Apocalyptic imaginaries: Risk and regulation in discourses of military AI and nuclear weapons

This comparative review explores the advancements and applications of Artificial Intelligence (AI) in agriculture, focusing on the developments in the United States (USA) and Africa. The integration of AI technologies in agriculture has witnessed significant progress globally, addressing challenges and transforming traditional farming practices. In the USA, precision agriculture and smart farming techniques driven by AI have become integral components of modern agricultural systems. These innovations include autonomous machinery, drone technology for crop monitoring, and predictive analytics for yield optimization. In contrast, the application of AI in African agriculture presents a distinct set of challenges and opportunities. The review delves into initiatives aimed at leveraging AI to enhance agricultural productivity, improve resource management, and address food security concerns in various African nations. These efforts include the deployment of AI for pest and disease detection, crop monitoring in remote areas, and the implementation of data-driven decision-making tools to support smallholder farmers. The comparative analysis sheds light on the disparities in AI adoption between the USA and Africa, emphasizing factors such as infrastructure, technological accessibility, and resource availability. Additionally, it explores collaborative efforts and partnerships that bridge the gap and contribute to the sustainable development of AI in African agriculture. As both regions navigate the complexities of implementing AI in agriculture, this review underscores the potential for technology to play a pivotal role in addressing global food challenges. The findings highlight the need for tailored approaches, policy frameworks, and international collaborations to ensure inclusive and equitable access to AI-driven innovations in agriculture, fostering a shared commitment to sustainable and technologically empowered farming practices.

Artificial intelligence (AI) is applied in numerous areas of society and has also led to significant changes in the field of medicine. Medicine is a branch of science of exceptional importance, and it is therefore necessary to ensure a high level of patient protection. The quality of healthcare has significantly improved through the use of artificial intelligence in various stages of the medical process, from the analysis of medical data and diagnostics, through therapy planning, to patient monitoring and the management of healthcare systems. The aim of this paper is to analyze the civil law aspects of artificial intelligence in medicine, with a particular focus on questions of liability for damage resulting from the use of such systems. The paper will first present the basic characteristics and areas of application of artificial intelligence in medicine, and then examine potential sources of damage and the legal basis for the liability of various stakeholders, including AI system manufacturers, software developers and data providers, healthcare institutions, and healthcare professionals. Special attention will be given to the challenges of proving causation and allocating liability in situations where decisions are made or supported by autonomous algorithmic systems. However, at the same time, numerous legal issues arise, particularly in the field of civil liability in cases where the application of artificial intelligence results in harm to a patient. Given the great importance of medicine and the need to ensure a high level of patient protection, the application of artificial intelligence must be accompanied by appropriate legal protection. The paper gives answers to a number of questions, with particular emphasis on the question of who may be held liable for damage caused by the use of artificial intelligence in medicine, as well as under which regulations and in what manner such liability is determined. Artificial intelligence has the potential to significantly enhance medical practice, its application must be accompanied by appropriate legal mechanisms that ensure patient protection and clearly define the responsibility of all participants in the system. Future legal development in this area will likely focus on further adapting existing civil law institutions to the specificities of artificial intelligence, while simultaneously strengthening preventive risk management mechanisms and transparency of AI systems in medicine.

The aim of the work is to provide an overview of the potential application of artificial intelligence in forensic medicine and related sciences, and to identify concerns related to providing medico-legal opinions and legal liability in cases in which possible harm in terms of diagnosis and/or treatment is likely to occur when using an advanced system of computer-based information processing and analysis. The material for the study comprised scientific literature related to the issue of artificial intelligence in forensic medicine and related sciences. For this purpose, Google Scholar, PubMed and ScienceDirect databases were searched. To identify useful articles, such terms as "artificial intelligence," "deep learning," "machine learning," "forensic medicine," "legal medicine," "forensic pathology" and "medicine" were used. In some cases, articles were identified based on the semantic proximity of the introduced terms. Dynamic development of the computing power and the ability of artificial intelligence to analyze vast data volumes made it possible to transfer artificial intelligence methods to forensic medicine and related sciences. Artificial intelligence has numerous applications in forensic medicine and related sciences and can be helpful in thanatology, forensic traumatology, post-mortem identification examinations, as well as post-mortem microscopic and toxicological diagnostics. Analyzing the legal and medico-legal aspects, artificial intelligence in medicine should be treated as an auxiliary tool, whereas the final diagnostic and therapeutic decisions and the extent to which they are implemented should be the responsibility of humans.

