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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).

Recent advancements in generative artificial intelligence (GAI) have sparked debates and research on its transformative potential in education. This study explored how a specific group of educators partner with GAI tools, particularly ChatGPT, to complement and enhance their teaching. Within an interpretative paradigm, the study used an exploratory case study design to investigate how 13 educators enrolled as students in a structured master's programme at an open distance learning university in South Africa use GAI tools in their teaching. Their posts on a discussion forum were used to collect data. Findings reveal that they actively employ GAI to streamline their teaching practices, specifically to personalized tutors, simplifying English texts and translations, assessments, lesson planning, and critical thinking tasks. The study also highlights the need to understand the limitations and boundaries of GAI, including concerns about accuracy, biases and reliability. Importantly, the absence of institutional guidelines on GAI raises questions about the necessity for clear guidelines and policies to ensure responsible and ethical integration in educational contexts. The paper concludes by emphasizing the transformative potential of GAI and the fact that it will increasingly act as a partner in teaching and influence modern teaching practices. Ultimately, GAI's foothold in education is undeniable, challenging educators and policymakers to navigate its implications while maximizing its potential to support effective teaching.

Background: The COVID-19 pandemic and the shift to digital learning materials have dramatically reduced the use of paper-based textbooks in higher education. In the field of engineering, students need a comprehensive understanding of mathematical concepts, which can be achieved through the use of e-textbooks. These digital learning materials can provide a more comprehensive and effective learning experience by incorporating a variety of multimedia elements, such as audio, visual aids, and interactive features. Rationale/Relevance: Mathematics students are expected to possess critical problem-solving skills, yet they are rarely asked to elaborate on and explain their mathematical reasoning and concepts through written argument in the engineering mathematics classroom. While students may excel in formula manipulation, they may still possess misconceptions about mathematical principles and concepts. A reliance on mechanical and procedural approaches, such as formula application, without proper conceptual understanding can result in serious misunderstandings of mathematics and its real-world applications. Without the ability to reason about mathematical concepts, students may struggle to connect what they are learning in class with real-world scenarios, leading to difficulties in solving practical problems. It is, therefore, crucial to foster conceptual understanding and critical reasoning skills in mathematics education, rather than solely relying on memorisation, in order to equip students with the necessary skills to succeed in their careers. Research Problem/ Research Question: This study will answer the following research question:How effective are e-textbooks in promoting conceptual understanding of engineering mathematics? Aim of the Study/Focus: The purpose of this study is to investigate conceptual learning in engineering mathematics using an e-textbook as a learning tool. Methodology: This qualitative case study explores e-textbooks as adaptive technology, with functionalities that include artificial intelligence allowing students to develop their understanding by interacting with digital text, watching videos of real-world mathematics concepts, and responding to quick quizzes on concepts; as well as practising and mastering further mathematical principles and concepts. It focuses on first-year engineering students and lecturers at a University of Technology in South Africa. The analysis of interview recordings was done with ATLAS.ti. analytical software. Theoretical Framework: The data from this study was analyzed through the lenses of cultural historical activity theory (CHAT) and Vosniadou's notion of conceptual change, allowing the researchers to explain complex real-world situations that students experience when engaging with the e-textbook to solve mathematics problems. Recommendations: The work presented here has implications for future studies of conceptual learning in mathematics research and may provide opportunities around learning engineering mathematical concepts, particularly in the context of developing countries. This is because it offers novel instructional approaches that are tailored to the specific needs and challenges of these countries.

While ChatGPT has recently become very popular, AI has a long history and philosophy. This paper intends to explore the promises and pitfalls of the Generative Pre-trained Transformer (GPT) AI and potentially future technologies by adopting a speculative methodology. Speculative future narratives with a specific focus on educational contexts are provided in an attempt to identify emerging themes and discuss their implications for education in the 21st century. Affordances of (using) AI in Education (AIEd) and possible adverse effects are identified and discussed which emerge from the narratives. It is argued that now is the best of times to define human vs AI contribution to education because AI can accomplish more and more educational activities that used to be the prerogative of human educators. Therefore, it is imperative to rethink the respective roles of technology and human educators in education with a future-oriented mindset.

The current article used real data to demonstrate the analysis and synthesis of Mixed Methods Research (MMR) data with generative Artificial Intelligence (Gen AI). I explore how reliable and valid Gen AI data outputs are and how to improve their use. The current content is geared towards enhancing methodological application regardless of field or discipline and includes access to a prompt library and examples of using outputs. The demonstration data used emanated from a study done in South Africa, with a quantitative sample size of 969 first-year engineering students and, for the qualitative part, 14 first-year students. In the current article, I compare my original analysis to ChatGPT results. Generative AI as a mind tool is best used with human insight, and I found this to be especially true when coding qualitative data. ChatGPT produced generic codes if asked to do inductive coding, and the results improved when training the Gen AI on human examples, which led to moderate and significant correlations between human and machine coding. The quantitative analysis was accurate for the descriptive statistics, but the researcher had to use best judgment to select the correct inferential analysis. Quantitative and qualitative analysis should be conducted separately in generative AI before asking the Chatbot for help with mixed methods results. In the current paper, I give guidelines and a tutorial on how to use chatbots in an ethically responsible and scientifically sound manner for research in social and human sciences.

For over two decades now, the application of Citizen Science to Education has been evolving, and fundamental topics, such as the drivers of motivation to participate in Citizen Science projects, are still under discussion. Some recent developments, though, like the use of Artificial Intelligence to support data collection and validation, seem to point to a clear-cut divergence from the mainstream research path. The objective of this paper is to summarise the development trajectory of research on Citizen Science in Education so far, and then shed light on its future development, to help researchers direct their efforts towards the most promising open questions in this field. We achieved these objectives by using the lens of the Affordance-Actualisation theory and the Main Path Analysis method.

