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This paper focuses on the opportunities and challenges associated with the use of artificial intelligence (AI) in academic library operations. In the quest to render fast, effective and efficient services, academic libraries have adopted different technologies in the past. Artificial intelligence technologies is the latest among the technologies currently being introduced in libraries. The technology which is considered an intelligent system, come in the form of robots and expert systems which have natural language processing, machine learning and pattern recognition capabilities. This paper examined the features of AI, the application of AI to library operations, examples of academic libraries with AI technologies in Sub-Saharan Africa, the need for AI in libraries and the challenges associated with the adoption of AI in libraries. The study concluded that AI holds a lot of prospects for the improvement of information services delivery in African academic libraries. Consequently, its adoption is a sinequanon to delivering robust library services in the Fourth Industrial Revolution (4IR). [ABSTRACT FROM AUTHOR]

Computers in education, along with soft-computing technology applications, have revolutionised global interconnectedness and the need for a well-educated workforce. Many studies worldwide explore technology in education, often relying on systematic reviews, though concerns about selection bias have emerged. This article takes a different approach, employing bibliometric analysis to delve into the trends, key authors, institutions, and themes of soft-computing technology applications in education (SCTAE) research in Africa. Initially, 7,435 papers were downloaded from Scopus and then narrowed down to 1,358 using the PRISMA model and defined criteria. Utilising the VOSViewer text mining tool, the article maps out prolific authors, institutions, and thematic networks. It provides detailed findings and outlines opportunities, challenges, and future research prospects in SCTAE in the African context.

Due to the recent developments in the delivery of teaching-learning processes when COVID-19 hit the world with a health crisis and pandemic, it is crucial to look into the quality of courses delivered via online means or through distance education modality. This paper examines implementation and application of quality assurance (QA) landscape in Distance Education (DE). A documentary review using bibliographic inquiry is used as a methodology approach to gather relevant information to address the study questions. Previous studies on QA in DE are examined and arranged into themes using thematic analysis. Findings revealed that most of the literature on QA in DE in Africa and Asia based on the reviewed Open and Distance Learning (ODL) institutions are basically dealing with frameworks, outcomes and performance, instructional design, student services and challenges as well as parity in terms of quality with the traditional institutions. Therefore, this paper concludes that more studies are needed for QA in DE to match the post-COVID-19 trends on improving QA. This implies that there is a need to expand research on QA in DE to include areas of artificial intelligence. [ABSTRACT FROM AUTHOR]

This study explores the transformative potential of fourth industrial revolution (called Industry 4.0) technologies in the context of poverty elimination, with a particular focus on Africa. Given the multidimensional nature of poverty, which spans economic, social, and environmental aspects, there is a critical need for innovative and sustainable solutions. This paper presents a comprehensive literature review to identify how recent advancements, such as artificial intelligence (AI), the Internet of Things (IoT), robotics, blockchain, big data, and 5G can be harnessed to address various facets of poverty. Drawing on insights from existing research and expert opinions, we propose a conceptual framework that integrates these technologies with strategic policy interventions, infrastructure development, and capacity building. The paper proposes a framework that illustrates the prerequisite requirements before adopting Industry 4.0 technologies in poverty elimination efforts. This framework aims to ensure that the benefits of technological innovations are accessible to the most vulnerable populations, thereby contributing to the broader goals of socioeconomic development and poverty reduction. The work shows that while Industry 4.0 presents a critical opportunity for sustainable development and poverty elimination in Africa, it needs to have essential capacities to optimize the use of observations, visualizations, and mindset management before or when adopting the first stage of Industry 4.0 solutions for poverty elimination. [ABSTRACT FROM AUTHOR]

A Clinical Data Repository (CDR) is a dynamic database capable of real-time updates with patients' data, organized to facilitate rapid and easy retrieval. CDRs offer numerous benefits, ranging from preserving patients' medical records for follow-up care and prescriptions to enabling the development of intelligent models that can predict, and potentially mitigate serious health conditions. While several research works have attempted to provide state-of-the-art reviews on CDR design and implementation, reviews from 2013 to 2023 cover CDR regulations, guidelines, standards, and challenges in CDR implementation without providing a holistic overview of CDRs. Additionally, these reviews need to adequately address critical aspects of CDR; development and utilization, CDR architecture and metadata, CDR management tools, CDR security, use cases, and artificial intelligence (AI) in CDR design and implementation. The collective knowledge gaps in these works underscore the imperative for a comprehensive overview of the diverse spectrum of CDR as presented in the current study. Existing reviews conducted over the past decade, from 2013 to 2023 have yet to comprehensively cover the critical aspects of CDR development, which are essential for uncovering trends and potential future research directions in Africa and beyond. These aspects include architecture and metadata, security and privacy concerns, tools employed, and more. To bridge this gap, in particular, this study conducts a comprehensive systematic review of CDR, considering critical facets such as architecture and metadata, security and privacy issues, regulations guiding development, practical use cases, tools employed, the role of AI and machine learning (ML) in CDR development, existing CDRs, and challenges faced during CDR development and deployment in Africa and beyond. Specifically, the study extracts valuable discussions and analyses of the different aspects of CDR. Key findings revealed that most architectural models for CDR are still in the theoretical phase, with low awareness and adoption of CDR in healthcare environments, susceptibility to several security threats, and the need to integrate federated learning in CDR systems. Overall, this paper would serve as a valuable reference for designing and implementing cutting-edge clinical data repositories in Africa and beyond. [ABSTRACT FROM AUTHOR]

Due to its enormous potential, artificial intelligence (AI) can transform healthcare on a seemingly infinite scale. However, as we continue to explore the immense potential of AI, it is vital to consider the ethical concerns associated with its development and deployment. One specific concern that has been flagged in the literature is the responsibility gap (RG) due to the introduction of AI in healthcare. When the use of an AI algorithm or system results in a negative outcome for a patient(s), to whom can or should responsibility for that outcome be assigned? Although the concept of the RG was introduced in Anglo-American and European philosophy, this paper aims to broaden the debate by providing an Ubuntu-inspired perspective on the RG. Ubuntu, deeply rooted in African philosophy, calls for collective responsibility, and offers a uniquely forward-looking approach to address the alleged RG caused by AI in healthcare. An Ubuntu-inspired perspective can serve as a valuable guide and tool when addressing the alleged RG. Incorporating Ubuntu into the AI ethics discourse can contribute to a more ethical and responsible integration of AI in healthcare., (© 2024. The Author(s).)

