The DOI matches its Crossref record; 30 of the 32 references it cites were found in a registry or an index; none of the 36 sources its claims cite is checked for support yet.
ARTIFICIAL INTELLIGENCE IN DEVELOPING COUNTRIES: BRIDGING THE GAP BETWEEN POTENTIAL AND IMPLEMENTATION
Open accessAbstract
This paper examines the role of Artificial Intelligence (AI) in developing countries, focusing on bridging the gap between its vast potential and effective implementation. As AI technologies advance globally, their impact on socio-economic development becomes increasingly critical, particularly in regions with diverse challenges and opportunities. The study investigates the current landscape of AI adoption in developing countries, analyzing the potential benefits, challenges, and ethical considerations. Through a comprehensive review of literature and case studies, the paper explores strategies and solutions for harnessing AI's transformative power in diverse sectors such as healthcare, agriculture, and education. The findings emphasize the importance of capacity building, public-private partnerships, and tailored policy frameworks to address infrastructure limitations and skill gaps. The research contributes to a nuanced understanding of the opportunities and complexities surrounding AI implementation in developing countries, providing insights for policymakers, practitioners, and scholars seeking to navigate this evolving technological landscape Keywords: Artificial Intelligence; Global Connectivity; Emerging Technologies; Organizational Resilience; Sustainable Growth; Developing Country.
Overview
AI snapshot
Written from the paper’s full text.
TL;DR
This review examines AI adoption in developing countries, arguing that AI can support economic growth, healthcare, education, agriculture, finance, and governance but faces infrastructure, skills, and ethical barriers. It proposes strategies such as infrastructure investment, capacity building, public-private partnerships, and ethical/regulatory frameworks to bridge potential and implementation.
Key findings
- AI adoption in developing countries is increasing in sectors such as healthcare, agriculture, education, and governance, while developed countries lead due to stronger infrastructure, R&D investment, skilled workforces, and conducive regulation.
- Potential benefits include economic growth and job creation, improved healthcare diagnostics and remote monitoring, personalized education, and precision agriculture; the paper cites the McKinsey Global Institute that AI could contribute significantly to global economic output by 2030, without giving a numeric value.
- Challenges include infrastructure limitations (high-speed internet, computing resources, data storage, power supply), skill gaps and education challenges, and ethical/societal concerns such as algorithmic bias, transparency, privacy, security, job displacement, and inequality.
- Case studies illustrate implementation: India’s CropIn for precision agriculture, Kenya’s Ilara Health for AI diagnostics, Brazil’s INPE for Amazon deforestation monitoring, Rwanda’s Smart Africa/Zindi for personalized learning, and Vietnam’s NAPAS for AI-driven financial inclusion chatbots. Strategies proposed include capacity building, infrastructure development, public-private partnerships, national AI strategies, incentives, and regulatory frameworks.
Methods
- The paper is a comprehensive review of literature and case studies, synthesizing existing literature, analyzing pertinent case studies, and scrutinizing real-world examples.
- No primary data collection, sample, or quantitative analysis is reported; no systematic search protocol or inclusion criteria are described.
- Case studies from selected developing countries—India, Kenya, Brazil, Rwanda, and Vietnam—are used to illustrate AI implementation.
Limitations
- The paper does not state explicit limitations.
- Evident from the text: it is a narrative review without a described systematic search strategy, inclusion criteria, or critical appraisal, so the selection and synthesis may be non-exhaustive.
- Evident from the text: the case studies are brief and descriptive, with no outcome metrics or evaluation data, and claims about benefits rely on cited secondary sources.
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Claims the paper makes
31 Not checked yet · 29 No basis given · 20 Own interpretation
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In the paper
“Economic, infrastructural, and educational disparities create hurdles that impact the equitable distribution of AI benefits.”
§ ARTIFICIAL INTELLIGENCE IN DEVELOPING COUNTRIES: BRIDGING THE GAP BETWEEN POTENTIAL AND IMPLEMENTATION > INTRODUCTION
plaindirect- Asserted: passes — The authors put it forward as true.
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In the paper
“Notably, AI is being deployed in diverse sectors such as healthcare, agriculture, education, and governance to unlock efficiencies and drive innovation.”
§ ARTIFICIAL INTELLIGENCE IN DEVELOPING COUNTRIES: BRIDGING THE GAP BETWEEN POTENTIAL AND IMPLEMENTATION > AI Adoption Trends in Developing Nations:
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In the paper
“As AI adoption proliferates, it is anticipated that the creation of new markets, products, and services will stimulate economic activity.”
§ ARTIFICIAL INTELLIGENCE IN DEVELOPING COUNTRIES: BRIDGING THE GAP BETWEEN POTENTIAL AND IMPLEMENTATION > Potential Benefits of AI in Developing Contexts
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In the paper
“AI applications also assist in automating administrative tasks, allowing educators to focus on interactive and personalized teaching methods (UNESCO, 2019)”
- Also stated in: § The Role of AI in Specific Sectors · plain
“Furthermore, AI facilitates administrative tasks, freeing up educators to focus on innovative teaching methods (UNESCO, 2019).”