The delivery of adequate services to citizens has been a major problem that has ignited social protests in South Africa’s local municipalities. The paper found that a lack of accountability, transparency, and public participation has impacted service delivery. It was found that services delivered to the citizens have failed to meet their expectations. This is because in situations where there is no accountability, transparency, or public participation, there is a lack of inclusivity, which in turn raises violent feelings that ignite social protests. The paper found that the use of Artificial Intelligence would promote transparency, accountability, and inclusivity which are needed for delivering adequate services that meet people’s expectations. To achieve those assertions, the paper used secondary datacollected through a qualitative research methodology and analysed via document and content analysis. There is a need for South Africa’s local municipalities to adopt Artificial Intelligence technology in the service delivery process. [ABSTRACT FROM AUTHOR]

Background: The ethical governance of Artificial Intelligence (AI) in health care and public health continues to be an urgent issue for attention in policy, research, and practice. In this paper we report on central themes related to challenges and strategies for promoting ethics in research involving AI in global health, arising from the Global Forum on Bioethics in Research (GFBR), held in Cape Town, South Africa in November 2022., Methods: The GFBR is an annual meeting organized by the World Health Organization and supported by the Wellcome Trust, the US National Institutes of Health, the UK Medical Research Council (MRC) and the South African MRC. The forum aims to bring together ethicists, researchers, policymakers, research ethics committee members and other actors to engage with challenges and opportunities specifically related to research ethics. In 2022 the focus of the GFBR was "Ethics of AI in Global Health Research". The forum consisted of 6 case study presentations, 16 governance presentations, and a series of small group and large group discussions. A total of 87 participants attended the forum from 31 countries around the world, representing disciplines of bioethics, AI, health policy, health professional practice, research funding, and bioinformatics. In this paper, we highlight central insights arising from GFBR 2022., Results: We describe the significance of four thematic insights arising from the forum: (1) Appropriateness of building AI, (2) Transferability of AI systems, (3) Accountability for AI decision-making and outcomes, and (4) Individual consent. We then describe eight recommendations for governance leaders to enhance the ethical governance of AI in global health research, addressing issues such as AI impact assessments, environmental values, and fair partnerships., Conclusions: The 2022 Global Forum on Bioethics in Research illustrated several innovations in ethical governance of AI for global health research, as well as several areas in need of urgent attention internationally. This summary is intended to inform international and domestic efforts to strengthen research ethics and support the evolution of governance leadership to meet the demands of AI in global health research., (© 2024. The Author(s).)

The sanctity of the doctor-patient relationship is deeply embedded in tradition - the Hippocratic oath, medical ethics, professional codes of conduct, and legislation - all of which are being disrupted by big data and 'artificial' intelligence (AI). The transition from paper-based records to electronic health records, wearables, mobile health applications and mobile phone data has created new opportunities to scale up data collection. Databases of unimaginable magnitude can be harnessed to develop algorithms for AI and to refine machine learning. Complex neural networks now lie at the core of ubiquitous AI systems in healthcare. A transformed healthcare environment enhanced by innovation, robotics, digital technology, and improved diagnostics and therapeutics is plagued by ethical, legal and social challenges. Global guidelines are emerging to ensure governance in AI, but many low- and middle-income countries have yet to develop context- specific frameworks. Legislation must be developed to frame liability and account for negligence due to robotics in the same way human healthcare providers are held accountable. The digital divide between high- and low-income settings is significant and has the potential to exacerbate health inequities globally.

This paper presents a foresight perspective of nanotechnology in South Africa based on a 20-year period scientometric analysis of the country's nanotechnology publications on the Web of Science (WoS) Core Collection. Firstly, publication trends are reported; then, possible socio-economic relevant sectors arising from this information are determined. Lastly, indicators that can be used in foresight exercises to evaluate the potential nanotechnology research areas in South Africa are examined. The 20-year review is also compared with the recent past year, 2019, to identify any changing trends. South Africa's nanotechnology publications per year grew exponentially from 68 papers in 2000 to 1672 in 2019, an increase of 2459%. The total share of nanotech publications increased from 1.4% in 2000 to 6.6% in 2019, thus a 0.52% increase per year. Compared with Brazil, Russia, India and China, the BRICS countries, South Africa has the lowest nanotechnology productivity with an activity index of 0.68. Over the last 5 years, South Africa nanotech publications had a Hirsch-index of 94 and an average citations rate of 12.76 per paper. Universities are the most prominent publishers, and there are very few publications from the private sector, which can negatively impact the commercialisation of nanotechnology research. The top 10 most prolific researchers, author or co-author over 20% of the nanotechnology papers are reported. A mixture of old and new top researchers' names suggests succession planning in the system as the years progress. The emergence of computer science as one of the top 20 subjects publishing in nanotech in 2019 and a high level of researcher collaboration suggests possible convergence of nanotech, information technology and artificial intelligence in South Africa. The strategic socio-economic-focused nanotechnology research areas identified for South Africa include material science, photoluminance and optics, medicine, catalysis, electronics, energy, biotech, magnetism, sensors, water and communicable diseases. The top collaborating countries, top researchers, top institutions and nanotechnology economic hubs are reported for each strategic research area. The level of innovation was evaluated using the nanotechnology value chain, and there is a meagre 3.5% of papers reporting on nano-enabled products. [ABSTRACT FROM AUTHOR]

Various international guideline documents suggest a human-centric approach to the development and use of artificial intelligence (AI) in society, to ensure that AI products are developed and used with due respect to ethical principles and human rights. Key principles contained in these international documents are: transparency (explainability), accountability, fairness and privacy. Some governments are using AI in the delivery of public services, but there is a lack of appropriate policy and legal frameworks to ensure responsible AI in government. This paper reviews recent international developments and concludes that, an appropriate policy and legal framework must be based on the key principles contextualised to the world of AI. A national legal framework alone is not sufficient and should be accompanied by a practical instrument, such as an algorithm impact assessment, aimed at reducing risk or harm. Recommendations for a possible South African legal framework for responsible AI in government are proposed. [ABSTRACT FROM AUTHOR]