The power of converging and coupling Artificial Intelligence (AI), Fifth Generation Cellular(5G), Internet of Things (IoT), Big Data analytics, and Cyber-security has been touted to be the next revolutionary technologies. If applied correctly, the synergy of the aforementioned technologies is poised to unravel, transform and yield unparalleled economic efficiency and productivity globally across various sectors. It is important to note that each of these technologies has an enormous impact as a standalone in various industries/sectors but when properly combined, the technologies will create an array of opportunities that hugely impact widely all sectors of global economy. This paper will carry out a systematic review of applications of the above-mentioned technologies towards improving Africa's multi-sectoral economy, and their global impact. The findings of this paper will guide the development of techniques of converging various technologies towards enhancing efficiency and impacting sustainability in virtually all sectors of human endeavors. [ABSTRACT FROM AUTHOR]

Context and background Multidimensional Poverty (MP) considers poverty in multiple dimensions of deprivations such as health, education, energy, the standard of living and access to basic services. MP remains a major challenge in Africa, with a large proportion of the population living in MP. According to United Nations Development Programme (UNDP), Africa has shown the highest Multidimensional Poverty Index (MPI) having over 40% of its population living in MP. Goal and Objectives: This paper is a review, aimed at assessing the potential of the integration of satellite and Artificial Intelligence (AI) in mapping MP, with a specific focus on Africa. Methodology: Based on the reviews of past studies, the combination of satellite data such as nighttime light, daytime satellite imagery and high-resolution settlement data in combination with techniques such as field surveys, statistical correlation models (transfer learning) and AI (deep learning) has been applied in mapping MP. Results: The findings from studies show that the combination of satellite data and AI has the capability of providing more accurate and granular MP maps, compared to the traditional approach. Again, this paper explains the concept of MP with a specific focus on Africa and presents a map depicting the current MPI in African countries. Finally, pitfalls especially in the accuracy, granularity and frequency of MP data were identified. Consequently, the satellite and AI approaches are recommended for more accurate, frequent, cost-effective and granular data, required in mapping poverty and design of interventions that effectively address the needs of the vulnerable populations in Africa. [ABSTRACT FROM AUTHOR]

Context and background The incorporation of artificial intelligence (AI) into businesses has become increasingly widespread in recent years, particularly in developed countries, fundamentally reshaping the landscape of entrepreneurship and innovation. AI presents numerous opportunities for small businesses, including those involved in cross-border trade. It has the potential to transform various aspects of business operations, such as customer interactions, marketing research, understanding consumer behavior, optimizing routes, and forecasting sales and marketing trends. Additionally, AI can contribute to the formalization of business processes, and assist these businesses in adapting more effectively to the ever-changing dynamics of the market. However, the successful implementation of AI necessitates a meticulous assessment of AI applications to ensure alignment with the objectives and resources of each individual small business. Goal and Objectives: The primary focus of this paper is to explore the potential of Artificial Intelligence in enhancing Africa's cross-border trade. The paper will address the following research questions: What are the effective applications of artificial intelligence in cross-border informal trade in Africa, and what are the limitations? What does the future hold for African countries in terms of utilizing artificial intelligence for cross-border trade? How can African informal cross-border traders and their stakeholders benefit from incorporating artificial intelligence in their activities? Methodology: The research is purely empirical research based on observation and measurement of phenomena, as directly experienced by the researcher. Results: AI can be effectively applied by Informal Cross-Border Traders (ICBT) to enhance their businesses and enhance competitiveness. There are several AI applications accessible to ICBT within their operational context. Although the adoption and utilization of AI in Africa are still in their infancy, there is considerable promise for the future. Africans must address the challenges hindering the adoption and utilization of AI, as technology is advancing rapidly, and opportunities await those who embrace it. [ABSTRACT FROM AUTHOR]

African public administrations are in an especially difficult position with respect to the adoption of artificial intelligence (AI), in both how they harness its benefits but also in managing potential harms. Through a narrative review of existing research, this paper synthesises findings from previous digital government implementations on the African continent, and considers the implications for AI use. The analysis is centred on the way public officials are bypassed or empowered to work with emerging technologies, and the influence of public service values on their adoption. Four concerns emerge as relevant: Integrity of recommendations provided by decision-support systems, including how they are influenced by local organisational practices and the reliability of underlying infrastructures; Inclusive decision-making that balances the (assumed) objectivity of data-driven algorithms and the influence of different stakeholder groups; Exception and accountability in how digital and AI platforms are funded, developed, implemented and used; and an expectation of Complete understanding of people and events through the integration of traditionally dispersed data sources and systems, and how policy actors seek to mitigate the risks associated with this aspiration. [ABSTRACT FROM AUTHOR]

AI technology is capturing the African imaginations as a gateway to progress and prosperity. There is a growing interest in AI by different actors across the continent including scientists, researchers, humanitarian and aid organizations, academic institutions, tech start-ups, and media organizations. Several African states are looking to adopt AI technology to capture economic growth and development opportunities. On the other hand, African researchers highlight the gap in regulatory frameworks and policies that govern the development of AI in the continent. They argue that this could lead to AI technology exacerbating problems of inequalities and injustice in the continent. However, most of the literature on AI ethics is biased toward Euro-American perspectives and lack the understanding of how AI development is apprehended in the Global South, and particularly Africa. Drawing on the case study of the first African Master's in Machine Intelligence program, this paper argues for looking beyond the question of ethics in AI and examining AI governance issues through the analytical lens of the raciality of computing and the political economy of technoscience to understand AI development in Africa. By doing so, this paper seeks a different theorization for AI ethics from the South that is based on lived experiences of those in the margins and avoids the framings of technological futures that simplistically pathologize or celebrate Africa. [ABSTRACT FROM AUTHOR]