§ ARTIFICIAL INTELLIGENCE IN DEVELOPING COUNTRIES: BRIDGING THE GAP BETWEEN POTENTIAL AND IMPLEMENTATION > Potential Benefits of AI in Developing Contexts
What the sources say
- Not checked yetcites UNESCO, 2019
plaindirect- Asserted: passes — The authors put it forward as true.
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- Also stated in: § The Role of AI in Specific Sectors · plain
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In the paper
“While the transformative potential of AI is evident, its adoption and integration present a distinctive set of challenges and opportunities for developing countries (Sood, Sharma, and Bhardwaj, 2022;Biswas, 2020).”
§ ARTIFICIAL INTELLIGENCE IN DEVELOPING COUNTRIES: BRIDGING THE GAP BETWEEN POTENTIAL AND IMPLEMENTATION > INTRODUCTION
What the sources say
- Not checked yetcites Sood, Sharma, and Bhardwaj, 2022
- Not checked yetcites Biswas, 2020
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In the paper
“AI-powered diagnostics can enhance the accuracy and speed of disease identification, especially in regions with limited access to healthcare professionals.”
§ ARTIFICIAL INTELLIGENCE IN DEVELOPING COUNTRIES: BRIDGING THE GAP BETWEEN POTENTIAL AND IMPLEMENTATION > Potential Benefits of AI in Developing Contexts
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A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“AI technologies have the potential to serve as catalysts for economic growth in developing countries.”
§ ARTIFICIAL INTELLIGENCE IN DEVELOPING COUNTRIES: BRIDGING THE GAP BETWEEN POTENTIAL AND IMPLEMENTATION > Potential Benefits of AI in Developing Contexts
hedgedconditional- Asserted: passes — The authors put it forward as true.
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In the paper
“For instance, AI-powered healthcare applications are enhancing diagnostic capabilities and healthcare delivery in regions with limited access to medical expertise (Han et al., 2020, Al-Turjman, 2023, Patil and Shankar, 2023).”
§ ARTIFICIAL INTELLIGENCE IN DEVELOPING COUNTRIES: BRIDGING THE GAP BETWEEN POTENTIAL AND IMPLEMENTATION > AI Adoption Trends in Developing Nations:
What the sources say
- Not checked yetcites Han et al., 2020
- Not checked yetcites Al-Turjman, 2023
- Not checked yetcites Patil and Shankar, 2023
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In the paper
“Reliable access to high-speed internet, robust computing resources, and advanced data storage facilities are often prerequisites for optimal AI deployment.”
§ ARTIFICIAL INTELLIGENCE IN DEVELOPING COUNTRIES: BRIDGING THE GAP BETWEEN POTENTIAL AND IMPLEMENTATION > Potential Benefits of AI in Developing Contexts
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In the paper
“Developing countries also grapple with a shortage of skilled talent proficient in AI technologies, hindering the effective deployment and maintenance of AI systems (Pedro et al., 2019, Dwivedi et al., 2021).”
§ ARTIFICIAL INTELLIGENCE IN DEVELOPING COUNTRIES: BRIDGING THE GAP BETWEEN POTENTIAL AND IMPLEMENTATION > INTRODUCTION
What the sources say
- Not checked yetcites Pedro et al., 2019
- Not checked yetcites Dwivedi et al., 2021
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- Probably not a substantial claim. 39
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A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“AI has the potential to drive innovation, improve public services, and address pressing issues in healthcare, agriculture, and education in developing countries.”
§ ARTIFICIAL INTELLIGENCE IN DEVELOPING COUNTRIES: BRIDGING THE GAP BETWEEN POTENTIAL AND IMPLEMENTATION > INTRODUCTION
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In the paper
“The global ascent of Artificial Intelligence (AI) stands as one of the most transformative phenomena in contemporary technological landscapes, reshaping the way societies operate, businesses function, and individuals interact with information (Mannuru et al., 2023).”
§ ARTIFICIAL INTELLIGENCE IN DEVELOPING COUNTRIES: BRIDGING THE GAP BETWEEN POTENTIAL AND IMPLEMENTATION > INTRODUCTION
What the sources say
- Not checked yetcites Mannuru et al., 2023
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The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“In recent years, developing countries have increasingly embraced AI technologies, recognizing their potential to catalyze economic growth, enhance public services, and address pressing societal challenges.”
§ ARTIFICIAL INTELLIGENCE IN DEVELOPING COUNTRIES: BRIDGING THE GAP BETWEEN POTENTIAL AND IMPLEMENTATION > AI Adoption Trends in Developing Nations:
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In the paper
“The creation of jobs in AI development, maintenance, and oversight becomes essential, contributing to the growth of a skilled workforce and fostering entrepreneurship in emerging technological fields (Arntz et al., 2016)”
§ ARTIFICIAL INTELLIGENCE IN DEVELOPING COUNTRIES: BRIDGING THE GAP BETWEEN POTENTIAL AND IMPLEMENTATION > Potential Benefits of AI in Developing Contexts
What the sources say
- Not checked yetcites Arntz et al., 2016
plaindirect- Asserted: passes — The authors put it forward as true.