Lecture Notes in Computer Science (LNCS) is a globally recognised publication outlet for the field of Computer Science, including in South Africa. In this study, spanning from 1973 to 2022, we investigated the research participation of South African based authors in LNCS. The publication output and citation impact of these authors were compared to the global Computer Science and LNCS output. The authorship patterns and collaborative behaviour of South African LNCS papers were explored, and a keyword or topic analysis also conducted. Of the total of 518 662 LNCS papers published globally between 1973 and 2022, South African based researchers contributed 1150 papers (0.22%). The LNCS papers from South Africa exhibit a strong collaborative publication culture, with 1043 (91%) co-authored and 107 (9%) singleauthored works. Local LNCS researchers prefer institutional collaboration (43%), followed by international (37%) and national collaboration (11%). Europe emerged as the most significant collaboration partner for LNCS researchers in South Africa. Of the 1150 papers, 836 (73%) had received citations, while 314 (27%) had not. On average, papers published by South African based authors received 6.05 citations, compared to the global LNCS average of 9.49 citations per paper. A keyword analysis revealed that the majority of papers by South African authors focus on artificial intelligence. The results indicate that, although LNCS serves as a reputable dissemination platform for Computer Science research output both globally and locally, South African authors should consider publishing more journal articles to build and improve their researcher profiles. Significance: * The study shows that LNCS is the most frequent publication outlet for Computer Science researchers, globally and in South Africa. * The study offers insight into the publication output, authorship patterns, collaborative behaviour and citation impact of South African based Computer Science researchers. [ABSTRACT FROM AUTHOR]

The coronavirus disease 2019 (COVID-19) has developed into a pandemic. Data-driven techniques can be used to inform and guide public health decision- and policy-makers. In generalizing the spread of a virus over a large area, such as a province, it must be assumed that the transmission occurs as a stochastic process. It is therefore very difficult for policy and decision makers to understand and visualize the location specific dynamics of the virus on a more granular level. A primary concern is exposing local virus hot-spots, in order to inform and implement non-pharmaceutical interventions. A hot-spot is defined as an area experiencing exponential growth relative to the generalised growth of the pandemic. This paper uses the first and second waves of the COVID-19 epidemic in Gauteng Province, South Africa, as a case study. The study aims provide a data-driven methodology and comprehensive case study to expose location specific virus dynamics within a given area. The methodology uses an unsupervised Gaussian Mixture model to cluster cases at a desired granularity. This is combined with an epidemiological analysis to quantify each cluster's severity, progression and whether it can be defined as a hot-spot. [ABSTRACT FROM AUTHOR]

This paper investigates the usefulness of Google search patterns with Artificial Intelligence (AI) techniques for timely influenza-like illness (ILI) forecasting for each of the nine South African provinces. Traditional surveillance methods are limited by delay in reporting. Existing digital disease surveillance studies that employ alternative online data have scarcely explored sub-Saharan African countries. In South Africa, Google search data has only been recently studied for ILI surveillance at the national level. Meanwhile, the differences in socio-economic and technological conditions across provinces call for finer spatial investigation. We perform correlation analysis between Google trends (GT) data for 21 ILI-related terms and real-life ILI surveillance data for each province. Next, we develop models to assess the predictive performance of these GT data for forecasting ILI rates, using time series, machine learning, and deep learning methods. We observe sufficient correlation for only two of the nine provinces: Gauteng and Western Cape. Thus, GT data could only be used to forecast ILI in these two provinces. Interestingly, these two provinces are regarded as the most economically developed. In the other seven provinces, LSTM, a deep learning technique, gives more accurate predictions than a baseline autoregressive model when only past ILI data are used for forecasting future ILI trends. The results reveal that, for provinces for which GT data is sufficiently available, it is not only free and fast, but is an effective predictor on its own as well as when added to past ILI data for forecasting future ILI infection rates. The correlation analysis suggests an association between provincial socio-economic development and the use of digital platforms for disease surveillance. Overall, the study established the need for finer scale ILI forecasting which will inform targeted planning for disease surveillance and interventions. [ABSTRACT FROM AUTHOR]

Database right, as a subcategory of copyright law, rewards the creative exploits, labour and skill expended in compiling, arranging and organising a set of information for ease of use. When humans create, the labour and ingenuity exerted in reaching completion is compensated by the law by bestowing exclusive rights of exploitation on creators and their assigns. In the sphere of Electronic Health Record databases, data (which are mainly inputted by clinicians) are obtained from various sources, including clinician deduction, patients and caregivers’ observations and reports, and family-reported or community-reported traits. Using the doctrinal research method and writing primarily from a South African legal standpoint with insights drawn from other countries, the author considers the dynamics of navigating ownership rights in Electronic Health Record databases and the associated challenges when AI is involved. In summary, this paper examines the Electronic Health Record ownership rights of humans and machines, particularly in the South African context. [ABSTRACT FROM AUTHOR]

This paper presents a review on techniques used to monitor rolling stock equipment in order to predict the maintenance needs and activities before failure could actually occur. There is limited number of research on this study which has focused majorly on the infrastructure, and very little has covered the topic on maintenance or the rolling stock maintenance. This paper explores the available current source and findings about three maintenance strategies, two which are currently the most commonly practiced in South Africa and one which is taking the focus on this research. The review also revealed that artificial intelligence techniques are rarely used in monitoring of railway equipment. Part of the discussion in this paper is about the use of these AI techniques in maintenance. A discussion of a proposed maintenance strategy that uses the application of AI techniques is included and the whole review is closed off by outlining the possible challenges and future considerations relating to the predictive maintenance of Electric Trains. [ABSTRACT FROM AUTHOR]

High quality road infrastructure is essential to support economic growth for any region. For South Africa’s landlocked economic hub 79% of goods are transported using roads infrastructure. Protection of the road infrastructure is implemented by means of overload control monitoring at traffic control centres (TCCs) on freight corridors. Statistics collected from TCC operations indicate that 75% to 85% of statically weighed vehicles are legally loaded, with the implication that unnecessary time was wasted for these vehicles. This paper therefore proposes an algorithm, called the intelligent weigh-in-motion (IWIM) algorithm, with the purpose to decrease static weighing of vehicles by implementing data sharing between TCCs on the freight corridor, combined with intelligent interpretation of this data. The selected algorithm was chosen after testing multiple artificial intelligence (AI) models (logistic regression, random forest tree, and artificial neural network) to achieve the best performance to decrease static weighing of vehicles while not increasing the number of overloaded vehicles allowed to proceed on the corridor. The best performing model to differentiate between overloaded and legal vehicles, random forest tree, achieved an average improvement of 65,83% in terms of vehicles to be statically weighed when compared to the current rule-based system employed at TCCs. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) is an important enabler of the fourth and fifth industrial revolutions. It has the potential to empower intelligent production systems that are central to industry 4.0 and help place worker wellbeing and the planet at the centre of industry 5.0 processes. However, few studies have considered the different roles of AI in industry 4.0 and 5.0. This paper reports on a bibliometric review of 2887 documents examining the use of AI for industry 4.0 and industry 5.0. We found that scholarly activity increased rapidly from 2018 onwards and can be attributed to a large variety of countries, including India, South Africa and China. Our analysis reveals four clusters in the literature, with two showing particular promise for researchers notably around the themes of industry 5.0 and deep learning. Further insights are presented and discussed, followed by a future research agenda. [ABSTRACT FROM AUTHOR]