Large scale databases are critical for helping scientists decipher long-term patterns in human evolution. This paper describes the conception and development of such a research database and illustrates how big data can be harnessed to formulate new ideas about the past. The Role of Culture in Early Expansions of Humans (ROCEEH) is a transdisciplinary research center whose aim is to study the origins of culture and the multifaceted aspects of human expansions across Africa and Eurasia over the last three million years. To support its research, the ROCEEH team developed an online tool named the ROCEEH Out of Africa Database (ROAD) and implemented its web-based applications. ROAD integrates geographical data as well as archaeological, paleoanthropological, paleontological and paleobotanical content within a robust chronological framework. In fact, a unique feature of ROAD is its ability to dynamically link scientific data both spatially and temporally, thereby allowing its reuse in ways that were not originally conceived. The data stem from published sources spanning the last 150 years, including those generated by the research team. Descriptions of these data rely on the development of a standardized vocabulary and profit from online explanations of each table and attribute. By synthesizing legacy data, ROAD facilitates the reuse of heritage data in novel ways. Database queries yield structured information in a variety of interoperable formats. By visualizing data on maps, users can explore this vast dataset and develop their own theories. By downloading data, users can conduct further quantitative analyses, for example with Geographic Information Systems, modeling programs and artificial intelligence. In this paper, we demonstrate the innovative nature of ROAD and show how it helps scientists studying human evolution to access datasets from different fields, thereby connecting the social and natural sciences. Because it permits the reuse of "old" data in new ways, ROAD is now an indispensable tool for researchers of human evolution and paleogeography. [ABSTRACT FROM AUTHOR]

Background: There have been several proposals by researchers for the introduction of Artificial Intelligence (AI) technology due to its promising role in radiotherapy practice. However, prior to the introduction of the technology, there are certain general recommendations that must be achieved. Also, the current challenges of AI must be addressed. In this review, we assess how Africa is prepared for the integration of AI technology into radiotherapy service delivery., Methods: To assess the readiness of Africa for integration of AI in radiotherapy services delivery, a narrative review of the available literature from PubMed, Science Direct, Google Scholar, and Scopus was conducted in the English language using search terms such as Artificial Intelligence, Radiotherapy in Africa, Machine Learning, Deep Learning, and Quality Assurance., Results: We identified a number of issues that could limit the successful integration of AI technology into radiotherapy practice. The major issues include insufficient data for training and validation of AI models, lack of educational curriculum for AI radiotherapy-related courses, no/limited AI teaching professionals, funding, and lack of AI technology and resources. Solutions identified to facilitate smooth implementation of the technology into radiotherapy practices within the region include: creating an accessible national data bank, integrating AI radiotherapy training programs into Africa's educational curriculum, investing in AI technology and resources such as electronic health records and cloud storage, and creation of legal laws and policies to support the use of the technology. These identified solutions need to be implemented on the background of creating awareness among health workers within the radiotherapy space., Conclusion: The challenges identified in this review are common among all the geographical regions in the African continent. Therefore, all institutions offering radiotherapy education and training programs, management of the medical centers for radiotherapy and oncology, national and regional professional bodies for medical physics, ministries of health, governments, and relevant stakeholders must take keen interest and work together to achieve this goal., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Associazione Italiana di Fisica Medica e Sanitaria. Published by Elsevier Ltd. All rights reserved.)

The Nguni term inkaba refers both to the umbilical cord and the navel. In this paper, we use the idea of the umbilical cord to explain an enduring intellectual connection that many African scholars feel towards African epistemes. By drawing on the term inkaba, the paper explores the idea of "umbilical sense" as an intellectual instinct that one develops in relation to a range of knowledges that have been marginalised. This sense speaks to the intellectual orientation drawn from growing and developing as persons within indigenous communities. We argue that the desire and ability to respond to the conceptual shortcomings in Western academe stems from an umbilical sense, or a navel sense – ukuzwa ngenkaba in Nguni languages. Drawing on African writers such as Mazisi Kunene and Ifeanyi Menkiti, we propose that the acquisition of knowledge forms part of an African conception of personhood that links one to many corpuses of historical knowledges. Knowledge thus forms part of how we emerge as persons and, in turn, make other persons. We explore the implications of our concept for current debates on the decolonisation of knowledge and the meaning of post-human knowledge with the emergence of artificial intelligence. [ABSTRACT FROM AUTHOR]

Changes in average wage are usually attributed to technological change in the past industrial revolutions. With the emergence and gradual explosion of artificial intelligence (AI) in the 4th industrial revolution, this paper applies panel vector autoregressive technique, with annual data from the World Bank and global economy from 2004 to 2017, to examine the effect of artificial intelligence on average wages in Southern Africa. Findings from the study show that artificial intelligence has a significant negative relationship with average wages but is positively associated with gross domestic product per capita (GDPC), unemployment and inflation. The study also finds inflation and GDPC to be positively associated with average wage. Based on the finding, policy direction focusing on wage stabilisation, redistribution of income, advance learning and skill development training that promote competitiveness to computerisation is recommended. [ABSTRACT FROM AUTHOR]

Solar energy-based technologies have developed rapidly in recent years, however, the inability to appropriately estimate solar energy resources is still a major drawback for these technologies. In this study, eight different artificial intelligence (AI) models namely; convolutional neural network (CNN), artificial neural network (ANN), long short-term memory recurrent model (LSTM), eXtreme gradient boost algorithm (XG Boost), multiple linear regression (MLR), polynomial regression (PLR), decision tree regression (DTR), and random forest regression (RFR) are designed and compared for solar irradiance prediction. Additionally, two hybrid deep neural network models (ANN-CNN and CNN-LSTM-ANN) are developed in this study for the same task. This study is novel as each of the AI models developed was used to estimate solar irradiance considering different timesteps (hourly, every minute, and daily average). Also, different solar irradiance datasets (from six countries in Africa) measured with various instruments were used to train/test the AI models. With the aim to check if there is a universal AI model for solar irradiance estimation in developing countries, the results of this study show that various AI models are suitable for different solar irradiance estimation tasks. However, XG boost has a consistently high performance for all the case studies and is the best model for 10 of the 13 case studies considered in this paper. The result of this study also shows that the prediction of hourly solar irradiance is more accurate for the models when compared to daily average and minutes timestep. The specific performance of each model for all the case studies is explicated in the paper. [ABSTRACT FROM AUTHOR]

This paper offers an overview of some of the highlights of the 2023 NISO Plus Annual Conference that was held virtually from February 14–February 16, 2023. This was the fourth such conference and the third to be held in a completely virtual format due to the global impact of COVID-19. These conferences have resulted from the merger of NISO and the National Federation of Abstracting and Information Services (NFAIS) in June 2019, replacing the NFAIS Annual Conferences and offering a new, more interactive format. As with prior years, there was no general topical theme (although the importance of metadata was a common thread throughout), but there were topics of interest for everyone working in the information ecosystem - from the practical subjects of standards, archiving, digital preservation, Open Science, and Open Access. With speakers and attendees from around the world and across multiple time zones and continents, it truly was a global conversation! [ABSTRACT FROM AUTHOR]