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The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“The digital divide, reflective of uneven access to technology, exacerbates these disparities and limits the widespread adoption of AI solutions.”
§ ARTIFICIAL INTELLIGENCE IN DEVELOPING COUNTRIES: BRIDGING THE GAP BETWEEN POTENTIAL AND IMPLEMENTATION > INTRODUCTION
plaindirect- Asserted: passes — The authors put it forward as true.
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The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“The trajectories of AI adoption in developed nations have been shaped by significant investments in research and development, a highly skilled workforce, and a conducive regulatory environment.”
§ ARTIFICIAL INTELLIGENCE IN DEVELOPING COUNTRIES: BRIDGING THE GAP BETWEEN POTENTIAL AND IMPLEMENTATION > AI Adoption Trends in Developing Nations:
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In the paper
“AI applications in crop monitoring, pest control, and predictive analytics contribute to sustainable agricultural practices, ensuring food security and bolstering the livelihoods of farmers (FAO, 2020)”
§ ARTIFICIAL INTELLIGENCE IN DEVELOPING COUNTRIES: BRIDGING THE GAP BETWEEN POTENTIAL AND IMPLEMENTATION > Potential Benefits of AI in Developing Contexts
What the sources say
- Not checked yetcites FAO, 2020
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The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“The adoption of AI in developing nations is driven by a confluence of factors, including technological advancements, increased connectivity, and a growing awareness of the transformative power of AI applications.”
§ ARTIFICIAL INTELLIGENCE IN DEVELOPING COUNTRIES: BRIDGING THE GAP BETWEEN POTENTIAL AND IMPLEMENTATION > AI Adoption Trends in Developing Nations:
plaindirect- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Developed countries, often characterized by robust technological infrastructures and well-established innovation ecosystems, have been at the forefront of AI development and implementation.”
§ ARTIFICIAL INTELLIGENCE IN DEVELOPING COUNTRIES: BRIDGING THE GAP BETWEEN POTENTIAL AND IMPLEMENTATION > AI Adoption Trends in Developing Nations:
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In the paper
“Ethical considerations, such as bias in algorithms and responsible AI use, add complexity to the integration process.”
§ ARTIFICIAL INTELLIGENCE IN DEVELOPING COUNTRIES: BRIDGING THE GAP BETWEEN POTENTIAL AND IMPLEMENTATION > INTRODUCTION
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In the paper
“Artificial Intelligence, characterized by machine learning algorithms, neural networks, and advanced computing capabilities, has evolved from a niche field to a pervasive force driving innovation across diverse sectors (Gupta et al., 2021, Zhang et al., 2021, Ukoba and Jen, 2022, Sanni et al., 2024).”
§ ARTIFICIAL INTELLIGENCE IN DEVELOPING COUNTRIES: BRIDGING THE GAP BETWEEN POTENTIAL AND IMPLEMENTATION > INTRODUCTION
What the sources say
- Not checked yetcites Gupta et al., 2021
- Not checked yetcites Zhang et al., 2021
- Not checked yetcites Ukoba and Jen, 2022
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In the paper
“From voice-activated personal assistants to complex predictive analytics, AI has become an integral part of industries such as healthcare, finance, manufacturing, and beyond.”
§ ARTIFICIAL INTELLIGENCE IN DEVELOPING COUNTRIES: BRIDGING THE GAP BETWEEN POTENTIAL AND IMPLEMENTATION > INTRODUCTION
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In the paper
“Personalized learning platforms powered by AI can adapt to individual student needs, overcoming resource constraints and fostering inclusive education.”
§ ARTIFICIAL INTELLIGENCE IN DEVELOPING COUNTRIES: BRIDGING THE GAP BETWEEN POTENTIAL AND IMPLEMENTATION > Potential Benefits of AI in Developing Contexts
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In the paper
“Automation, driven by AI, can enhance productivity across industries, leading to increased efficiency and competitiveness.”
§ ARTIFICIAL INTELLIGENCE IN DEVELOPING COUNTRIES: BRIDGING THE GAP BETWEEN POTENTIAL AND IMPLEMENTATION > Potential Benefits of AI in Developing Contexts
plaindirect- Asserted: passes — The authors put it forward as true.
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In the paper
“A study by the McKinsey Global Institute indicates that AI technologies could contribute significantly to global economic output by 2030, with developing countries poised to benefit from this growth (Manyika et al., 2017)”
§ ARTIFICIAL INTELLIGENCE IN DEVELOPING COUNTRIES: BRIDGING THE GAP BETWEEN POTENTIAL AND IMPLEMENTATION > Potential Benefits of AI in Developing Contexts
What the sources say
- Not checked yetcites Manyika et al., 2017
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The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Developed countries have witnessed extensive integration of AI across industries, with applications ranging from advanced robotics in manufacturing to sophisticated AI algorithms in finance and business analytics.”