With the increased use of Artificial Intelligence (AI) in wildlife conservation, issues around whether AI-based monitoring tools in wildlife conservation comply with standards regarding AI Ethics are on the rise. This review aims to summarise current debates and identify gaps as well as suggest future research by investigating (1) current AI Ethics and AI Ethics issues in wildlife conservation, (2) Initiatives Stakeholders in AI for wildlife conservation should consider integrating AI Ethics in wildlife conservation. We find that the existing literature weakly focuses on AI Ethics and AI Ethics in wildlife conservation while at the same time ignores AI Ethics integration in AI systems for wildlife conservation. This paper formulates an ethically aligned AI system framework and discusses pre-eminent on-demand AI systems in wildlife conservation. The proposed framework uses agile software life cycle methodology to implement guidelines towards the ethical upgrade of any existing AI system or the development of any new ethically aligned AI system. The guidelines enforce, among others, the minimisation of intentional harm and bias, diversity in data collection, design compliance, auditing of all activities in the framework and ease of code inspection. This framework will inform AI developers, users, conservationists, and policymakers on what to consider when integrating AI Ethics into AI-based systems for wildlife conservation. [ABSTRACT FROM AUTHOR]

Although the accounting definition of assets contemplates intangible, abstract assets such as those embodied in intellectual property (IP), South African company law largely views IP as a legal and not a business asset. This paper tentatively suggests an approach that uses artificial intelligence (AI) to mitigate weaknesses in the South African patent law relating to the absence of patent searches and examinations. It is hoped that using AI will enable the filing of quality patents that satisfy the prescribed patentability criteria. High-quality patents will allow companies to accumulate patents as corporate assets. The approach is based on the algorithmic use of AI technologies such as machine learning, natural language processing, deep learning alongside the Internet of Things, and IP analytics to strengthen South Africa's IP system and create asset value for corporations. The paper recommends using the proposed AI technologies by companies and the Patents Office to enable the filing of high-quality patents, which will lead to the accumulation of corporate assets in the form of patents. The methodology is doctrinal, and the paper relies on recent literature on IP and AI, South African law, case law and examples drawn from studies conducted in other countries. [ABSTRACT FROM AUTHOR]

South Africa as a nation became democratic in 1994 because of the end of apartheid. Since 1994, higher education has geared towards transformation and redress of the inequalities created by the inhuman policies of apartheid. While few applaudable steps have been taken towards this direction, South African higher education remains largely untransformed. For the past two years, a wave of student protest swept across the nation, calling for decolonisation of higher education in general and the curriculum in particular. This move brings to mind several questions about decolonisation and transformation. What is the state of South African higher education? Why has it remained untransformed since the advent of democracy? What should be decolonised to ensure transformation of the present and the future? This paper therefore ventures to answer these three questions using the theory of social transformation as a lens. The paper points out that funding structures, research politics, administrative structures and a lack of interest are amongst the reasons for the lack of transformation. The paper concludes that there will be no transformation until higher education institutions have been decolonised. Social transformation is therefore argued as the pathway for decolonisation. The paper recommends that transformation in higher education should go beyond the shelves where they are stored as policy to the classroom and university environment for practice and universities need to revise their understandings of transformation under the guidance of the DHET. [ABSTRACT FROM AUTHOR]

Sustainable planning of waste management is contingent on reliable data on waste characteristics and their variation across the seasons owing to the consequential environmental impact of such variation. Traditional waste characterization techniques in most developing countries are time-consuming and expensive; hence the need to address the issue from a modelling approach arises. In modelling the complexity within the system, a paradigm shift from the classical models to the intelligent models has been observed. The application of artificial intelligence models in waste management is gaining traction; however its application in predicting the physical composition of waste is still lacking. This study aims at investigating the optimal combinations of network architecture, training algorithm and activation functions that accurately predict the fraction of physical waste streams from meteorological parameters using artificial neural networks. The city of Johannesburg was used as a case study. Maximum temperature, minimum temperature, wind speed and humidity were used as input variables to predict the percentage composition of organic, paper, plastics and textile waste streams. Several sub-models were stimulated with combination of nine training algorithms and four activation functions in each single hidden layer topology with a range of 1-15 neurons. Performance metrics used to evaluate the accuracy of the system are, root mean square error, mean absolute deviation, mean absolute percentage error and correlation coefficient (R). Optimal architectures in the order of input layer-number of neurons in the hidden layer-output layer for predicting organic, paper, plastics and textile waste were 4-10-1, 4-14-1, 4-5-1 and 4-8-1 with R-values of 0.916, 0.862, 0.834 and 0.826, respectively at the testing phase. The result of the study verifies that waste composition prediction can be done in a single hidden-layer satisfactorily.

