Bongs Lainjo
Bongs Lainjo, MASc (Engineering), is a distinguished independent researcher whose work bridges the critical intersection of IT and AI with a strong focus on healthcare and academia. With over 100 articles published on artificial intelligence, his groundbreaking research has been presented at numerous national and international conferences, cementing his reputation as a thought leader.
Bongs' career includes prestigious roles, such as a Senior Advisor to the United Nations, where he provided expert guidance on program management, reproductive health commodity security (RHCS), and evaluation. USAID also sought his expertise, where he served as a Logistics and Management Information Systems Advisor, contributing to global public health initiatives. Additionally, he was Chief of Party/Senior Data Management Advisor at Columbia University, New York.
With over a decade of experience as a professor at top Canadian academic institutions, Bongs has influenced countless students and colleagues. His global career has taken him across Africa, the Pacific Islands, Asia, and the United States, enriching his knowledge and perspectives.
Beyond his academic articles, Bongs is also the author of three books, further showcasing his commitment to advancing knowledge in his areas of expertise. His contributions continue to shape the fields of AI,
Bongs' career includes prestigious roles, such as a Senior Advisor to the United Nations, where he provided expert guidance on program management, reproductive health commodity security (RHCS), and evaluation. USAID also sought his expertise, where he served as a Logistics and Management Information Systems Advisor, contributing to global public health initiatives. Additionally, he was Chief of Party/Senior Data Management Advisor at Columbia University, New York.
With over a decade of experience as a professor at top Canadian academic institutions, Bongs has influenced countless students and colleagues. His global career has taken him across Africa, the Pacific Islands, Asia, and the United States, enriching his knowledge and perspectives.
Beyond his academic articles, Bongs is also the author of three books, further showcasing his commitment to advancing knowledge in his areas of expertise. His contributions continue to shape the fields of AI,
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Streamline by improving indicator causal links at all result levels;
Mitigate duplication of indicators;
Establish authentic contributions between different result levels;
Establish meaningful synergies among different results levels: no lower level result can contribute to more than one upper-level result;
Strengthen the program design;
Promote a common understanding among key actors and
Minimize cost and optimize the number of indicators included in the program.
It's Relevance includes:
Improving intended and unintended intervention results and makes foreign aid more focused with evidence-based results;
Establishing more effective, continuous and sustainable synergies among frontline forces, IPs, Funding Agencies, Stakeholders and Beneficiaries.
To strengthen the knowledge of IPs, PMs and other key stakeholders emphasizing sustainable engagement in program management and implementation.
To address existing nuances, highlighting the synergies that exist among the different result levels of the Strategic Framework and hence facilitating a common ground between potential evaluators and different interested partiesStreamline by improving indicator causal links at all result levels;
To mitigate duplication of indicators;
To Establish authentic contributions between different result levels;
To Establish meaningful synergies among different results levels: no lower level result can contribute to more than one upper upper-level result;Strengthen the program design;
To promote a common understanding among key actors and
To minimize cost and optimize the number of indicators included in the program.
Papers by Bongs Lainjo
when it is put into practice, it has led to relevant dependence, cost inefficiency, and a lack of attention on local
agendas. Conversely, AI is a responsible, culture-sensitive, and scalable approach to traditional aid modalities.
This paper then discusses the flaws of foreign aid and calls for using AI as a sustainable solution to meet
development goals in education, health, and economic growth. This work employed case studies and crosssectional studies of AI utilization in different countries to emancipate its capability to assist communities, enhance
independence, and push forward the United Nations Sustainable Development Goals (UN-SDGs). In particular,
the results highlight the orientation to ethical, inclusion, and partnership approaches to ensure AI’s favorable
developmental impact.
Keywords: Foreign Aid, Artificial Intelligence, Sustainable Development, Education, Healthcare, Economic
Growth, Dependency, Capacity Building
(AI) and its varied impacts on our future. The exploration is guided by
the question; how will the thematic dynamics of AI shape our future? To
achieve this, the paper provides a historical overview of AI, including
its evolution from theoretical beginnings to its present, whereby it is
applied in almost all fields. The paper also explores AI’s transformative
role in various sectors, including healthcare, finance, manufacturing,
and education. The process emphasizes how AI fosters efficiency and
innovation. The researcher has also given significant attention to the
socio-economic benefits of AI, including improved efficiency, healthcare
advancements, and educational accessibility. However, the paper
also addresses the darker aspects, including job displacement, ethical
concerns, and the risks of AI in warfare and security. The discussion
mainly dwells on how AI is dual-edged, a potential enhancer and
disruptor of operations in those areas where it applies. The paper
provides an in-depth analysis of the key themes of AI, especially
autonomy and intelligence augmentation, examining their influences on
societal norms. This scrutiny unfolds the complex relationship between
technological advancement and human existence by exploring its varied
aspects ranging from socio-economic benefits to ethical concerns. The
paper, therefore, aims to unravel the intricate tapestry of AI’s role in
shaping our future trajectory. An inquiry into the issue at hand provides
insights into AI’s potential to revolutionize our world while taking due
consideration of its ethical and social challenges. As such, the paper
advocates for a balanced approach to AI development, considering
its complex relationships with societal norms and ethical standards,
highlighting the need for global cooperation in AI governance. The
future of AI, as discussed in this paper, is a confluence of remarkable
possibilities and significant responsibilities, requiring collective efforts
to harness its full potential responsibly.