§ ARTIFICIAL INTELLIGENCE IN DEVELOPING COUNTRIES: BRIDGING THE GAP BETWEEN POTENTIAL AND IMPLEMENTATION > AI Adoption Trends in Developing Nations:
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In the paper
“Education systems are incorporating AI for personalized learning experiences, and governments are exploring AI applications for public service delivery and policy formulation.”
§ ARTIFICIAL INTELLIGENCE IN DEVELOPING COUNTRIES: BRIDGING THE GAP BETWEEN POTENTIAL AND IMPLEMENTATION > AI Adoption Trends in Developing Nations:
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The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“In agriculture, AI-driven precision farming is optimizing resource use and improving yields.”
- Also stated in: § Potential Benefits of AI in Developing Contexts · plain
“Precision agriculture, enabled by AI, optimizes resource use, enhances crop yields, and mitigates environmental impact.”
§ ARTIFICIAL INTELLIGENCE IN DEVELOPING COUNTRIES: BRIDGING THE GAP BETWEEN POTENTIAL AND IMPLEMENTATION > AI Adoption Trends in Developing Nations:
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- Also stated in: § Potential Benefits of AI in Developing Contexts · plain
None of the sources cited for this claim have been checked yet.
In the paper
“While some routine and manual tasks may become automated, AI-driven technologies generate new opportunities that require human expertise.”
§ ARTIFICIAL INTELLIGENCE IN DEVELOPING COUNTRIES: BRIDGING THE GAP BETWEEN POTENTIAL AND IMPLEMENTATION > Potential Benefits of AI in Developing Contexts
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A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The gap in AI implementation, particularly in developing countries, can be bridged through strategic and concerted efforts.”
§ ARTIFICIAL INTELLIGENCE IN DEVELOPING COUNTRIES: BRIDGING THE GAP BETWEEN POTENTIAL AND IMPLEMENTATION > Vietnam: AI-Driven Chatbots for Financial Inclusion:
plaindirect- Asserted: passes — The authors put it forward as true.
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- Rests on an assumption: The proposed strategies (capacity building, infrastructure development, public-private partnerships) are effective in bridging the AI implementation gap in developing countries.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“The Instituto Nacional de Pesquisas Espaciais (INPE) utilizes AI algorithms to analyze satellite imagery and monitor deforestation in real-time.”
§ ARTIFICIAL INTELLIGENCE IN DEVELOPING COUNTRIES: BRIDGING THE GAP BETWEEN POTENTIAL AND IMPLEMENTATION > Precision Agriculture for Smallholder Farmers:
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In the paper
“AI-driven algorithms also optimize trading strategies, improving efficiency and market outcomes (Wang et al., 2020).”
§ ARTIFICIAL INTELLIGENCE IN DEVELOPING COUNTRIES: BRIDGING THE GAP BETWEEN POTENTIAL AND IMPLEMENTATION > The Role of AI in Specific Sectors
What the sources say
- Not checked yetcites Wang et al., 2020
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The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Bias in algorithms, a lack of transparency in decision-making processes, and concerns about data privacy and security are paramount ethical considerations.”
§ ARTIFICIAL INTELLIGENCE IN DEVELOPING COUNTRIES: BRIDGING THE GAP BETWEEN POTENTIAL AND IMPLEMENTATION > Potential Benefits of AI in Developing Contexts
plain- Asserted: passes — The authors put it forward as true.
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In the paper
“In the finance sector, AI plays a pivotal role in risk management, fraud detection, and customer service.”
§ ARTIFICIAL INTELLIGENCE IN DEVELOPING COUNTRIES: BRIDGING THE GAP BETWEEN POTENTIAL AND IMPLEMENTATION > The Role of AI in Specific Sectors
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The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“The Smart Africa initiative collaborates with companies like Zindi Africa to implement AI-driven platforms that provide personalized learning content.”
§ ARTIFICIAL INTELLIGENCE IN DEVELOPING COUNTRIES: BRIDGING THE GAP BETWEEN POTENTIAL AND IMPLEMENTATION > Precision Agriculture for Smallholder Farmers:
plaindirect- Asserted: passes — The authors put it forward as true.
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A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Developing countries can benefit from partnerships with tech companies, research institutions, and international organizations.”
§ ARTIFICIAL INTELLIGENCE IN DEVELOPING COUNTRIES: BRIDGING THE GAP BETWEEN POTENTIAL AND IMPLEMENTATION > Vietnam: AI-Driven Chatbots for Financial Inclusion:
plainconditional- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper may give no reason why it holds.
- Rests on an assumption: The proposed strategies (capacity building, infrastructure development, public-private partnerships) are effective in bridging the AI implementation gap in developing countries.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Localized research efforts ensure that AI solutions are tailored to the specific needs and challenges of developing countries.”