Chapter 25 - Outlook of the future landscape of artificial intelligence in medicine and new challenges

Rapid digital transformation has resulted in a paradigm shift, creating the need for shaping effective AI (artificial intelligence) governance. AI governance encompasses laws, policies, frameworks, and practices at global, regional, national, and organizational levels. Evaluating the impact of AI requires addressing ethical considerations. The need for appropriate AI governance has been highlighted by the United Nations (UN), which has access to existing normative and policy instruments, such as international standards. Global efforts in the UN system regarding AI governance are grounded in international law.AI governance takes a pragmatic approach and is delivered through an ecosystem that includes research, development, coordination, monitoring, evaluation, capacity building, and stakeholder engagement. Effective AI governance is essential in shaping the future of AI. At the European Union (EU) level, significant progress has been made, particularly with the AI Act, which is the first-ever regulation specifically on AI. The AI Act follows a risk-based approach and categorizes AI systems into four risk levels. It applies not only to EU-based providers but also to those outside the EU, particularly when their AI inputs are used within the EU.As Europe’s geopolitical influence remains crucial, particularly in light of recent developments, AI governance must be addressed from a multi-stakeholder perspective. The aim of this paper is to identify the current challenges in shaping AI governance in Europe through this approach. The study employs a qualitative, case-study methodology, analyzing the roles and needs of key stakeholders, including governments, regulatory bodies, international institutions, AI engineers, ethicists, industry associations, and end-users.

Artificial intelligence (AI) governance is required to reap the benefits and manage the risks brought by AI systems. This means that ethical principles, such as fairness, need to be translated into practicable AI governance processes. A concise AI governance definition would allow researchers and practitioners to identify the constituent parts of the complex problem of translating AI ethics into practice. However, there have been few efforts to define AI governance thus far. To bridge this gap, this paper defines AI governance at the organizational level. Moreover, we delineate how AI governance enters into a governance landscape with numerous governance areas, such as corporate governance, information technology (IT) governance, and data governance. Therefore, we position AI governance as part of an organization’s governance structure in relation to these existing governance areas. Our definition and positioning of organizational AI governance paves the way for crafting AI governance frameworks and offers a stepping stone on the pathway toward governed AI.

Africa as a continent recognizes the need for Artificial Intelligence (AI) governance frameworks. However, despite initiatives like the African Union's Continental AI Strategy, African nations grappled with challenges in formulating comprehensive AI policies, while established global frameworks provided advanced benchmarks for comparison. To address this disparity, this study conducted a comparative analysis of AI governance in Africa relative to global standards and practices. Employing a comprehensive document analysis methodology, the research examined key policy documents, strategic frameworks, and regulatory guidelines across African, European, American, and Asian contexts. The findings revealed that while the African Union demonstrated commitment to coordinated AI governance, African approaches generally lagged behind global benchmarks in comprehensiveness, formalization, and ethical integration. The study identified a notable fragmentation in AI governance across African nations, contrasting with more unified approaches in other regions. African frameworks emphasized leveraging AI for socio-economic development, diverging from the risk mitigation focus seen in EU regulations. The integration of indigenous African ethical perspectives in AI governance frameworks was limited, presenting both challenges and opportunities. Significant disparities in digital infrastructure and AI capacity between Africa and other regions were found to affect governance implementation. The study concluded that despite these challenges, there was potential for Africa to develop innovative, context-specific AI governance models that could contribute valuable insights to the global discourse on responsible AI development. Recommendations included accelerating the implementation of the Continental AI Strategy, prioritizing investment in digital infrastructure, developing Africa-centric AI ethics frameworks, establishing mechanisms for regular benchmarking against global standards, fostering increased collaboration, and implementing AI literacy programs across the continent.

