities. However, it also underscores concerns related to privacy, ethics, and potential job displacement. The paper advocates for responsible AI development, emphasizing the importance of ethical guidelines, regulations, and transparency. These findings offer valuable insights for policymakers, industry experts, and researchers, helping them better understand and harness the potential of AI while addressing its risks and societal impacts.

In Artificial Intelligence, “Artificial” means objects that are produced by human beings, and “Intel ligence” is the capability to form tactics to achieve goals by interacting with huge information. Artificial Intelligence (AI) is evolving rapidly in higher education, and various AI applications have been developed to solve some of the most pressing problems that higher education field currently face. The use of Artificial Intelligence or AI is rising in higher education. With this rise, the morality of AI programs is being questioned Higher education is crucial for producing ethical citizens and profess ionals globally. The introduction of generative AI (GenAI), such as ChatGPT, has posed opportunities and challenges to the traditional model of education. However, the current conversations primarily focus on policy development and assessment, with limited research on the future of higher education.AI - powered language tools (AILTs) are commonly used by university students, yet there is a limited understanding of how students utilize and perceive these tools in everyday academic communication practice. Following the very recent launch of the ChatGPT, chatbot, numerous comments and speculations were posted concerning the potential aspects of society that are expected to benefit from this AI revolution. This paper addresses some of the most fundamental questions about the role, position, and implications of ChatGPT and generative artificial intelligence (AI) tools amidst the evolving landscape of higher education and modern society.AI education aims to teach AI concepts, essential knowledge, and skills related to the fundamental ideas in AI. The affordances of artificial intelligence (AI) have not been totally utilized in education. To effectively integrate AI into education, teachers’ AI - specific technological and pedagogical knowledge is important.

World leaders, CEOs, and academics have suggested that a revolution in artificial intelligence is upon us. Are they right, and what will advances in artificial intelligence mean for international competition and the balance of power? This article evaluates how developments in artificial intelligence (AI) — advanced, narrow applications in particular — are poised to influence military power and international politics. It describes how AI more closely resembles “enabling” technologies such as the combustion engine or electricity than a specific weapon. AI’s still-emerging developments make it harder to assess than many technological changes, especially since many of the organizational decisions about the adoption and uses of new technology that generally shape the impact of that technology are in their infancy. The article then explores the possibility that key drivers of AI development in the private sector could cause the rapid diffusion of military applications of AI, limiting first-mover advantages for innovators. Alternatively, given uncertainty about the technological trajectory of AI, it is also possible that military uses of AI will be harder to develop based on private-sector AI technologies than many expect, generating more potential first-mover advantages for existing powers such as China and the United States, as well as larger consequences for relative power if a country fails to adapt. Finally, the article discusses the extent to which U.S. military rhetoric about the importance of AI matches the reality of U.S. investments.

The objective of this study was to evaluate radiologists' and radiographers' opinions and perspectives on artificial intelligence (AI) and its integration into the radiology department. Additionally, we investigated the most common challenges and barriers that radiologists and radiographers face when learning about AI. A nationwide, online descriptive cross-sectional survey was distributed to radiologists and radiographers working in hospitals and medical centres from May 29, 2023 to July 30, 2023. The questionnaire examined the participants' opinions, feelings, and predictions regarding AI and its applications in the radiology department. Descriptive statistics were used to report the participants' demographics and responses. Five-points Likert-scale data were reported using divergent stacked bar graphs to highlight any central tendencies. Responses were collected from 258 participants, revealing a positive attitude towards implementing AI. Both radiologists and radiographers predicted breast imaging would be the subspecialty most impacted by the AI revolution. MRI, mammography, and CT were identified as the primary modalities with significant importance in the field of AI application. The major barrier encountered by radiologists and radiographers when learning about AI was the lack of mentorship, guidance, and support from experts. Participants demonstrated a positive attitude towards learning about AI and implementing it in the radiology practice. However, radiologists and radiographers encounter several barriers when learning about AI, such as the absence of experienced professionals support and direction. Radiologists and radiographers reported several barriers to AI learning, with the most significant being the lack of mentorship and guidance from experts, followed by the lack of funding and investment in new technologies.