§ ARTIFICIAL INTELLIGENCE IN DEVELOPING COUNTRIES: BRIDGING THE GAP BETWEEN POTENTIAL AND IMPLEMENTATION > Vietnam: AI-Driven Chatbots for Financial Inclusion:
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
- Rests on an assumption: The proposed strategies (capacity building, infrastructure development, public-private partnerships) are effective in bridging the AI implementation gap in developing countries.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“AI in education in Rwanda aims to bridge educational gaps and improve learning outcomes.”
§ ARTIFICIAL INTELLIGENCE IN DEVELOPING COUNTRIES: BRIDGING THE GAP BETWEEN POTENTIAL AND IMPLEMENTATION > Precision Agriculture for Smallholder Farmers:
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
In the paper
“AI algorithms analyze production data to enhance product quality and adapt manufacturing processes in real-time (Lee et al., 2019)”
§ ARTIFICIAL INTELLIGENCE IN DEVELOPING COUNTRIES: BRIDGING THE GAP BETWEEN POTENTIAL AND IMPLEMENTATION > The Role of AI in Specific Sectors
What the sources say
- Not checked yetcites Lee et al., 2019
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“The social acceptance of AI is crucial for its successful implementation.”
§ ARTIFICIAL INTELLIGENCE IN DEVELOPING COUNTRIES: BRIDGING THE GAP BETWEEN POTENTIAL AND IMPLEMENTATION > Potential Benefits of AI in Developing Contexts
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“This AI-driven approach optimizes resource use, improves yields, and contributes to sustainable agriculture”
§ ARTIFICIAL INTELLIGENCE IN DEVELOPING COUNTRIES: BRIDGING THE GAP BETWEEN POTENTIAL AND IMPLEMENTATION > Precision Agriculture for Smallholder Farmers:
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Accessible and robust infrastructure forms the foundation for effective AI implementation.”
§ ARTIFICIAL INTELLIGENCE IN DEVELOPING COUNTRIES: BRIDGING THE GAP BETWEEN POTENTIAL AND IMPLEMENTATION > Vietnam: AI-Driven Chatbots for Financial Inclusion:
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper may give no reason why it holds.
- Rests on an assumption: The proposed strategies (capacity building, infrastructure development, public-private partnerships) are effective in bridging the AI implementation gap in developing countries.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Brazil is deploying AI to address environmental challenges, particularly in the Amazon rainforest.”
§ ARTIFICIAL INTELLIGENCE IN DEVELOPING COUNTRIES: BRIDGING THE GAP BETWEEN POTENTIAL AND IMPLEMENTATION > Precision Agriculture for Smallholder Farmers:
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“The company Ilara Health utilizes AI-driven diagnostic tools to enhance medical imaging analysis.”
§ ARTIFICIAL INTELLIGENCE IN DEVELOPING COUNTRIES: BRIDGING THE GAP BETWEEN POTENTIAL AND IMPLEMENTATION > Precision Agriculture for Smallholder Farmers:
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The key strategy for bridging AI gap includes capacity building, infrastructure development, public-Private Partnerships as shown in the figure 2.”
§ ARTIFICIAL INTELLIGENCE IN DEVELOPING COUNTRIES: BRIDGING THE GAP BETWEEN POTENTIAL AND IMPLEMENTATION > Vietnam: AI-Driven Chatbots for Financial Inclusion:
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may state two separate claims as one.
- Rests on an assumption: The proposed strategies (capacity building, infrastructure development, public-private partnerships) are effective in bridging the AI implementation gap in developing countries.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Artificial Intelligence (AI) implementation introduces ethical challenges that necessitate thoughtful strategies to ensure fairness, address bias, and uphold privacy and security.”
§ ARTIFICIAL INTELLIGENCE IN DEVELOPING COUNTRIES: BRIDGING THE GAP BETWEEN POTENTIAL AND IMPLEMENTATION > Vietnam: AI-Driven Chatbots for Financial Inclusion:
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it. The paper may give no reason why it holds. It may state two separate claims as one.
- Rests on an assumption: AI technologies can be deployed in developing countries in a way that ensures fairness, privacy, and security and benefits all segments of society.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Knowledge exchange, joint research projects, and sharing best practices contribute to a collective effort in bridging the AI gap.”
§ ARTIFICIAL INTELLIGENCE IN DEVELOPING COUNTRIES: BRIDGING THE GAP BETWEEN POTENTIAL AND IMPLEMENTATION > Vietnam: AI-Driven Chatbots for Financial Inclusion:
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper may give no reason why it holds. It may state two separate claims as one.
- Rests on an assumption: The proposed strategies (capacity building, infrastructure development, public-private partnerships) are effective in bridging the AI implementation gap in developing countries.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“These chatbots enable secure and convenient financial transactions, fostering financial inclusion in regions with limited banking infrastructure.”