Background/Purpose: The agriculture sector is the backbone of every nation which contributes to the global economy. The implementation of technology in agriculture has brought revolutionary development in its outcome. Due to this, a drastic improvement in the global economy from the agricultural sector is expected. Moreover, the implementation of artificial intelligence (AI) improves the productivity of farmers giving solutions to various challenges faced by the farmers. The various AI tools that are developed for the agriculture sector include precision farming, predictive analytics, automated machinery, smart irrigation systems, crop and soil monitoring, supply chain optimization, weather forecasting, and livestock management. Adopting AI in agriculture faces several challenges despite its long-term benefits. The high upfront costs to be invested in implementing AI technology make it difficult for small-scale and developing farmers to invest in AI. Implementing the above technology needs technical skills, fast internet connectivity, and costlier equipment. Due to the lack of the above-mentioned requirements, the AI technologies that are meant for agriculture do not reach the farmers. This results in the wastage of resources for AI without the outcome. Considering the above issues an appropriate simplified model is proposed that facilitates the adaptation of the AI technology by small and medium-scale farmers in their agriculture to improve the performance. Objective: The objective of this paper is to review the various journals related to the implementation of AI in Agriculture and to study the various issues related to its implementation. It also aims at identifying the research gap which will help to develop a model suitable for the end like small-scale and medium-scale farmers. Design/Methodology/Approach: A systematic literature review was conducted by gathering and examining relevant literature from international and national journals, conferences, databases, and other resources accessed via Google Scholar and various search engines. Findings/Result: The agriculture sector, crucial to every nation's economy, has seen revolutionary advancements through technology, especially AI. AI tools like precision farming, predictive analytics, and smart irrigation promise to enhance productivity and address various agricultural challenges. However, high implementation costs, resistance to new technologies, and lack of necessary infrastructure hinder widespread adoption among small-scale and developing farmers. To overcome these obstacles, a model is proposed to effectively support farmers in adopting AI technologies to boost agricultural performance. Originality/Value: The implementation of AI and ML tools in agriculture from diverse sources is done. This area needs study due to recent challenges faced by small and medium-scale farmers in the implementation of AI and ML tools in agriculture. The information acquired will help to create a new model by improving the outcomes of the existing scenario. Paper Type: Literature Review.

Aim. The aim of the work is to provide an overview of the potential application of artificial intelligence in forensic medicine and related sciences, and to identify concerns related to providing medico-legal opinions and legal liability in cases in which possible harm in terms of diagnosis and/or treatment is likely to occur when using an advanced system of computer-based information processing and analysis. Materials and methods. The material for the study comprised scientific literature related to the issue of artificial intelligence in forensic medicine and related sciences. For this purpose, Google Scholar, PubMed and ScienceDirect databases were searched. To identify useful articles, such terms as „artificial intelligence,” „deep learning,” „machine learning,” „forensic medicine,” „legal medicine,” „forensic pathology” and „medicine” were used. In some cases, articles were identified based on the semantic proximity of the introduced terms. Conclusions. Dynamic development of the computing power and the ability of artificial intelligence to analyze vast data volumes made it possible to transfer artificial intelligence methods to forensic medicine and related sciences. Artificial intelligence has numerous applications in forensic medicine and related sciences and can be helpful in thanatology, forensic traumatology, post-mortem identification examinations, as well as post-mortem microscopic and toxicological diagnostics. Analyzing the legal and medico-legal aspects, artificial intelligence in medicine should be treated as an auxiliary tool, whereas the final diagnostic and therapeutic decisions and the extent to which they are implemented should be the responsibility of humans.