The main purpose of this paper is to investigate the level of adoption of artificial intelligence(AI) to support library services delivery in university libraries in Africa. Qualitative research method was adopted to collect data. A preliminary survey of 102 university libraries in English speaking countries in Africa was conducted to identify the university libraries that have adopted AI in their libraries. Content analysis was used to analyse the responses. The study found that the only few university libraries in Africa have adopted AI technologies such as Chatbot, ChatGPT, LibKey from ThirdIron, robots, RFID technology and Grammarly. These AI technologies are used to render different library services like answering of directional and ready reference questions posed by library users, serving as a knowledge base for cataloguing information of library materials, self-check-out machine for books, used as a marketing tool for the library, tool for statistics evaluation and recommendations, assisting in the charging and discharging of library materials, etc. Lack of funds to acquire the AI tools, training of librarians, and lack of full knowledge of AI were the most mentioned challenges associated with adoption of AI in the libraries. The study recommended a formulation of policy to guide the adoption of new technologies such as AI, and training and re-training of librarians through workshops equip librarians with skills needed to effectively use the AI technologies. [ABSTRACT FROM AUTHOR]

The role of libraries in preparing students to thrive during their studies and innovate after university is growing in importance. Information is more easily accessible through digital channels and is increasingly abundant. Generative Artificial intelligence (AI) adds to this reality and increases the need for digitally driven information literacy skills. This paper aims to guide librarians by discussing the digitalization of information creation, retrieval, and use. It recommends the training of both digital and information literacy for students. Librarians are called upon to provide clear guidelines to their universities to steer the use of generative AI. The implications of digital information sources and generative AI are discussed with the role of librarians in context. Information literacy and digital literacy are related. Academic libraries should include digital topics in information literacy training. Information literacy should be trained at the start of university education and before students begin dissertation writing. University libraries should propose guidelines for the use of generative artificial intelligence tools by students. [ABSTRACT FROM AUTHOR]

Malaria is a significant health concern worldwide, particularly in Africa where its prevalence is still alarmingly high. Using artificial intelligence algorithms to diagnose cells with malaria provides great convenience for clinicians. In this paper, a densely connected convolutional dynamic learning network (DCDLN) is proposed for the diagnosis of malaria disease. Specifically, after data processing and partitioning of the dataset, the densely connected block is trained as a feature extractor. To classify the features extracted by the feature extractor, a classifier based on a dynamic learning network is proposed in this paper. Based on experimental results, the proposed DCDLN method demonstrates a diagnostic accuracy rate of 97.23%, surpassing the diagnostic performance than existing advanced methods on an open malaria cell dataset. This accurate diagnostic effect provides convincing evidence for clinicians to make a correct diagnosis. In addition, to validate the superiority and generalization capability of the DCDLN algorithm, we also applied the algorithm to the skin cancer and garbage classification datasets. DCDLN achieved good results on these datasets as well, demonstrating that the DCDLN algorithm possesses superiority and strong generalization performance. [ABSTRACT FROM AUTHOR]

This article presents the results of a bibliometric review of the study of real estate bubbles in the scientific literature indexed in Web of Science and Scopus, from 2007 to 2022. The analysis was developed using a sample of 2276 documents, which were reviewed in R software and analyzed with the assistance of the Bibliometrix package of the same software. The results indicate that there has been considerable productivity on the topic of real estate bubbles since 2007, with an emphasis on housing price formation processes and the social effects when bubbles burst. The authors found that there were not many case studies located in Latin America or Africa, nor were there approaches with advanced predictive modeling techniques using machine learning or artificial intelligence. The article provides an understanding of the state of the art in real estate bubble research and situates new research in front of the influential literature previously published. [ABSTRACT FROM AUTHOR]

Through document analysis and interviews, this paper examines the patent narratives of Transsion, a Chinese company dominating Africa's smartphone market and a leading innovator in facial recognition technologies (FRTs) optimized for darker skin tones. We identify two major narratives concerning Transsion's FRT patenting practice. First is 'empowerment', through which Transsion argues that there are 'blind spots' in conventional AI technologies and presents its AI camera as a remedy and an empowerment tool for dark-skinned users by 'seeing' their beauty. Second is the 'warfare' narrative, which is shaped by the heightening market competition and accelerating patent races among Chinese phone makers. As the battle for tech supremacy intensifies in Africa, Transsion expresses a strong sense of crisis and considers its FRT patents as 'weapons of competition' in preparation for a future smartphone warfare in Africa. This study makes two contributions. Empirically, through analyzing patents, we examine a relatively less-known Chinese tech company that has tremendous impact in the Global South. Theoretically, we interrogate the possibility of algorithmic empowerment against racist AI and technological independence through patents, although developing AI as weaponry, in the China-Africa context, also hampers the politics of decolonization. [ABSTRACT FROM AUTHOR]

Globally, newsrooms are adopting various forms of artificial intelligence (AI) as part of the newsgathering, production and distribution process. Western countries and China, in particular, are leading the innovation of AI in newsrooms—testing technologies that include machine learning, automated content creation and moderation and speech-to-text programs. The adoption of AI in newsrooms, and research of its impact, has predominantly focused on Western countries. Although some African countries have begun incorporating AI into sectors such as government agencies, health, education and finance, little information exists about how newsrooms on the continent are using AI technologies. In this paper, we propose a research agenda to advance the scholarship and understanding of the use of AI in African newsrooms and its implications for journalism in Africa. Through a systematic search of databases, we examine the current use of AI in newsrooms in Africa, along with a review of the opportunities and challenges it presents for journalists. We also present a comprehensive examination and discussion about the theoretical frameworks being used to examine newsroom processes—and the opportunities to adapt those theories to analyse the use of AI in African-led scholarship. Also included in the study are recommendations for addressing methodological challenges related to the use of AI in newsrooms. [ABSTRACT FROM AUTHOR]

This article introduces readers to the special issue on Selected Issues in the Ethics of Artificial Intelligence. In this paper, I make a case for a wider outlook on the ethics of AI. So far, much of the engagements with the subject have come from Euro-American scholars with obvious influences from Western epistemic traditions. I demonstrate that socio-cultural features influence our conceptions of ethics and in this case the ethics of AI. The goal of this special issue is to entertain more diverse views, particularly those from Africa; it brings together six articles addressing pertinent issues in the ethics of AI. These articles address topics around artificial moral agency, patiency, personhood, social robotics, and the principle of explicability. These works offer unique contributions for and from an African perspective. I contend that a wider engagement with the ethics of AI is worthwhile as we anticipate a global deployment of artificial intelligence systems. [ABSTRACT FROM AUTHOR]