§ ARTIFICIAL INTELLIGENCE IN DEVELOPING COUNTRIES: BRIDGING THE GAP BETWEEN POTENTIAL AND IMPLEMENTATION > Vietnam: AI-Driven Chatbots for Financial Inclusion:
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: unclear — It is unclear why the paper says it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“This AI-driven approach helps authorities detect illegal logging and enforce environmental regulations.”
§ ARTIFICIAL INTELLIGENCE IN DEVELOPING COUNTRIES: BRIDGING THE GAP BETWEEN POTENTIAL AND IMPLEMENTATION > Precision Agriculture for Smallholder Farmers:
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Ilara Health's portable diagnostics also facilitate healthcare delivery in remote areas, addressing the challenges of distance and limited medical infrastructure.”
§ ARTIFICIAL INTELLIGENCE IN DEVELOPING COUNTRIES: BRIDGING THE GAP BETWEEN POTENTIAL AND IMPLEMENTATION > Precision Agriculture for Smallholder Farmers:
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds. It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“In the retail sector, AI applications include demand forecasting, personalized marketing, and inventory management.”
§ ARTIFICIAL INTELLIGENCE IN DEVELOPING COUNTRIES: BRIDGING THE GAP BETWEEN POTENTIAL AND IMPLEMENTATION > The Role of AI in Specific Sectors
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“AI applications in education include personalized learning platforms, intelligent tutoring systems, and automated grading systems.”
§ ARTIFICIAL INTELLIGENCE IN DEVELOPING COUNTRIES: BRIDGING THE GAP BETWEEN POTENTIAL AND IMPLEMENTATION > The Role of AI in Specific Sectors
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Public-private partnerships can facilitate the sharing of resources, expertise, and data.”
§ ARTIFICIAL INTELLIGENCE IN DEVELOPING COUNTRIES: BRIDGING THE GAP BETWEEN POTENTIAL AND IMPLEMENTATION > Vietnam: AI-Driven Chatbots for Financial Inclusion:
plainconditional- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
- Rests on an assumption: The proposed strategies (capacity building, infrastructure development, public-private partnerships) are effective in bridging the AI implementation gap in developing countries.
None of the sources cited for this claim have been checked yet.
In the paper
“AI transforms manufacturing processes through predictive maintenance, quality control, and supply chain optimization.”
§ ARTIFICIAL INTELLIGENCE IN DEVELOPING COUNTRIES: BRIDGING THE GAP BETWEEN POTENTIAL AND IMPLEMENTATION > The Role of AI in Specific Sectors
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“Predictive analytics optimizes pricing strategies and inventory levels, reducing costs and enhancing profitability (Verhoef et al., 2020)”
§ ARTIFICIAL INTELLIGENCE IN DEVELOPING COUNTRIES: BRIDGING THE GAP BETWEEN POTENTIAL AND IMPLEMENTATION > The Role of AI in Specific Sectors
What the sources say
- Not checked yetcites Verhoef et al., 2020
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“Ensuring ethical AI practices safeguards against potential misuse and supports the responsible and beneficial deployment of AI technologies in developing countries (Floridi et al., 2021, Murphy and Barr, 2022, Shneiderman, 2020)”
§ ARTIFICIAL INTELLIGENCE IN DEVELOPING COUNTRIES: BRIDGING THE GAP BETWEEN POTENTIAL AND IMPLEMENTATION > Legal and Ethical Considerations for AI in Developing Countries:
What the sources say
- Not checked yetcites Floridi et al., 2021
- Not checked yetcites Murphy and Barr, 2022
- Not checked yetcites Shneiderman, 2020
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. It may state two separate claims as one.
- Rests on an assumption: AI technologies can be deployed in developing countries in a way that ensures fairness, privacy, and security and benefits all segments of society.
In the paper
“Additionally, fostering diversity within AI development teams and engaging with stakeholders in the decision-making process helps identify and rectify biases, ensuring that AI applications are fair and equitable (Suresh, and Guttag, 2019)”
§ ARTIFICIAL INTELLIGENCE IN DEVELOPING COUNTRIES: BRIDGING THE GAP BETWEEN POTENTIAL AND IMPLEMENTATION > Vietnam: AI-Driven Chatbots for Financial Inclusion:
What the sources say
- Not checked yetcites Suresh, and Guttag, 2019
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The challenges and opportunities of AI in developing countries extend beyond national borders.”
§ ARTIFICIAL INTELLIGENCE IN DEVELOPING COUNTRIES: BRIDGING THE GAP BETWEEN POTENTIAL AND IMPLEMENTATION > Regulatory Frameworks to Encourage Responsible AI Use:
plain- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Developing countries face challenges in infrastructure, skills, and ethical considerations, yet within these challenges lie opportunities for leapfrogging traditional constraints.”
