Regulating Artificial Intelligence in Finance

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.

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.

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

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.

Technological developments in medicine have created significant transformations in healthcare services and offered more effective diagnosis and treatment options for patients. Among these advances, artificial intelligence (AI) plays a pivotal role in a variety of medical applications, from disease diagnosis and treatment planning to clinical research and patient care optimization. However, the rapid development of artificial intelligence in medicine also raises ethical challenges and concerns, including patient privacy, data security, inequality and societal impacts. This study examines the potential benefits and risks associated with the global use of artificial intelligence in medicine. The study presents examples and features of global AI-based medical applications, including data-driven diagnosis and treatment, disease prediction and early warning systems, personalized care and treatment planning, drug development and discovery, telemedicine and remote healthcare. Discussions of confidentiality, fairness, integrity, transparency, patient autonomy, responsibility and accountability, change management, social acceptance are emphasized, emphasizing the importance of ethical rules and guidelines in the use of AI in medicine. An analysis of global publication trends in the study of AI and ethics in medicine is also presented, providing insights into the most influential countries and networks of collaboration. As a result, AI has enormous potential in medicine and offers numerous benefits, including better access to healthcare, improved diagnosis and treatment, customized care, resource efficiency, disease prevention and early detection. However, risks related to data security, privacy, inequality and ethical considerations must be addressed. Also, careful management, data security, ethical practices and protection of human factors are vital in leveraging the full potential of AI 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.

In recent years, significant breakthroughs are observed in developing artificial intelligence (AI), which radically affects the most diverse areas of human life and activity. This review article examines the introduction of AI in agriculture using the example of China, which is a leader in the pace of introduction of AI into the national economy and seeks to head off the United States in the overall leadership in the development of AI technologies. Thanks to active work in this direction and significant financial investments in this area, China has managed to transform substantially its agricultural sector. The purpose of the article is to analyze the current trends and opportunities offered by the application of AI in the agricultural sector of the PRC economy. To this end, a series of difficulties that China faces in the development of agriculture is considered, as well as the main currently known areas of application of AI in agriculture and the types of technologies used. Information on Chinese companies using AI technologies in agriculture is summarized, including their specialization, technologies used and benefits gained. Early evidence shows that AI is being applied firstly to improve productivity and manufacturing performance, and secondly to address labor shortages and achieve manufacturing sustainability. Analysis of the situation allows us to conclude that AI can become the main driving force in the development of agriculture.

In this intervention, I examine artificial intelligence (AI) in agriculture through a political ecology lens, analysing how promises of productivity, efficiency, and sustainability take shape across uneven postcolonial landscapes. Building on feminist and critical agrarian perspectives, I focus on the material relations of farming to show that AI in agriculture, often portrayed as immaterial, relies on deeply material infrastructures—sensors, data centres, energy systems, and extractive supply chains. Tracing how AI‐driven digital agriculture and smart agriculture reconfigure relations between farmers, land, and technology, I argue that care for agroecosystems is increasingly displaced by care for digital infrastructures, with dire consequences for the social and ecological relations that sustain rural labour, land, and livelihoods. Focusing on emergent applications of AI in agriculture in the Global South, I show how these technologies consolidate corporate power, erode agrarian knowledges, and reproduce postcolonial inequalities under neoliberal capitalism. I conceptualise AI as a socioecological fix, stressing the need to re‐spatialise and historicise technological change in the context of agrarian change, by attending to its infrastructural and ecological entanglements. I situate this analysis within debates on economies of repair, arguing that understanding AI in agriculture requires attention not only to breakdown and failure but also to the everyday practices of repair—technical, ecological, and social—that sustain digital infrastructures and reveal the uneven burdens of maintaining them across agrarian worlds. This intervention contributes to the Themed Intervention, 'Geographies of Responsibility, Care and Repair in Digital Worlds of AI'.

Research on Artificial Intelligence (AI) in finance has been growing significantly alongside its increasing implementation in the financial sector. This development raises questions about the specific financial areas and AI technology applications that are most frequently explored as research topics within AI in finance. This study aims to address these questions by employing a systematic literature review (SLR) method, analyzing journal articles indexed in Scopus (Q1–Q4) and published between 2020 and 2024. A search conducted using Publish or Perish on the Scopus database identified 496 records, which were subsequently filtered to 94 articles using the PRISMA protocol. The selected articles were examined through bibliometric analysis using VOSviewer, followed by content analysis. The findings reveal that fintech and risk management are the most frequently discussed financial areas in AI in finance research. Moreover, machine learning emerges as the most commonly addressed AI technology application in this domain. Notably, the combination of machine learning and risk management stands out as the most prominent research topic.

The coming of age of artificial intelligence in medicine.

Integrating Artificial Intelligence (AI) in agriculture holds great promise for optimizing resource utilization, improving crop yields, and promoting sustainable practices. However, the responsible adoption of AI in agriculture is critical to addressing ethical challenges, ensuring Transparency and avoiding unintended negative consequences. The responsible adoption of AI in agriculture entails a conscientious and ethical approach to integrating AI into farming practices. This research explores Ethical concerns, Transparency and Sustainable resource management by proposing a novel ETS framework for the responsible adoption of AI in agriculture and a case study of its application to achieve this objective. This framework can be a helpful tool to maximize the benefits of technology while safeguarding ethical, transparent, and sustainable outcomes for all stakeholders and the environment.