Developing and implementing artificial intelligence (AI) systems in an ethical manner faces several challenges specific to the kind of technology at hand, including ensuring that decision-making systems making use of machine learning are just, fair, and intelligible, and are aligned with our human values. Given that values vary across cultures, an additional ethical challenge is to ensure that these AI systems are not developed according to some unquestioned but questionable assumption of universal norms but are in fact compatible with the societies in which they operate. This is particularly pertinent for AI research and implementation across Africa, a ground where AI systems are and will be used but also a place with a history of imposition of outside values. In this paper, we thus critically examine one proposal for ensuring that decision-making systems are just, fair, and intelligible—that we adopt a principle of explicability to generate specific recommendations—to assess whether the principle should be adopted in an African research context. We argue that a principle of explicability not only can contribute to responsible and thoughtful development of AI that is sensitive to African interests and values, but can also advance tackling some of the computational challenges in machine learning research. In this way, the motivation for ensuring that a machine learning-based system is just, fair, and intelligible is not only to meet ethical requirements, but also to make effective progress in the field itself. [ABSTRACT FROM AUTHOR]

The objective of the study is to conduct an exploratory review of the COVID-19 pandemic by focusing on the theme of COVID-19 pandemic morbidity and mortality considering the dynamics of Artificial Intelligence and Quality of life (QOL). The methods used in this research paper include a review of literature, anecdotal evidence, and reports on the morbidity of COVID-19, including the scope of its devastating effects in different countries such as the US, Africa, UK, China, and Brazil, among others. The findings of this study suggested that the devastating effects of the Coronavirus are felt across different vulnerable populations. These include the elderly, front-line workers, marginalised communities, visible minorities, and more. The challenge in Africa is especially daunting because of inadequate infrastructure, financial, and human resources, among others. Besides, AI technology is being successfully used by scientists to enhance the development process of vaccines and drugs. However, its usage in other stages of the pandemic has not been adequately explored. Ultimately, it has been concluded that the effects of the COVID-19 are producing unprecedented and catastrophic outcomes in many countries. With a few exceptions, the common and current intervention approach is driven by many factors, including the compilation of relevant reliable and compelling data sets. On a positive note, the compelling trailblazing and catalytic contributions of AI towards the rapid discovery of COVID-19 vaccines are a good indication of future technological innovations and their effectiveness. [ABSTRACT FROM AUTHOR]

This review presents a comprehensive examination of recent advancements in utilizing multi-date satellite data to analyze spatial and temporal variations in seasonal and inter-annual surface water dynamics within arid environments of Africa. Remote sensing offers continuous, precise, and long-term datasets for surface water research. Various sensors with differing spatial resolutions are discussed, with high-resolution multispectral sensors providing superior spatial resolution but at higher costs. Conversely, dualsensor approaches, incuding optical sensors (Sentinel-2 and Landsat), radar satellites (Sentinel-1 and RADARSAT) and UAVs were investigated. The review further examines the efficiency and applicability of traditional algorithms such as the modified normalized difference water index (MNDWI), normalized difference water index (NDWI), and automated water extraction index (AWEI) in detecting and delineating surface water resources. Additionally, machine learning (ML) algorithms, including support vector machines (SVM), Random Forest (RF), deep learning and emerging methodologies like recurrent tranformer networks, have been explored. Therefore, we recommend that future research endeavours focus on leveraging high-resolution satellite imagery and integrating physical models with deep learning techniques, artificial intelligence, and online big data processing platforms to improve surface water mapping capabilities. [ABSTRACT FROM AUTHOR]

Artificial intelligence chatbots are one of the innovative examples of machine learning technology today. The inception of this technology has escalated several discussions on high-tech plagiarism in academic publishing. As the world gets overwhelmed with issues regarding academic integrity, an important task facing local African journals is "How equipped are their systems to ensure academic publishing integrity in the face of high-tech plagiarism?" This question demands a multifaceted call to action and a serious look at the current challenges facing local journals. [ABSTRACT FROM AUTHOR]

The increasing level of automation in the fourth industrial revolution has received a global interest in research and political landscape in recent years. While some research advances have been made in the developed world with regard to its implications for unemployment, next to no study has so far sought to establish if there is any statistical relationship between automation and unemployment in developing countries where unemployment is very high. The purpose of this study is to examine the effect of automation on unemployment in Southern Africa. Findings from the study show that automation has a significant positive relationship with unemployment rate. The study also finds foreign direct investment and business cycle (higher production output change) to be negatively associated with unemployment. Based on these findings, it is recommended that proper investment in learning and skill development training aimed at making individuals more competitive with regard to automation be pursued to increase the prospect of employment. [ABSTRACT FROM AUTHOR]

This paper investigates suitability of supervised machine learning classification methods for classification of biomes using pollen datasets. We assign modern pollen samples from Africa and Arabia to five biome classes using a previously published African pollen dataset and a global ecosystem classification scheme. To test the applicability of traditional and machine-learning based classification models for the task of biome prediction from high dimensional modern pollen data, we train a total of eight classification models, including Linear Discriminant Analysis, Logistic Regression, Naïve Bayes, K-Nearest Neighbors, Classification Decision Tree, Random Forest, Neural Network, and Support Vector Machine. The ability of each model to predict biomes from pollen data is statistically tested on an independent test set. The Random Forest classifier outperforms other models in its ability correctly classify biomes given pollen data. Out of the eight models, the Random Forest classifier scores highest on all of the metrics used for model evaluations and is able to predict four out of five biome classes to high degree of accuracy, including arid, montane, tropical and subtropical closed and open systems, e.g. forests and savanna/grassland. The model has the potential for accurate reconstructions of past biomes and awaits application to fossil pollen sequences. The Random Forest model may be used to investigate vegetation changes on both long and short time scales, e.g. during glacial and interglacial cycles, or more recent and abrupt climatic anomalies like the African Humid Period. Such applications may contribute to a better understanding of past shifts in vegetation cover and ultimately provide valuable information on drivers of climate change. [ABSTRACT FROM AUTHOR]