§ ARTIFICIAL INTELLIGENCE IN DEVELOPING COUNTRIES: BRIDGING THE GAP BETWEEN POTENTIAL AND IMPLEMENTATION > Regulatory Frameworks to Encourage Responsible AI Use:
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper may give no reason why it holds. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“AI is making significant impacts across sectors, from improving healthcare delivery and educational outcomes to optimizing agricultural practices and transforming financial services.”
§ ARTIFICIAL INTELLIGENCE IN DEVELOPING COUNTRIES: BRIDGING THE GAP BETWEEN POTENTIAL AND IMPLEMENTATION > Regulatory Frameworks to Encourage Responsible AI Use:
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The multifaceted applications of AI underscore its versatility in addressing diverse challenges.”
§ ARTIFICIAL INTELLIGENCE IN DEVELOPING COUNTRIES: BRIDGING THE GAP BETWEEN POTENTIAL AND IMPLEMENTATION > Regulatory Frameworks to Encourage Responsible AI Use:
plain- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Precision applications tailored to local needs, coupled with strategic policies, can ensure that the benefits of AI are distributed equitably, contributing to sustainable and inclusive development.”
§ ARTIFICIAL INTELLIGENCE IN DEVELOPING COUNTRIES: BRIDGING THE GAP BETWEEN POTENTIAL AND IMPLEMENTATION > Regulatory Frameworks to Encourage Responsible AI Use:
plainconditional- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper may give no reason why it holds. It may state two separate claims as one.
- Rests on an assumption: The proposed strategies (capacity building, infrastructure development, public-private partnerships) are effective in bridging the AI implementation gap in developing countries.
- Rests on an assumption: AI technologies can be deployed in developing countries in a way that ensures fairness, privacy, and security and benefits all segments of society.
The paper relies on this without support.
The proposed strategies (capacity building, infrastructure development, public-private partnerships) are effective in bridging the AI implementation gap in developing countries.
“The key strategy for bridging AI gap includes capacity building, infrastructure development, public-Private Partnerships as shown in the figure 2.”
“By employing these strategies, stakeholders can work collaboratively to bridge the gap in AI implementation.”
The paper relies on this without support.
AI technologies can be deployed in developing countries in a way that ensures fairness, privacy, and security and benefits all segments of society.
“Ensuring ethical AI practices safeguards against potential misuse and supports the responsible and beneficial deployment of AI technologies in developing countries (Floridi et al., 2021, Murphy and Barr, 2022, Shneiderman, 2020)”
“The goal is to create an environment where AI technologies contribute to inclusive development, ensuring that the benefits of these innovations reach all segments of society in developing countries.”
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“The deployment of AI-powered chatbots and virtual assistants ensures continuous and responsive customer engagement, reducing service bottlenecks and operational costs (Aderibigbe et al., 2023).”
From Artificial Intelligence Adoption for Sustainable Development in SMEs: Challenges, Strategic Pathways, and Lessons for Developing Economies (2026) · cites this paper as Aderibigbe et al., 2023
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“Existing research often broadly discusses AI readiness or national strategies (Aderibigbe et al., 2023) and identifies common challenges (Shonhe & Kolobe, 2023), but a comprehensive and integrated analysis is notably underexplored.”
From Artificial Intelligence Adoption for Sustainable Development in SMEs: Challenges, Strategic Pathways, and Lessons for Developing Economies (2026) · cites this paper as Aderibigbe et al., 2023
In the citing paper
“Without culturally congruent adaptation, AI risks becoming a tool that widens rather than bridges healthcare inequalities (21, 22).”
From Artificial intelligence in nursing: an integrative review of clinical and operational impacts (2025) · cites this paper as [21]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
Each contradiction between two passages listed here was confirmed by an independent check.
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ ARTIFICIAL INTELLIGENCE IN DEVELOPING COUNTRIES: BRIDGING THE GAP BETWEEN POTENTIAL AND IMPLEMENTATION > Vietnam: AI-Driven Chatbots for Financial Inclusion:
“(Suresh, and Guttag, 2019)”
§ ARTIFICIAL INTELLIGENCE IN DEVELOPING COUNTRIES: BRIDGING THE GAP BETWEEN POTENTIAL AND IMPLEMENTATION > References
“29. Suresh, H., & Guttag, J. (2021). A framework for understanding sources of harm throughout the machine learning life cycle.”