Fourth Industrial Revolution (4IR) technologies have elevated the capabilities and possibilities of improvement and efficiency in the energy sector. This paper interrogates how energy companies in South Africa, Germany and China apply 4IR technologies. A total of 26 energy companies in those countries were surveyed. An analysis was carried out using the Cronbach Alpha, Kruskal-Wallis and Mann-Whitney tests. Survey results indicate that 85% of companies acknowledge good levels of participation in the 4IR, and were clear about which 4IR technologies are important, although few companies develop these themselves. Technologies enabling access to big, real-time data (BRTD) and BRTD analysis software, are valued the most in measured importance, efficiency, reliability and ability to be integrated across the energy system. The transfer of data using the Internet of things ranked highly as a 4IR technology, whereas artificial intelligence, robotics and machine-human integration (also referred to as machine-human interaction) are considered less important, efficient, and reliable. China rates 4IR technologies as more important than South Africa and Germany do. For South Africa to be competitive in the global energy sector it needs to engage with and embrace 4IR technologies to a greater extent. [ABSTRACT FROM AUTHOR]

This research attempts to construct a second order observation model in understanding the significance of Artificial intelligence (AI) in changing the global power structure. Because of the inevitable ubiquity of AI in the world societies' near future, it impacts all the sections of society triggering socio-technical iterative developments. Its horizontal impact and states' race to become leader in the AI world asks for a vivid understanding of its impact on the international system. To understand the latter, Triple Helix (TH) model along with Shannon's information entropy has been used to operationalize system's theory. This model uses Shannon's information theory to calculate the uncertainty generated from the interactions between the sub-systems considered. Data for the latter has been taken from Sanford Artificial Intelligence Laboratory 2019 report. It is found that European countries are the most effected in the AI era, with probability of losing their global influence and thus creating power void. Emerging powers such as India, Canada, South Africa and Brazil have better chances to fill the void and emerge as global influences. [ABSTRACT FROM AUTHOR]

The integration of Artificial Intelligence (AI) within supply chain management (SCM) has precipitated a significant transformation in business processes, particularly in emerging economies like South Africa. This systematic literature review explores the extent of AI's influence on SCM practices across various industries within the South African context, underpinning the potential of AI to improve operational efficiency, strategic decision-making, and innovation. This review thoroughly followed the PRISMA guidelines. The findings reveal that AI significantly contributes to the advancement of SCM by improving accuracy in demand forecasting, optimising resource allocation, and facilitating real-time decision-making. This review synthesizes current research, offering a comprehensive overview of AI's transformative potential in SCM within South Africa. It also suggests areas for future research, particularly in addressing the challenges of AI implementation and exploring its impact on sustainable SCM practices. The implications for both practitioners and policymakers include prioritising digital infrastructure development, ethical AI integration, and encouraging public-private partnerships to support AI-driven innovations in supply chain networks. Future research should prioritise the development of practical methods that specifically target the demands and obstacles of incorporating AI into supply chains in South Africa, with the aim of promoting fair and sustainable growth. [ABSTRACT FROM AUTHOR]

This study advances research and practice related to adopting artificial intelligence (AI) in the context of South Africa (SA). The study evaluated AI adoption by South African manufacturing Small and Medium Enterprises (SMEs); established the challenges faced by manufacturing SMEs in adopting AI; and developed a framework for adopting AI for manufacturing SMEs' growth and survival. The study adopted a systematic literature review approach. Articles from Scopus and Google scholar databases, ranging from the years 2018 to 2024, were used. Of the 206 articles found, 54 were shortlisted. The systematic review analysis was performed using the PRISMA framework. The results identified AI adoption by South African manufacturing SMEs is low, limiting their innovation and productivity. The results also show, despite the numerous benefits AI adoption can offer manufacturing SMEs in the country, a major constraint is the lack of a framework to enhance adoption and implementation. Hence, this study was conducted to develop a framework to improve AI adoption by South African manufacturing SMEs. The findings contribute to the body of knowledge and provide new insights to manufacturing SME owners/managers, policymakers and practitioners into AI adoption to enhance manufacturing SMEs' ability to compete on the global stage. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this study is to explore the increased uptake of Artificial Intelligence (AI) technology by Facility Management (FM) organizations for enhanced operational efficiency and competitive advantage. While AI adoption in FM has been widely reported, limited attempts have been made to assess its impact on the social well-being of FM employees. To contribute towards addressing this gap, this study established the essential employee social well-being factors mostly impacted by the adoption of AI in South African FM organizations. Design/methodology/approach: A four-stage design comprising a comprehensive review of literature, expert interviews, questionnaire census and focus group discussion sessions was used to elicit data from a sample of participants drawn from 22 South African FM organizations. The data was analyzed using a combination of content analysis, relative importance index and interpretative structural modeling for various data sets toward achieving the study's objectives. Findings: Sixteen employee social well-being factors, classified under job satisfaction, social relationship and knowledge development categories, respectively, were identified as being impacted by AI adoption in FM organizations. Furthermore, it was established that job security, job autonomy and professional status, which belong to the job satisfaction social well-being factor category, were deemed by FM employees as being mostly impacted by AI adoption. Practical implications: The enhanced understanding of the impact of AI adoption on FM employees' social well-being factors will contribute to the development of a collaborative intelligence framework for managing AI adoption in FM organizations toward engendering optimal AI–FM employee relationships for improved productivity. Originality/value: Besides being one of the foremost studies to investigate the impact of AI adoption on FM employees' social well-being, this study introduces a hierarchical framework of understanding employee social well-being factors based on multi-stakeholder perspectives. [ABSTRACT FROM AUTHOR]

The current disconnect in policy development and management of the water, energy, and food resources threatens their security of supply despite their inextricable interlinkage, commonly known as Water-Energy-Food nexus. The security of these basic human needs is aggravated by climate change's cross-cutting role, which impacts their availability. These apprehensions suggest that contemporary methods are required to improve and enhance the management of the food, water, and energy resources within the climate change discourse. This study applies Systems Thinking methodologies to foster collaboration amongst key stakeholders within Eskom, the electricity generating sector in South Africa. This was done through several simulation workshops held amongst employees within the generation, transmission, distribution, and corporate employees. The workshops demonstrated the ability of the systems thinking approach to enable stakeholders to apply the “nexus thinking” in managing the WEF sectors within the climate change discourse. The study concludes by recommending the application of this simulation within policy development and other key sectors to enable a broader application of nexus thinking, thus assisting in addressing the silo approach within these resource management. [ABSTRACT FROM AUTHOR]