China is the largest foreign information and communications technology (ICT) investor in Africa, and Chinese telecommunication companies including Huawei, CloudWalk, and Transsion have brought a range of artificial intelligence (AI) technology to Africa, from face recognition to smart cities. Existing analysis of China's AI impact in Africa, however, habitually falls into a Cold War-style narrative of 'authoritarianism' against the backdrop of 'liberal democracy.' This article calls for going beyond the oversimplified paradigm and proposes to examine the new phenomenon through a more sophisticated and critical lens. First, I show how (English language) media's investigations and academia's analysis of China's AI policy and its global impact (particularly in Africa) usually concentrate on a deep concern about the exportation of authoritarianism from Beijing. The article then demonstrates that the 'authoritarianism vs liberal democracy' model is problematic, if not outdated, and particularly insufficient to capture the multi-layered and overlapping realities of authoritarian and democratic moves by states around the world and the distinguishing properties of AI technologies. Based on a critical appraisal of the notions of surveillance capitalism and data colonialism and the recent empirical evidence from Africa in the third and fourth sections, I propose that these two concepts are more productive analytical frameworks for understanding what is going on in Africa related to the presence of China's AI, as well as for updating and moving forward the debates about whether China is colonizing Africa. [ABSTRACT FROM AUTHOR]

This study investigates the impact of ChatGPT on Chinese L2 writing proficiency among African students, aligning with the Sustainable Development Goals (SDGs) for quality education and reduced inequalities. Employing a mixed-methods approach, the research involved 46 participants to quantitatively assess improvements in syntactic and lexical complexity, accuracy, and fluency. Qualitative insights were garnered through semi-structured interviews, revealing positive perceptions of ChatGPT in enhancing writing skills, yet highlighting challenges like technological access and economic constraints. The findings suggest that while ChatGPT effectively enhances Chinese L2 proficiency, its integration in African educational contexts requires addressing infrastructural and pedagogical barriers. This study contributes to the discourse on Artificial Intelligence in language education and its role in advancing African sustainable educational practices. [ABSTRACT FROM AUTHOR]

Malaria is a life-threatening disease caused by Plasmodium parasites that enter the bloodstream via insect vectors, such as mosquitos. The disease has devastated parts of Africa and the wider human population throughout human periods and thus remains a significant global health challenge due to the difficulty in the procurement of a vaccine and the nature of its spread. While many have turned towards preventative methods, the first thing that must happen is a timely and accurate diagnosis of malaria. This is crucial for the effective management and control of the disease. However, the current diagnostic methods have various limitations, including cost, availability, and accuracy. This review aims to unmask the challenges of malaria diagnosis by exploring existing diagnostic techniques and shedding light on emerging technologies that show promise in improving malaria detection. Furthermore, it examines the potential of innovative approaches, such as artificial intelligence (AI) and molecular diagnostics, in transforming malaria diagnosis and contributing to global efforts toward malaria elimination. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) tools are now used in our daily lives. This study aimed to explore the level of awareness, perceived benefits, threats, attitudes, and level of satisfaction with AI tools among individuals within higher education in Asia and Africa. A cross-sectional study was conducted in August 2023. Snowball sampling was used with a convenience sample of 815 highly educated Asian and African participants from 11 countries. About 56% of participants have Bachelor's degrees. 312 participants (38%) were unaware of AI tools and AI tools were used rarely by 316 (63%) of 503 participants who were aware of them. ChatGPT is the most popular of this study's AI tools (N=405, 81%). Participants who used AI tools reported greater benefits than those who did not (p < 0.05). Of the four educational groups, those with a Master's degree reported a higher AI tool threat than those with a Diploma (P < 0.05). Female participants reported more AI-related threats than males (P < 0.05). In conclusion, this research is important because of the rapid development of modern technology around the world. Nevertheless, Asia and Africa still lag behind developed nations in AI technology awareness. [ABSTRACT FROM AUTHOR]

Introduction: Malaria is one of the most prevalent infectious diseases in subSaharan Africa, with 247 million cases reported worldwide in 2021 according to the World Health Organization. Optical microscopy remains the gold standard technique for malaria diagnosis, however, it requires expertise, is time-consuming and difficult to reproduce. Therefore, new diagnostic techniques based on digital image analysis using artificial intelligence tools can improve diagnosis and help automate it. Methods: In this study, a dataset of 2571 labeled thick blood smear images were created. YOLOv5x, Faster R-CNN, SSD, and RetinaNet object detection neural networks were trained on the same dataset to evaluate their performance in Plasmodium parasite detection. Attention modules were applied and compared with YOLOv5x results. To automate the entire diagnostic process, a prototype of 3D-printed pieces was designed for the robotization of conventional optical microscopy, capable of auto-focusing the sample and tracking the entire slide. Results: Comparative analysis yielded a performance for YOLOv5x on a test set of 92.10% precision, 93.50% recall, 92.79% F-score, and 94.40% mAP0.5 for leukocyte, early and mature Plasmodium trophozoites overall detection. F-score values of each category were 99.0% for leukocytes, 88.6% for early trophozoites and 87.3% for mature trophozoites detection. Attention modules performance show non-significant statistical differences when compared to YOLOv5x original trained model. The predictive models were integrated into a smartphone-computer application for the purpose of image-based diagnostics in the laboratory. The system can perform a fully automated diagnosis by the auto-focus and X-Y movements of the robotized microscope, the CNN models trained for digital image analysis, and the smartphone device. The new prototype would determine whether a Giemsa-stained thick blood smear sample is positive/negative for Plasmodium infection and its parasite levels. The whole system was integrated into the iMAGING smartphone application. Conclusion: The coalescence of the fully-automated system via auto-focus and slide movements and the autonomous detection of Plasmodium parasites in digital images with a smartphone software and AI algorithms confers the prototype the optimal features to join the global effort against malaria, neglected tropical diseases and other infectious diseases. [ABSTRACT FROM AUTHOR]