BackgroundThe current applications of artificial intelligence (AI) in medicine continue to attract the attention of medical students. This study aimed to identify undergraduate medical students’ attitudes toward AI in medicine, explore present AI-related training opportunities, investigate the need for AI inclusion in medical curricula, and determine preferred methods for teaching AI curricula.MethodsThis study uses a mixed-method cross-sectional design, including a quantitative study and a qualitative study, targeting Palestinian undergraduate medical students in the academic year 2022–2023. In the quantitative part, we recruited a convenience sample of undergraduate medical students from universities in Palestine from June 15, 2022, to May 30, 2023. We collected data by using an online, well-structured, and self-administered questionnaire with 49 items. In the qualitative part, 15 undergraduate medical students were interviewed by trained researchers. Descriptive statistics and an inductive content analysis approach were used to analyze quantitative and qualitative data, respectively.ResultsFrom a total of 371 invitations sent, 362 responses were received (response rate = 97.5%), and 349 were included in the analysis. The mean age of participants was 20.38 ± 1.97, with 40.11% (140) in their second year of medical school. Most participants (268, 76.79%) did not receive formal education on AI before or during medical study. About two-thirds of students strongly agreed or agreed that AI would become common in the future (67.9%, 237) and would revolutionize medical fields (68.7%, 240). Participants stated that they had not previously acquired training in the use of AI in medicine during formal medical education (260, 74.5%), confirming a dire need to include AI training in medical curricula (247, 70.8%). Most participants (264, 75.7%) think that learning opportunities for AI in medicine have not been adequate; therefore, it is very important to study more about employing AI in medicine (228, 65.3%). Male students (3.15 ± 0.87) had higher perception scores than female students (2.81 ± 0.86) (p < 0.001). The main themes that resulted from the qualitative analysis of the interview questions were an absence of AI learning opportunities, the necessity of including AI in medical curricula, optimism towards the future of AI in medicine, and expected challenges related to AI in medical fields.ConclusionMedical students lack access to educational opportunities for AI in medicine; therefore, AI should be included in formal medical curricula in Palestine.

Introduction: “Democratizing” artificial intelligence (AI) in medicine and healthcare is a vague term that encompasses various meanings, issues, and visions. This article maps the ways this term is used in discourses on AI in medicine and healthcare and uses this map for a normative reflection on how to direct AI in medicine and healthcare towards desirable futures. Methods: We searched peer-reviewed articles from Scopus, Google Scholar, and PubMed along with grey literature using search terms “democrat*”, “artificial intelligence” and “machine learning”. We approached both as documents and analyzed them qualitatively, asking: What is the object of democratization? What should be democratized, and why? Who is the demos who is said to benefit from democratization? And what kind of theories of democracy are (tacitly) tied to specific uses of the term? Results: We identified four clusters of visions of democratizing AI in healthcare and medicine: 1) democratizing medicine and healthcare through AI, 2) multiplying the producers and users of AI, 3) enabling access to and oversight of data, and 4) making AI an object of democratic governance. Discussion: The envisioned democratization in most visions mainly focuses on patients as consumers and relies on or limits itself to free market-solutions. Democratization in this context requires defining and envisioning a set of social goods, and deliberative processes and modes of participation to ensure that those affected by AI in healthcare have a say on its development and use.

Contemporary discourse on Artificial Intelligence (AI) in medicine is oft-sensationalised to the point of bearing no resemblance to its everyday impact and potential - either to proselytise it as a saviour or to condemn its perilous, amoral and sprawling reach.This report aims to unravel the paucity of understanding underpinning this hyperbolic duality, whilst addressing the potential clearly defining its ethical use poses to the semi-public healthcare models in Ireland and Europe. The report contrasts the challenge of regulating the breakneck development of AI, with healthcare's necessity for stringent quality control in ethical technological development to ensure patients' well-being.Physical, practical and philosophical approaches to Artificial Intelligence in medicine are explored through Beauchamp and Childress' principles of delivering care with beneficence, non maleficence, justice and autonomy. AI is scrutinised under Kantian deontological, Benthamite utilitarian and Rawlsian perspectives on health justice. Actor Network theory is used to explain sociotechnical interactions governing human stakeholders developing ethical AI.These analyses operate firstly to define AI concisely, then ground it in its contemporary and future functions in healthcare. They highlight the importance of aligning medical AI with accepted ethical standards as a necessity of its integrated use across healthcare. This report concludes that balanced assessment of AI's role in healthcare requires improvement in three areas: improving clarity in definition of AI and its extant remit in medicine; aligning contemporary discourse on AI use with contemporary objective ethical, legal and system frameworks; and clearly identifying for dismissal a number of logical fallacies deliberately sensationalising AI's potential.