PurposeArtificial intelligence (AI) plays a substantial role in many domains, including medical fields. However, we still lack evidence to support whether or not cancer patients will accept the clinical use of AI. This research aims to assess the attitudes of Chinese cancer patients toward the clinical use of artificial intelligence in medicine (AIM), and to analyze the possible influencing factors.Patients and methodsA questionnaire was delivered to 527 participants. Targeted people were Chinese cancer patients who were informed of their cancer diagnosis.ResultsThe effective response rate was 76.3% (402/527). Most cancer patients trusted AIMs in both stages of diagnosis and treatment, and participants who had heard of AIMs were more likely to trust them in the diagnosis phase. When an AIM’s diagnosis diverged from a human doctor’ s, ethnic minorities, and those who had received traditional Chinese medicine (TCM), had never received chemotherapy, were more likely to choose “AIM”, and when an AIM’s therapeutic advice diverged from a human doctor’s, male participants, and those who had received TCM or surgery, were more likely to choose “AIM”.ConclusionMost Chinese cancer patients believed in the AIM to some extent. Nevertheless, most still thought that oncology physicians were more trustworthy when their opinions diverged. Participants’ gender, race, treatment received, and AIM related knowledge might influence their attitudes toward the AIM. Most participants thought AIM would assist oncology physicians in the future, while little really believed that oncology physicians would completely be replaced.

Purpose – This study explores the integration of Artificial Intelligence (AI) in Islamic finance, analyzing global trends, ethical implications, and future research directions. The purpose is to assess AI’s role in enhancing Sharia compliance, regulatory adherence, and operational efficiency in Islamic financial institutions.Methodology – Using a bibliometric analysis approach, this research examines publications from 2010 to 2023 indexed in Scopus and Web of Science. Keywords such as Islamic finance, "artificial intelligence, AI in finance, Sharia compliance, and globalization guided the search. Analytical tools, including VOSviewer and CiteSpace, were employed to visualize publication trends, citation networks, and influential research contributions from key countries like Pakistan, Malaysia, Turkey, Saudi Arabia, Indonesia, Qatar, Iran, and the UAE.Findings – The research indicate that AI significantly enhances financial inclusion, risk assessment, compliance automation, and customer service in Islamic finance. Key research areas highlight AI-driven solutions for Sharia-compliant financial products, ethical considerations, and regulatory frameworks. However, limitations exist, as this study focuses only on English-language publications, potentially omitting critical insights from non-English sources.Implications – This research contributes to the understanding of AI's role in Islamic finance, offering practical implications for financial institutions seeking to integrate AI for efficiency, transparency, and compliance. Originality – It provides an original bibliometric perspective on AI applications in Islamic finance, underscoring the sector’s potential for innovation and sustainable growth in a globalized financial landscape.

The transformative impact of artificial intelligence (AI) in finance and banking is pro7 found, revolutionizing the way financial institutions operate, interact with customers, and make 8 decisions. This article explores the technological landscape that has paved the way for AI adoption, including falling data storage costs, data availability, advancements in machine learning, cost reduction, regulatory compliance, competitive advantage, risk management, customer experience, fraud detection, increased connectivity, and rapid advances in AI technologies. It discusses how AI is enhancing customer support, improving security through fraud detection algorithms, and enhancing credit scoring accuracy through machine learning. The value creation potential of AI in banking includes unlocking up to $1 trillion of incremental value annually through personalized services, cost reduction via automation, and uncovering new opportunities. Additionally, it provides examples of AI applications in banking, including personalization, automation, and insight extraction, showcasing how AI is transforming the industry. Despite these advancements, the article acknowledges challenges such as a lack of clear strategy, legacy systems, and fragmented data assets and proposes solutions like promoting a growth mindset and responsible AI deployment. It also addresses current issues in the banking sector, such as cyberattacks, voice cloning, and fraud, emphasizing the importance of AI in addressing these challenges. Overall, the article highlights the transformative potential of AI in finance and banking, urging banks to embrace an AI-first mindset for sustained growth and innovation.

Artificial intelligence (AI) has become an increasingly important tool in agriculture, providing farmers with innovative solutions to improve productivity, efficiency, and sustainability. Using AI-powered tools such as drones, sensors, and machine learning algorithms, farmers can gather and analyze data about soil health, crop growth, and weather patterns to make informed decisions and optimize their operations. AI has the potential to transform agriculture by enabling more precise and targeted applications of fertilizers, pesticides, and other inputs, reducing waste, and increasing yields. It also has the potential to reduce the environmental impact of agriculture by minimizing the use of harmful chemicals and promoting sustainable farming practices. Additionally, AI can assist in the automation of tedious and repetitive tasks, freeing up farmers to focus on more strategic decision-making and higher-value activities. This can also help to address labor shortages in the agriculture sector, particularly in countries with aging populations or where traditional agriculture work is seen as less desirable. Overall, the use of AI in agriculture has the potential to revolutionize the industry, making it more efficient, sustainable, and profitable while also ensuring that we can continue to feed a growing global population in a responsible and sustainable manner. Keyword: Artificial Intelligence, AI in Agriculture, AI and Agriculture, Sustainable Farming, AI application in Agriculture