Streamlined data-driven drug discovery remains challenging, especially in resource-limited settings. We present ZairaChem, an artificial intelligence (AI)- and machine learning (ML)-based tool for quantitative structure-activity/property relationship (QSAR/QSPR) modelling. ZairaChem is fully automated, requires low computational resources and works across a broad spectrum of datasets. We describe an end-to-end implementation at the H3D Centre, the leading integrated drug discovery unit in Africa, at which no prior AI/ML capabilities were available. By leveraging in-house data collected over a decade, we have developed a virtual screening cascade for malaria and tuberculosis drug discovery comprising 15 models for key decision-making assays ranging from whole-cell phenotypic screening and cytotoxicity to aqueous solubility, permeability, microsomal metabolic stability, cytochrome inhibition, and cardiotoxicity. We show how computational profiling of compounds, prior to synthesis and testing, can inform progression of frontrunner compounds at H3D. This project is a first-of-its-kind deployment at scale of AI/ML tools in a research centre operating in a low-resource setting. Streamlined data-driven drug discovery remains challenging, especially in resource-limited settings. Here, the authors present ZairaChem, an AI/ML tool that streamlines QSAR/QSPR modelling, implemented for the first time at the H3D Centre in South Africa. [ABSTRACT FROM AUTHOR]

• Agriculture productivity in Africa and most developing countries are far from optimum. • Digitalization is a potential game changer for agricultural productivity. • Sensors, IoT, AI, and smartphones are key for Africa's agricultural digitalization. • Digitalization could strengthen African food security and globally. • Infrastructure and policy challenges digitalization on the continent. Africa's agriculture has over time, improved and expanded in scale and efficiency, yet its productivity is far from its optimum, and the majority of the continent is still food insecure. To harness its potential for a drastic increase to avoid continued mass hunger and support the bioeconomy's growth, farm productivity in Africa must be retooled. This paper examines the role of digitalization as a potential game changer and an impetus for a transformational future for large-scale and smallholder farming and livelihoods through driving technological and methodological innovation and automation. Digitalization is seen here as consisting of four supporting elements: sensors (in situ or remote), wireless transmission of data, artificial intelligence (AI), and transmission of diagnoses and or recommendations to the end user. In addition to strengthening food security on the continent and globally and the ensuing economic benefits, other cross-cutting effects are presented. These include risk management through early warning systems for natural disasters, improved decision-making with big data, reduction in rural-urban migration by the attraction of youth towards agriculture, and the improvement in environmental stewardship and building climate resilience. Challenges to realizing the benefits of digitalization, including lack of infrastructure and human resource expertise and the role of public-private partnerships in developing solutions, are also addressed. [ABSTRACT FROM AUTHOR]

The controllability and targeting of nanoparticles (NPs) offer solutions for precise and sustainable agriculture. However, the development potential of nanoenabled agriculture remains unknown. Here, we build an NP-plant database containing 1,174 datasets and predict (R² higher than 0.8 for 13 random forest models) the response and uptake/ transport of various NPs by plants using a machine learning approach. Multiway feature importance analysis quantitatively shows that plant responses are driven by the total NP exposure dose and duration and plant age at exposure, as well as the NP size and zeta potential. Feature interaction and covariance analysis further improve the interpretability of the model and reveal hidden interaction factors (e.g., NP size and zeta potential). Integration of the model, laboratory, and field data suggests that Fe2O3 NP application may inhibit bean growth in Europe due to low night temperatures. In contrast, the risks of oxi dative stress are low in Africa because of high night temperatures. According to the prediction, Africa is a suitable area for nanoenabled agriculture. The regional differences and temperature changes make nanoenabled agriculture complicated. In the future, the temperature increase may reduce the oxidative stress in African bean and European maize induced by NPs. This study projects the development potential of nanoenabled agriculture using machine learning, although many more field studies are needed to address the differences at the country and continental scales. [ABSTRACT FROM AUTHOR]

Monkeypox or Mpox is an infectious virus predominantly found in Africa. It has spread to many countries since its latest outbreak. Symptoms such as headaches, chills, and fever are observed in humans. Lumps and rashes also appear on the skin (similar to smallpox, measles, and chickenpox). Many artificial intelligence (AI) models have been developed for accurate and early diagnosis. In this work, we systematically reviewed recent studies that used AI for mpox-related research. After a literature search, 34 studies fulfilling prespecified criteria were selected with the following subject categories: diagnostic testing of mpox, epidemiological modeling of mpox infection spread, drug and vaccine discovery, and media risk management. In the beginning, mpox detection using AI and various modalities was described. Other applications of ML and DL in mitigating mpox were categorized later. The various machine and deep learning algorithms used in the studies and their performance were discussed. We believe that a state-of-the-art review will be a valuable resource for researchers and data scientists in developing measures to counter the mpox virus and its spread. [ABSTRACT FROM AUTHOR]

Even as novel technologies emerge and medicines advance, pathogen-transmitting mosquitoes pose a deadly and accelerating public health threat. Detecting and mitigating the spread of Anopheles stephensi in Africa is now critical to the fight against malaria, as this invasive mosquito poses urgent and unprecedented risks to the continent. Unlike typical African vectors of malaria, An. stephensi breeds in both natural and artificial water reservoirs, and flourishes in urban environments. With An. stephensi beginning to take hold in heavily populated settings, citizen science surveillance supported by novel artificial intelligence (AI) technologies may offer impactful opportunities to guide public health decisions and community-based interventions. Coalitions like the Global Mosquito Alert Consortium (GMAC) and our freely available digital products can be incorporated into enhanced surveillance of An. stephensi and other vector-borne public health threats. By connecting local citizen science networks with global databases that are findable, accessible, interoperable, and reusable (FAIR), we are leveraging a powerful suite of tools and infrastructure for the early detection of, and rapid response to, (re)emerging vectors and diseases. [ABSTRACT FROM AUTHOR]

The increased access to smartphones in Africa and elsewhere in the global south has opened new markets and new areas for surveillance/platform capitalism/data colonialism to operate. This article attends to the socio-technical practices of Transsion, the Chinese maker of Africa's top selling smartphones, and through these showcases how essential hardware are to the global data economy. Working from a mix of data, including translocal fieldwork in Shenzhen, Accra, Addis Ababa, and a close reading of Transsion products and artifacts alongside business practices, the article shows how the company's prioritizing of Black African consumer needs sustains its competitive position and how its constellation of hardware and apps are integral to its success in routine experimentation of artificial intelligence, facial recognition, and other emerging areas of computation. Ultimately, the argument is that consumer hardware such as low-cost smartphones are critical to the datafication of the everyday in the global south via the bundling of surveillant and extractive software and should be considered sites of power within discourses on the platform era. [ABSTRACT FROM AUTHOR]