Citations
Cites
High-performance medicine: the convergence of human and artificial intelligence
Topol, E. J. (2019) · Nature Medicine · doi:10.1038/s41591-018-0300-7 · Cited by 10,450
Deep Learning with Differential Privacy
Abadi, M., Chu, A., Goodfellow, I., McMahan, H. B., Mironov, I., Talwar, K., & Zhang, L. (2016) · Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security · doi:10.1145/2976749.2978318 · Cited by 6,578
Floridi, L., Cowls, J., Beltrametti, M., Chatila, R., Chazerand, P., Dignum, V., Luetge, C., Madelin, R., Pagallo, U., Rossi, F., et al. (2018) · Minds and Machines · doi:10.1007/s11023-018-9482-5 · Cited by 4,774
Dwivedi, Y. K., Hughes, L., Ismagilova, E., Aarts, G., Coombs, C., Crick, T., Duan, Y., Dwivedi, R., Edwards, J., Eirug, A., et al. (2021) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2019.08.002 · Cited by 4,611
Artificial intelligence to deep learning: machine intelligence approach for drug discovery
Gupta, R., Srivastava, D., Sahu, M., Tiwari, S., Ambasta, R. K., & Kumar, P. (2021) · Molecular Diversity · doi:10.1007/s11030-021-10217-3 · Cited by 1,473
Artificial intelligence in education : challenges and opportunities for sustainable development
Pedró, F., Subosa, M., Rivas, A., & Valverde, P. (2019) · MINISTERIO DE EDUCACIÓN · Cited by 902
World Development Report 2019: The Changing Nature of Work
Bank, W. (2019) · World Bank · doi:10.1596/978-1-4648-1328-3 · Cited by 889
Explainable AI (XAI): A systematic meta-survey of current challenges and future opportunities
Saeed, W., & Omlin, C. (2023) · Knowledge-Based Systems · doi:10.1016/j.knosys.2023.110273 · Cited by 783
A Framework of Potential Sources of Harm Throughout the Machine Learning Life Cycle
Suresh, H., & Guttag, J. V. (2021) · DSpace@MIT (Massachusetts Institute of Technology) · Cited by 576
Application of deep learning algorithms in geotechnical engineering: a short critical review
Zhang, W., Li, H., Li, Y., Liu, H., Chen, Y., & Ding, X. (2021) · Artificial Intelligence Review · doi:10.1007/s10462-021-09967-1 · Cited by 556
Research works printed but not linked: 4
- Lee, L. H., & Ramesh, S. (2019). Artificial intelligence in manufacturing: A review. Journal of Manufacturing Systems, 53, 261-270.
- Rana, M.M. (2023). Conservation Agriculture for Sustainable Crop Productivity and Economic Return for the Smallholders of Bangladesh: A Systematic Review. Turkish Journal of Agriculture-Food Science and Technology, 11(10), 2009-2015.
- Shaktawat, P., & Swaymprava, S. (2024). Digital Agriculture: Exploring the Role of Information and Communication Technology for Sustainable Development. Ed. Biswajit Mallick and Jyotishree Anshuman published by PMW, New Delhi, p.31. Shneiderman, B. (2020). Bridging the gap between ethics and practice: guidelines for reliable, safe, and trustworthy human-centered AI systems. ACM Transactions on Interactive Intelligent Systems (TiiS), 10(4), 1-31.
- Verhoef, P. C., Kannan, P. K., & Inman, J. J. (2020). From Multi-Channel Retailing to Omni- Channel Retailing: Introduction to the Special Issue on Multi-Channel Retailing. Journal of Retailing, 96(1), 1-6.
Cited by
Sorted by citations
Items 101–172 of 172
When AI Eats the Healthcare World - Is Trusting AI Fed, or Earned? (Preprint)Preprint
Lutfi, S. L., Alzaabi, A. A., & Saat, G. A. M. (2025) · doi:10.2196/preprints.74371
Generative AI Solutions for Unlocking Customer Loyalty and Retention Strategies in Emerging Markets
Ed-Daakouri, I., & Alla, L. (2025) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-0918-7.ch004
AI Algorithms in Sustainable Healthcare
Vișan, A. I., & Neguț, I. (2025) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3693-9725-1.ch001 · Cited by 1
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Metadata
- Authors
- Aderibigbe, A. O., Ohenhen, P. E., Nwaobia, N. K., Gidiagba, J. O., & Ani, E. C.
- Affiliations
- University of Nebraska–Lincoln (US)
- University of Johannesburg (ZA)
- Venue
- Computer Science & IT Research Journal
- Publisher
- Fair East Publishers
- Published
- 2023-12-03
- Volume
- 4
- Issue
- 3
- Pages
- 185-199
- ISSN
- 2709-0043 (ISSN-L)
- 2709-0051
- OpenAlex
- W4389286730
- Semantic Scholar
- 3d7d961a9278d53f004238e83715d66be6fb1b23
- Cited by
- 172
- References
- 32
- Citations by year
- 2026: 62 · 2025: 77 · 2024: 33
- FWCI
- 15.22
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Licence
- Locations
- Computer Science & IT Research Journal (publishedVersion, cc-by-nc)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Ethics and Social Impacts of AI
- Physical Sciences › Computer Science › Information Systems › Internet of Things and AI
- Keywords
- artificial intelligence adoption, developing countries, socio-economic development, AI ethics, health care, capacity building, public-private partnerships, AI policy frameworks, digital infrastructure, skills gap
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
A language model drew the claims from the paper on 2026-10-09, under version 3 of the claim definition (model deepseek-v4.1-flash, prompt extract-v3).
A second model labelled each claim on 2026-10-09 (model jev-1.13-20260917, definition revision gold-guideline-v0.2).
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