Artificial neural networks (ANNs) are powerful data-oriented "black-box" algorithms capable of assessing and delineating linear and multifaceted non-linear correlations between the dependent and explanatory variables. Through the years, neural networks have proven to be effective and robust analytical techniques for establishing artificial intelligence-based tools for modelling, estimating, and projecting spatial and temporal variations in water bodies. Accordingly, ANN-based algorithms gained increased attention and have emerged as practical alternatives to traditional approaches for hydro-chemical analysis. ANNs are among the widely used computer systems for modelling surface water quality. Considering their wide recognition, resilience, flexibility, and accuracy, the current study employs a neural network-based methodology to construct a novel water quality index (WQI) model suitable for analysing South African rivers. The feed-forward, back-propagated multilayered perceptron model has three parallel-distributed neuron layers interconnected with seventy weighted links orientated laterally from left to right. First, the input layer includes thirteen neuro-nodes symbolising thirteen explanatory variables, including NH3, Ca, Cl, Chl-a, EC, F, CaCO3, Mg, Mn, NO3, pH, SO4, and turbidity (NTU). Second, the hidden layer consists of eleven neuro-nodes accountable for computational tasks. Lastly, the output layer features one neuron responsible for conveying network outcomes using a single-digit WQI rating extending from zero to one hundred, where zero represents substandard water quality and one hundred denotes exceptional water quality. The AI-based model was developed using water quality data obtained from six monitoring locations within four drainage basins under the management of the Umgeni Water Board in the KwaZulu-Natal Province of South Africa. The dataset comprises 416 samples randomly divided into training, testing, and validation sets using a proportional split of 70:15:15%. The Broyden–Fletcher–Goldfarb–Shanno (BFGS) technique was utilised to conduct backpropagation training and adjust synapse weights. The dependent variables are the WQI scores from the universal water quality index (UWQI) model developed specifically for South African river basins. The ANN demonstrated enhanced efficiency through an overall correlation coefficient (R) of 0.985. Furthermore, the neural network attained R-values of 0.987, 0.992, and 0.977 for the training, testing, and validation intervals. The ANN model achieved a Nash–Sutcliffe efficiency (NSE) value of 0.974 and coefficient of determination (R2) of 0.970. Sensitivity analysis provided additional validation of the preparedness and computational competence of the ANN model. The typical target-to-output error tolerance for the ANN model is 0.242, demonstrating an adequate predictive ability to deliver results comparable with the target UWQI, having the lowest and highest index ratings of 75.995 and 94.420, respectively. Accordingly, the three-layer neural network is scientifically sound, with index values and water quality evaluations corresponding to the UWQI results. The current research project seeks to document the processes used and the outcomes obtained. [ABSTRACT FROM AUTHOR]

AI-based simulations for educational and assessment purposes are gaining global recognition. Informed by cultural comparison research, this study investigates cross-country variations in users' utilization and perceptions of a simulation-based assessment. Specifically, we conducted a comparative analysis between a sample of South African and Canadian students to uncover potential differences in assessment scores, communication patterns, and reactions vis-a-vis a simulation assessment for evaluating teamwork skills. Data were collected from over 500 undergraduate students in South Africa and Canada who completed a simulation assessment and reported their reactions and perceptions. The findings yielded several noteworthy observations. First, South African students attained higher assessment scores than Canadian students; although, the difference did not quite reach statistical significance at p < 0.05. Second, significant variations were observed in the quantity and style of communication. South African students used fewer words and more polite language, while Canadian students tended to use more decisive language and provided more explanations and help to their virtual teammates. Third, South African students were more likely to perceive their virtual teammates as "human" and were less concerned whether they were real people or virtual. Lastly, compared with their Canadian counterparts, South African students reported more positive reactions and perceived the assessment to be more accurate. These findings warrant further investigation. [ABSTRACT FROM AUTHOR]

Purpose: While artificial intelligence (AI) has shown its promise in assisting human decision, there exist barriers to adopting AI for decision-making. This study aims to identify barriers in the adoption of AI for automated organisational decision-making. AI plays a key role, not only by automating routine tasks but also by moving into the realm of automating decisions traditionally made by knowledge or skilled workers. The study, therefore, selected respondents who experienced the adoption of AI for decision-making. Design/methodology/approach: The study applied an interpretive paradigm and conducted exploratory research through qualitative interviews with 13 senior managers in South Africa from organisations involved in AI adoption to identify potential barriers to using AI in automated decision-making processes. A thematic analysis was conducted, and AI coding of transcripts was conducted and compared to the manual thematic coding of transcripts with insights into computer vs human-generated coding. A conceptual framework was created based on the findings. Findings: Barriers to AI adoption in decision-making include human social dynamics, restrictive regulations, creative work environments, lack of trust and transparency, dynamic business environments, loss of power and control, as well as ethical considerations. Originality/value: The study uniquely applied the adaptive structuration theory (AST) model to AI decision-making adoption, illustrated the dimensions relevant to AI implementations and made recommendations to overcome barriers to AI adoption. The AST offered a deeper understanding of the dynamic interaction between technological and social dimensions. [ABSTRACT FROM AUTHOR]

Despite having 12 officially recognised languages (with the addition of South African Sign Language on 3 May 2023), South Africa's education system is becoming increasingly monolingual. The current article makes the case for employing artificial intelligence (AI) to improve multilingualism in South Africa, with specific reference to the educational context. The article discusses the value of first language education, particularly in the context of decolonisation, and shows how AI can be leveraged to facilitate education in all South African languages. Google Translate has emerged as the most popular machine translation platform, and with the introduction of AI to Google Translate in 2016, and later improvements in 2020, Google Translate has achieved a level of accuracy that is sufficient for comprehension in a number of language pairs. This platform's accuracy is still insufficient for Sintu languages, but as is argued in the current article, this accuracy can be improved by expanding online corpora and by integrating Google Translate into the classroom. The article makes practical suggestions as to how this can be accomplished. [ABSTRACT FROM AUTHOR]