The dangers of population-level mycotoxin exposure have been well documented. Climate-sensitive aflatoxins (AFs) are important food hazards. The continual effects of climate change are projected to impact primary agricultural systems, and consequently food security. This will be due to a reduction in yield with a negative influence on food safety. The African climate and subsistence farming techniques favour the growth of AF-producing fungal genera particularly in maize, which is a food staple commonly associated with mycotoxin contamination. Predictive models are useful tools in the management of mycotoxin risk. Mycotoxin climate risk predictive models have been successfully developed in Australia, the USA, and Europe, but are still in their infancy in Africa. This review aims to investigate whether AFs' occurrence in African maize can be effectively mitigated in the face of increasing climate change and food insecurity using climate risk predictive studies. A systematic search is conducted using Google Scholar. The complexities associated with the development of these prediction models vary from statistical tools such as simple regression equations to complex systems such as artificial intelligence models. Africa's inability to simulate a climate mycotoxin risk model in the past has been attributed to insufficient climate or AF contamination data. Recently, however, advancement in technologies including artificial intelligence modelling has bridged this gap, as climate risk scenarios can now be correctly predicted from missing and unbalanced data. [ABSTRACT FROM AUTHOR]

Increasingly, innovation in artificial intelligence technologies portends the re-conceptualization of human existentiality along the paradigm of posthumanism. An exposition of this through a critical culturo-historical methodology uncloaks the Eurocentric genitive basis of the philosophical anthropology that underpins this technological posthumanism, as well as its dystopian possibilities. As a contribution to obviating the latter, an Africanist civilizational humanism proclaimed by Pixley ka Isaka Seme is proffered as a plausible alternative paradigm for humanity's technological advancement. Seme, a pan-Africanist thinker of the early twentieth century, proclaimed humanistic-spirituality as the indispensable gift African Civilisation-in-its-renaissance is yet to offer global humanity. His postulation is being provided as a contribution to the archive on cross-cultural ethics of artificial intelligence. [ABSTRACT FROM AUTHOR]

Background: Artificial Intelligence (AI) platforms, increasingly deployed in public health, utilize robust data systems as a critical component for health emergency preparedness. Yet, Africa faces numerous challenges in the availability, analyses, and use of data to inform health decision-making. Countries have limited access to their population data. Those with access, struggle to utilize these data for program improvements. Owing to the rapid growth of mobile phone ownership and use in the region, Africa is poised to leverage AI technologies to increase the adoption, access and use of data for health. To discuss and propose solutions for responsible development and adoption of innovations like AI in Africa, a virtual workshop was organized from the 21st to 24th June, 2021. This report highlights critical policy dimensions of strengthening digital health ecosystems by high-level policymakers, technical experts, academia, public and private sector partners. Method: The four days' workshop focused on nine sessions, with each session focusing on three themes. Discussions during the sessions concentrated on public and private sectors, the academia and multilateral organizations' deployment of AI. These discussions expanded participants' understanding of AI, the opportunities and challenges that exist during adoption, including the future of AI for health in the African region. Approximately 250 participants attended the workshop, including countries representatives from ministries of Health, Information and Technology, Developmental Organizations, Private Sector, Academia and Research Institutions among others. Results: The workshop resolved that governments and relevant stakeholders should collaborate to ensure that AI and digital health receive critical attention. Government ownership and leadership were identified as critical for sustainable financing and effective scale-up of AI-enabled applications in Africa. Thus, government is to ensure that key recommendations from the workshop are implemented to improve health sector development in Africa. Conclusions: The AI workshop was a good forum to deliberate important issues regarding AI for health in the African context. It was concluded that there is a need to focus on vital priorities in deploying AI in Africa: Data protection, privacy and sharing protocols; training and creating platforms for researchers; funding and business models; developing frameworks for assessing and implementing AI; organizing forums and conferences on AI; and instituting regulations, governance and ethical guidelines for AI. There is a need to adopt a health systems approach in planning for AI to reduce inefficiencies, redundancies while increasing effectiveness in the use of AI. Thus, robust collaborations and partnerships among governments and various stakeholders were identified as key. [ABSTRACT FROM AUTHOR]

African humanism should be considered more in the theoretical discussion on transhumanism. Using an underexplored humanistic philosophy of Ubuntu, this article shows how the perspective from the global south provides guidelines for pursuing transhumanism without jeopardizing humanism. It argues that heuristics from African (Ubuntu) humanism can serve transhumanist goals. While transhumanism has attracted severe criticisms from bio‐conservatives, this article counterargues some salient objections. Drawing on an Ubuntu understanding of humanism, this article posits that the transhumanist vision of the posthuman does not threaten our humanity. Ubuntu humanism is shown to be supportive of transhumanism and can plausibly serve as a guide to protecting transhumanist trajectories from potential abuses. This article concludes that the embedded values of African humanism deserve to be taken seriously in galvanizing global trust in transhuman futures. [ABSTRACT FROM AUTHOR]

Purpose: Studies have documented the clinical potentials of artificial intelligence (AI) in medical imaging practice to improving patient care. This study aimed to qualitatively explore the perception of radiographers relating to the integration of AI in medical imaging practice in Africa. Methods: The study employed a qualitative design using an open-ended online instrument administered between March and August 2020. Participants consisted of radiographers working within Africa during the time of the study. Data obtained were analysed using qualitative content analysis. Six themes of concerns were generated: expectant tool; career insecurity; cost of new technology, equipment preservation and data insecurity; service delivery quality; need for expanding AI awareness. Results: A total of 475 valid responses were obtained. Participants demonstrated a positive outlook about AI in relation to clinical quality improvement, competent diagnosis, radiation dose reduction and improvement in research. They however expressed concerns relating to the implementation of this technology, including job security and loss of core professional radiographer skills and roles. In addition, concerns regarding AI equipment maintenance, lack of awareness about AI and education and training opportunities were evident. Conclusion: Awareness of the importance of AI in medical imaging practice was acknowledged; however, concerns relating to job security, data protection must be given critical attention for successful implementation of these advanced technologies in medical imaging in Africa. Inclusion of AI modules in the training of future radiographers is highly recommended. [ABSTRACT FROM AUTHOR]

The risk of money laundering induced by information and communication technologies (ICT) in the financial sector could be controlled by adopting artificial intelligence systems and increased security investment in Sub‐Saharan Africa. The establishment of a regulatory framework for controlling ICT is necessary to combat capital laundering effectively. Poverty and unemployment harm the struggle against money laundering. The fight against corruption and political stability contribute positively to policies against money laundering. [ABSTRACT FROM AUTHOR]