Global artificial intelligence (AI) governance must prioritize equity, embrace a decolonial mindset, and provide the Global South countries the authority to spearhead solution creation. Decolonization is crucial for dismantling Western-centric cognitive frameworks and mitigating biases. Integrating a decolonial approach to AI governance involves recognizing persistent colonial repercussions, leading to biases in AI solutions and disparities in AI access based on gender, race, geography, income and societal factors. This paradigm shift necessitates deliberate efforts to deconstruct imperial structures governing knowledge production, perpetuating global unequal resource access and biases. This research evaluates Sub-Saharan African progress in AI governance decolonization, focusing on indicators like AI governance institutions, national strategies, sovereignty prioritization, data protection regulations, and adherence to local data usage requirements. Results show limited progress, with only Rwanda notably responsive to decolonization among the ten countries evaluated; 80% are 'decolonization-aware', and one is 'decolonization-blind'. The paper provides a detailed analysis of each nation, offering recommendations for fostering decolonization, including stakeholder involvement, addressing inequalities, promoting ethical AI, supporting local innovation, building regional partnerships, capacity building, public awareness, and inclusive governance. This paper contributes to elucidating the challenges and opportunities associated with decolonization in SSA countries, thereby enriching the ongoing discourse on global AI governance.

The application of generative artificial intelligence (AI) technology in the healthcare sector can significantly enhance the efficiency of China’s healthcare services. However, risks persist in terms of accuracy, transparency, data privacy, ethics, and bias. These risks are manifested in three key areas: first, the potential erosion of human agency; second, issues of fairness and justice; and third, questions of liability and responsibility. This study reviews and analyzes the legal and regulatory frameworks established in China for the application of generative AI in healthcare, as well as relevant academic literature. Our research findings indicate that while China is actively constructing an ethical and legal governance framework in this field, the regulatory system remains inadequate and faces numerous challenges. These challenges include lagging regulatory rules; an unclear legal status of AI in laws such as the Civil Code; immature standards and regulatory schemes for medical AI training data; and the lack of a coordinated regulatory mechanism among different government departments. In response, this study attempts to establish a governance framework for generative AI in the medical field in China from both legal and ethical perspectives, yielding relevant research findings. Given the latest developments in generative AI in China, it is necessary to address the challenges of its application in the medical field from both ethical and legal perspectives. This includes enhancing algorithm transparency, standardizing medical data management, and promoting AI legislation. As AI technology continues to evolve, more diverse technical models will emerge in the future. This study also proposes that to address potential risks associated with medical AI, efforts should be made to establish a global AI ethics review committee to promote the formation of internationally unified ethical and legal review mechanisms.

During the past several years the application of digital health and artificial intelligence in sleep medicine has been developing at an extremely rapid pace. The diagnosis and treatment of patients with obstructive sleep apnea can be improved by artificial intelligence, facilitating the clinical work of sleep medicine specialists. Technologies based on artificial intelligence are becoming an integral part of the clinical practice of specialists in sleep medicine and ENT specialists. Artificial intelligence in medicine serves to make the right diagnosis, which is the key to proper treatment. From the literature review of scientific articles on artificial intelligence, the authors conclude that its application in sleep medicine can bring many benefits for rapid diagnosis and treatment. Artificial intelligence supports the treatment of obstructive sleep apnea and, even though it demands the right amount of data, which is a hurdle, it will prevent the development of a variety of problems, including severe morning headaches, daytime drowsiness, neurocognitive disorders, cardiovascular and metabolic disorders.