The purpose of this study was to examine the effectiveness of LMS digital tools used by academics to foster student engagement at the University of Technology in South Africa. International studies have examined how academics encourage student engagement in online learning environments. They also investigated what teachers do and why they do it. The study that explored this problem on the LMS was not discovered by the researcher in a South African context. The intent of this study is to fill the gap in the literature. Participants were 116 academics from the faculties of A (76%) and B (24%). The question posed was: Which of the LMS tools were used effectively by the academics to foster students' engagement? To answer this question, embedded design was used in a mixed-method approach. Data were gathered using a survey questionnaire with both open-ended and closed-ended questions and interviews. Quantitative data were analyzed with the SPSS version 29 frequency distribution and percentage. Qualitative data were analyzed using Atlas.ti version 22. The results showed that 90.5% of the academics concurred that the learning activities on the LMS foster students to interact and engage. Instructional designers should support academics in the selections and the use of appropriate engagement tools on the LMS. The findings revealed that academics used LMS and third-party tools relating to the discussion forums, groups, Collaborate Ultra, Microsoft Teams chats, and WhatsApp to encourage interactivity in an online environment, as well as the development of authentic assessments in the LMS in this era of artificial intelligence. [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]

This article explores artificial intelligence (AI) uptake in selected South African mainstream newsrooms. It seeks to determine the extent to which AI has been adopted and how journalists and editors perceive its appropriation in newsmaking practices. To address these two broad aims, the study used in-depth interviews with journalists and editors. Our findings suggest a slow, varied but methodical uptake of AI practices in South Africa's mainstream newsrooms. We deduced three uses of AI in these newsrooms. The first is what we call the holistic appropriation of AI. The second one is the exclusively technological appropriation of AI, and the last one is the task-specific appropriation of AI. This varied uptake of AI is taking place against a deep-seated skepticism with this technology. The skepticism is driven by fear of job losses, the costs of adopting AI, limited training, ethical issues around AI and its efficacy in the democratic process. On this last point, South African journalists question whether AI can be beneficial to the sustenance of a post-apartheid democratic society. Our argument, therefore, is that the optimism about AI in newsrooms that some researchers find in Europe and US newsrooms cannot be transferred to newsrooms in South Africa. [ABSTRACT FROM AUTHOR]

Background: Africa has the lowest density of healthcare workers in the world and artificial intelligence (AI) technology can assist doctors and patients to bridge that gap. This paper describes the 3 challenges we identified to adapt Ada, an AI-powered health management tool, to assist pregnant women and mothers in South Africa (SA). Challenges and solutions: Localization Maternal and child health challenges in SA differ from those in the West. We adapted Ada’s knowledge base to account for regional differences in incidence and disease presentation. We optimized 51 maternal and pediatric health conditions. Readability: An improved readability leads to better understanding of medical content. We calculated the readability score of Ada’s consumer-facing medical content using the Automated Readability Index and deemed it too high (grade 11.0±1.8, range=5.8-17.5). Using Content Design London’s Readability Guidelines, we reduced the readability score of Ada’s content to below grade 8 (7.4±0.8, range=4.6-10.2) while maintaining medical accuracy. Different approaches to AI: The majority of medical research is conducted in high-income countries and tends to recruit people with high literacy levels. This means the differences in underrepresented populations and low-income countries are not captured in medical research. When this data is applied to technology and AI, it can lead to bias. To reduce this bias, we use a white-box approach. This allows us to deliver solutions to meet the needs of a specific population. Using the expertise of medical doctors with clinical experience in low-income areas, we curated a medical knowledge base that was specific to our target population. Conclusions: AI solutions can only improve health outcomes in low-income countries if they are designed and adapted with the needs of the local population in mind. By partnering with local health departments, AI health companies can assist doctors and patients to reduce the burden on vulnerable healthcare systems. Key messages: White-box AI-approach makes it possible to build AI solutions that capture the differences in underrepresented populations and low-income countries. By partnering with local health departments, AI health companies can assist patients and doctors in reducing the burden on vulnerable healthcare systems. [ABSTRACT FROM AUTHOR]

The HIV prevalence in South Africa is the highest in the world. In 2017 12.6% of the population 7.06 million people were HIV positive6. Almost two thirds of this cohort are on ARVS contributing to South Africa's ARV programme-making it the largest in the world2. Other sectors within the health industry have also contributed to addressing the burden of disease by developing their own sector specific HIV-AIDS programmes. Over time, these programmes have grown from strength to strength and have incorporated components to driver better performance within these programmes. A novel concept in health technology is that of artificial intelligence (AI) and machine learning, which is redefining the health industry and is fast becoming very popular locally and globally. AI has not been fully integrated into the HIV-AIDS field to date. This paper explores the artificial intelligence landscape in the HIV-AIDS arena and assesses if AI can enhance HIV-AIDS programme management. [ABSTRACT FROM AUTHOR]

The prevalence of technology-embedded products, services, and cities, described colloquially as 'smart' technologies and 'smart' cities, has seen a spate of unprecedented growth in recent years. South Africa (SA) has not been left behind, with smartphones, smart watches, and smart voice-controlled virtual personal assistants such as Amazon's Alexa now frequently used. But while these technologies hold great promise to revolutionise homes, offices and cities, their adoption poses challenges to individual and collective interests and wellbeing. After demonstrating the legal and ethical difficulties brought about by the introduction of these technologies, this article explores whether SA legislation is sufficiently robust to address these challenges. While the current legislative landscape addresses certain crucial difficulties - such as the safeguarding of personal data by the Protection of Personal Information Act No. 4 of 2013 ('POPIA') - it is suggested that the position regulating other aspects of smart technology adoption is, in large part, fragmented and ill-equipped to deal with some of the more pressing legal and ethical questions. Our contention is that, not dissimilar to the issues arising from artificial intelligence-based technological adoption, the extant legislative and regulatory frameworks do not go far enough in addressing the many concerns emerging from recent novel technological design, development, and deployment. Not only do smart technologies give rise to unique challenges, so does their deployment within the Global South and in South Africa, in particular. We suggest that appropriate and effective regulatory reform measures be undertaken in SA to provide better ethical guidance and policy prescriptions buttressed by rigorous regulatory oversight. [ABSTRACT FROM AUTHOR]