Artificial intelligence (AI) is a relatively new medical resource with the potential to revolutionize current practices in the prevention and treatment of disease. AI has been defined as computer programs accomplishing tasks traditionally associated with human intelligence such as learning and solving problems. As the ethical benefits of increased efficiency and productivity of AI systems are being realized, the consequences of implementing such transformative technologies has raised ethical and regulatory questions across the globe. AI represents a tool to address longstanding issues in healthcare delivery and can achieve a caliber of healthcare quality that was previously beyond our grasp. However, AI systems may incorporate and often amplify existing patterns of practice, including societal biases and inequitable healthcare practices. Surmounting these ethical and regulatory challenges represents the next frontier in the successful implementation of AI to promote human development and wellbeing. In this study, we examined the current literature and analyzed the scope of practice around the ethical and regulatory issues surrounding AI in medicine and its application to healthcare. Knowledge integration was performed across disciplines relevant to the potential role for AI in facilitating progress, innovation, and quality assurance in healthcare. Thematic analysis was conducted on qualitative data pertaining to both ethical and regulatory challenges concerning the implementation of AI into healthcare practices. The project provided exposure to the innovative field of AI and various strategies related to ethical issues, regulatory laws, quality improvement, and healthcare management. We explored both the reliability and current limitations of AI in order to create best practices guidelines designed to facilitate the successful incorporation of AI into healthcare fields. Ethical challenges of AI such as risk management, data security, and a lack of transparency span all sectors working to implement these new technologies. All medical disciplines working to leverage the potential applications of AI struggle with the ethical challenges of informed consent, autonomy, accountability, biases, and equitable healthcare delivery. The field of laboratory medicine and pathology was a pioneer in the implementation of AI technology. Laboratory medicine and pathology face additional hurdles when ensuring accurate interpretation of results such as unequal contexts, opportunity costs, and low levels of acceptable risk and uncertainty. Rather than an all-or-nothing approach, we suggest a stepwise, transparent, and patient-centered approach with clear boundaries to the incorporation of new tools. The AI-assisted era of medical care will be transformative but will never be void of all risk or ethical challenges. This work represents the first of many steps in using AI technology to optimize healthcare delivery in a way that protects and strengthens the ethical values of medical care.

This research addresses the inherent limitations of traditional artificial intelligence (AI) systems, particularly their reliance on tokenization and the input-output paradigm, which constrain semantic continuity and scalability despite advancements in architectures like Meta’s Language Concept Model (LCM). We propose the BNAI Non-Token Neural Network framework to overcome these barriers through three iterative phases. The first phase introduces BNAI (Bulla Neural Artificial Intelligence), a novel metric encoding an AI’s digital DNA to enable faithful cloning and identity preservation. The second phase extends BNAI by incorporating ethical considerations, yet remains tethered to tokenization and input-output constraints. The third phase fully transcends these limitations by integrating the NO-TOKEN module for continuous embedding and the MIND-UNITY module for autonomous decision-making, fostering a paradigm of mutable, self-evolving AI systems. Experiments on the SST-2 dataset demonstrate the framework’s efficacy: the NO-TOKEN Model achieves 89% accuracy and 95ms latency, surpassing the BERT-base baseline (88% accuracy, 120ms latency), while the BNAI Model matches this performance (89% accuracy, 95.5ms latency) on a 16-core CPU after 100 epochs. These results validate the hypothesis that eliminating tokenization and input-output dualism enhances performance and efficiency. This research, conducted entirely as an open-source initiative, lays the foundation for scalable, ethical, and autonomous AI systems, with future work aimed at broader validation and ethical refinement.The results and open-source code presented in this research do more than demonstrate technical viability, they herald a new era for AI. By achieving superior performance on the SST-2 dataset and surpassing established baselines, the BNAI framework proves that non-tokenizing, self-evolving systems are not just feasible but transformative. This work establishes a “North Star” for AI innovation, where continuous learning and ethical autonomy drive progress.We invite collaboration and feedback, recognizing that diverse perspectives are essential to refining this revolutionary approach. By sharing our codebase and findings openly, we aim to inspire a global community of researchers, developers, and thinkers to join us in shaping an AI that thinks, learns, and evolves like a human being.

The rapid integration of artificial intelligence (AI) into clinical practice prompts a critical re-examination of the roles of physicians and how we educate them. While AI promises unparalleled gains in accuracy and speed, and better management decisions and health outcomes, doctors must be skilled in harnessing these new AI tools effectively and wisely to improve patient outcomes. We seek to layer further upon this with a call for medical education to go further than simply improving AI literacy of doctors, but to include a comprehensive reform of medical education. This reform would aim to expand physician capabilities from the traditional cognitive knowledge of medicine to integrating AI competencies seamlessly, with a renewed focus on the humanistic aspects of medicine. We propose the Humanistic Medicine - AI-Enabled Education (HuMe-AiNE) framework, which includes the key components: (1) standardisation and individualisation of AI competencies; (2) integration of AI tools through the curriculum; (3) fostering critical thinking skills in integrating technological solutions with a humanistic approach to patient care; and (4) developing a professional identity that encompasses both technology-related and humanistic capabilities. The AI revolution provides an opportunity for developments to medical education—to train doctors to be both tech-enabled physicians and true humanists.

Artificial intelligence (AI) technology poses possible threats to existing jobs. These threats extend not just to the number of jobs available but also to their quality. In the future, so some predict, workers could face fewer and potentially worse jobs, at least if society does not embrace reforms that manage the coming AI revolution. This paper uses the example of Daron Acemoglu and Simon Johnson’s recent book—Power and Progress (2023)—to illustrate some of the dilemmas and options for managing the future of work under AI. Acemoglu and Johnson, while warning of the potential negative effects of an AI-driven automation, argue that AI can be used for positive ends. In particular, they argue for its uses in creating more ‘good jobs’. This outcome will depend on democratising AI technology. This paper is critical of the approach taken by Acemoglu and Johnson—specifically, it misses the possibility for using AI to lighten work (i.e., to reduce its duration and improve its quality). This paper stresses the potential benefits of automation as a mechanism for lightening work. Its key arguments aim to advance critical debates focused on creating a future in which AI works for people not just for profits.

Artificial Intelligence (AI) has rapidly emerged as a transformative force in modern medicine, revolutionising diagnostics, treatment personalisation, and clinical decision-making. This review synthesises current literature on AI's evolution, applications, challenges, and future directions in healthcare. From early rule-based systems to advanced deep learning algorithms, AI has consistently demonstrated capabilities that rival and enhance human expertise—particularly in imaging, predictive analytics, and drug discovery. The role of AI in global health is also expanding, offering scalable solutions to reduce disparities in low-resource settings. However, the integration of AI raises ethical and legal concerns, including data privacy, algorithmic bias, and unclear accountability frameworks. Drawing on the Technology Acceptance Model (TAM), Diffusion of Innovations Theory, and Principlism, this review highlights theoretical perspectives essential to understanding AI adoption and governance. The paper concludes with a call for longitudinal studies, ethical frameworks, and policy innovations to support AI's responsible and equitable deployment in the medical field.

The article explores autonomous weapon systems (AWS) operating with artificial intelligence as a complex challenge to contemporary international humanitarian law (IHL) and the international human rights framework. It analyses the technological capabilities and levels of autonomy of combat systems – including land, aerial, and naval unmanned platforms – that are already being used in current armed conflicts. Particular attention is given to the compliance of AWS with the core principles of IHL: distinction, proportionality, humanity, and the prohibition of indiscriminate attacks. The study substantiates the problem of «blurred» responsibility, particularly the difficulty of attributing violations committed by autonomous or semi-autonomous weapon systems to a specific accountable subject. It examines the risks posed by AWS to the observance of Articles 2, 3, 8, and 13 of the European Convention on Human Rights. The potential of the European Court of Human Rights’ case law to adapt to emerging technological realities through structured interpretation and the expansion of precedent is analysed. Special attention is given to international dialogue under the auspices of the United Nations – notably within the framework of the Convention on Certain Conventional Weapons (CCW), which addresses weapons deemed to cause excessive injury or have indiscriminate effects – and to the work of the Group of Governmental Experts on Emerging Technologies in the Area of Lethal Autonomous Weapons System, as well as the role of soft law, state positions, and international organisations. The article concludes with recommendations regarding the need to preserve meaningful human control, update legal mechanisms of responsibility, and develop a universal regulatory framework for AWS. It also highlights the importance of an interdisciplinary approach, particularly the integration of ethical, technical, and security considerations in shaping the legal regime governing the use of artificial intelligence systems and tools in military contexts.