In South Africa, the growing power demand, challenges of having idle infrastructure, and power delivery issues have become crucial problems. Reliability enhancement necessitates a life-cycle performance analysis of the electrical power transformers. To attain reliable operation and continuous electric power supply, methodical condition monitoring of the electrical power transformer is compulsory. Abrupt breakdown of the power transformer instigates grievous economic detriment in the context of the cost of the transformer and disturbance in the electrical energy supply. On the condition that the state of the transformer is appraised in advance, it can be superseded to reduced loading conditions as an alternative to unexpected failure. Dissolved gas analysis (DGA) nowadays has become a customary method for diagnosing transformer faults. DGA provides the concentration level of various gases dissolved, and consequently, the nature of faults can be predicted subject to the concentration level of the gases. The prediction of fault class from DGA output has so far proven to be not holistically reliable when using conventional methods on account of the volatility of the DGA data in line with the rating and working conditions of the transformer. Several faults are unpredictable using the IEC gas ratio (IECGR) method, and an artificial neural network (ANN) has the hindrance of overfitting. Nonetheless, considering that transformer fault prediction is a classification problem, in this work, a unique classification algorithm is proposed. This applies a binary classification support vector machine (BCSVM). The classification precision is not reliant on the number of features of the input gases dataset. The results indicate that the proposed BCSVM furnishes improved results concerning IECGR and ANN methods traceable to its enhanced generalization capability and constructional risk-abatement principle. [ABSTRACT FROM AUTHOR]

The Fourth Industrial Revolution (4IR) is known to transform and create opportunities for the world of work. However, little is known about how the future workforce, such as university students, are being equipped and exposed to 4IR technologies and ways of thinking in a South African (SA) context. This study’s findings contribute to understanding the influence of organisational culture on the uptake of 4IR technology within higher education (HE) in SA during a pandemic. The study uses Edgar Schein’s theoretical framework to explore the organisational culture at a university in the Gauteng province. The article responds further to the questions on how 4IR technology and principles are understood and applied within the context, and how to investigate to what extent the 4IR is reflected upon or embedded in the university’s culture. A qualitative research design is used, and data are gathered through in-depth, semi-structured interviews from seven purposively selected academic and senior management staff members. Thematic analysis uncovered that the university’s ambitious and competitive culture contributed to a positive uptake of 4IR technology and principles, even pre-COVID-19. Furthermore, the specific influence of the university’s Vice-Chancellor to build 4IR thinking into the university helped shape more 4IR thinking and technologies, such as artificial intelligence, whilst still considering the existing disparities of SA, as a developing country. [ABSTRACT FROM AUTHOR]

Weight‐for‐age (WFA) growth faltering often precedes severe acute malnutrition (SAM) in children, yet it is often missed during routine growth monitoring. Automated interpretation of WFA growth within electronic health records could expedite the identification of children at risk of SAM. This study aimed to develop an automated screening tool to predict SAM risk from WFA growth, and to determine its predictive ability compared with simple changes in weight or WFA z‐score. To develop the screening tool, South African child growth experts (n = 30) rated SAM risk on 100 WFA growth curves, which were then used to train an artificial neural network (ANN) to assess SAM risk from consecutive WFA z‐scores. The ANN was validated in 185 children under five (63 SAM cases; 122 controls) using diagnostic accuracy methodology. The ANN's performance was compared with that of changes in weight or WFA z‐score. Even though experts' SAM risk ratings of the WFA growth curves differed considerably, the ANN achieved a sensitivity of 73.0% (95% confidence interval [CI]: 60.3; 83.4), specificity of 86.1% (95% CI: 78.6; 91.7) and receiver‐operating characteristic curve area of 0.795 (95% CI: 0.732; 0.859) during validation with real cases, outperforming changes in weight or WFA z‐scores. The ANN, as an automated screening tool, could markedly improve the identification of children at risk of SAM using routinely collected WFA growth information. Key messages: Growth faltering preceding severe acute malnutrition (SAM) is poorly identified during routine growth monitoring, and opportunities for preventative interventions are missed.Consistently identifying growth faltering is challenging, even for experts in child growth. Digitized growth monitoring tools incorporating automated pattern recognition by an artificial neural network (ANN) can assist clinicians in identifying growth faltering.In this study, an ANN was trained to identify children at risk of SAM based on weight‐for‐age growth curves, with promising results. With further refinement, the ANN could greatly improve the identification of children with growth faltering and facilitate earlier interventions to prevent SAM. [ABSTRACT FROM AUTHOR]

Despite the tremendous promise offered by artificial intelligence (AI) for healthcare in South Africa, existing policy frameworks are inadequate for encouraging innovation in this field. Practical, concrete and solution-driven policy recommendations are needed to encourage the creation and use of AI systems. This article considers five distinct problematic issues which call for policy development: (i) outdated legislation; (ii) data and algorithmic bias; (iii) the impact on the healthcare workforce; (iv) the imposition of liability dilemma; and (v) a lack of innovation and development of AI systems for healthcare in South Africa. The adoption of a national policy framework that addresses these issues directly is imperative to ensure the uptake of AI development and deployment for healthcare in a safe, responsible and regulated manner. [ABSTRACT FROM AUTHOR]

What sets AI systems and AI-powered medical robots apart from all other forms of advanced medical technology is their ability to operate at least to some degree autonomously from the human health care practitioner and to use machine-learning to generate new, often unforeseen, analysis and predictions. This poses challenges under the current framework of laws, regulations, and ethical guidelines applicable to health care in South Africa. The article outlines these challenges and sets out guiding principles for a normative framework to regulate the use of AI in health care. The article examines three key areas for legal reform in relation to AI in health care. First, it proposes that the regulatory framework for the oversight of software as a medical device needs to be updated to develop frameworks for adequately regulating the use of such new technologies. Secondly, it argues that the present HPCSA guidelines for health care practitioners in South Africa adopt an unduly restrictive approach centred in the outmoded semantics of telemedicine. This may discourage technological innovation that could improve access to health care for all, and as such the guidelines are inconsistent with the national digital health strategy. Thirdly, it examines the common law principles of fault-based liability for medical negligence, which could prove inadequate to provide patients and users of new technologies with redress for harm where fault cannot clearly be attributed to the healthcare practitioner. It argues that consideration should be given to developing a statutory scheme for strict liability, together with mandatory insurance, and appropriate reform of product liability pertaining to technology developers and manufacturers. These legal reforms should not be undertaken without also developing a coherent, human-rights centred policy framework for the ethical use of AI, robotics, and related technologies in health care in South Africa. [ABSTRACT FROM AUTHOR]