Keywords: Artificial Intelligence, Ethical Considerations, Historical
Evolution, Societal Impact, Regulation and Governance, Technological
AdvancementAI in Healthcare Prospects.
and main factors for public health strategy and evaluation. The application of Artificial
Intelligence (AI) in evaluating and interpreting statistics is a guarantee of making the process
much faster and with a lot more precision. Machine learning and deep learning, for example,
can provide methods for big data analysis, filter out variance that would otherwise be
confusing, and find correlations that otherwise would not have been seen with the naked eye
or other methods of analysis. Looking at the state of the topic of vital statistics analysis in the
present, the problems of gaps in data, their quality, and relevance come to the foreground. It
showcases AI solutions in data enrichment, live analysis, and inventive approaches such as
NLP and predictive analysis. Using data from elements that define clinical conditions like
blood pressure and heart rate, AI will give a better picture of population health in that it
enables monitoring of the onset of sicknesses such as hypertension. This paper employs case
studies across different countries to explain how AI has been useful in enhancing the quality
of collected public health data and helping make better policies. It is recommended that
future studies aim to eradicate the existing hurdles, including data aggregation from multiple
sources and AI model bias, to harness AI potential in the sphere of public health.
Keywords: Artificial Intelligence, Vital Statistics, Public Health, Data Analysis, Machine
Learning, Predictive Analytics, Data Privacy, Real-time Processing
innovation, research, and learning. The capability of AI to analyze enormous amounts of data at such
incredibly short times contributes to research advancement across natural sciences, humanities, social
sciences, engineering, and healthcare sciences. For instance, in natural sciences, AI algorithms support
various types of data analysis and simulation, helping to make new discoveries and provide methods and
new approaches to look at existing research methods. AI advances in social sciences employ prediction
modeling and machine learning to enhance economic models and other behavioral analyses. AI has
presented humanities advancements in text analysis and interpretation of history work, augmenting the
research based on historical data with data analysis. In engineering and technology, AI's role is twofold:
enhancing physical security and, at the same time, posing new threats in the form of complex cyber threats.
In a related context, AI’s application for diagnosis and treatment planning has been observed in the
healthcare sector. It has shown the potential capability of improving the care of patients far beyond any
imagined capabilities. Nevertheless, the application of AI in academia comes with some challenges.
Privacy, protection, ethical views, and prejudice enhancement are some of the most significant issues that
should be considered. Despite these challenges, AI creates multi-professional collaboration and advances
in knowledge and performance in various scientific disciplines. AI continues to thrive in the future of
academia, as future advancement holds possible new research horizons, educational improvement, and
world problem-solving. With the rapid evolution of AI, its incorporation into academia and its abuses,
biases, and risks need to be constantly reviewed.
Keywords: Artificial Intelligence, Academia, Data Analysis, Predictive Modelling, Cybersecurity,
Personalized Learning, Ethical Considerations, Interdisciplinary Research
environmental degradation and foster economic growth. This study investigates the transformative potential of
artificial intelligence (AI) in achieving these Sustainable Development Goals (SDGs). Analyzing data from 44
sources, the research highlights AI's capacity to address critical challenges in healthcare, education, environmental
management, economic growth, and gender equality. AI applications in renewable energy, waste management,
disease detection, personalized education, and gender equality are examined. The study also emphasizes the ethical
issues associated with AI, such as algorithmic bias, data privacy breaches, and job displacement. To fully leverage
AI's potential, it is essential to develop intelligent automation governance systems, foster interdisciplinary research
combining AI and sustainability, and promote public-private partnerships. Additionally, enhancing public AI
literacy and implementing eco-friendly AI policies are crucial. The study advocates for a holistic ethical framework
to maximize AI's benefits while mitigating risks, promoting cross-disciplinary collaboration, and establishing
ethical AI standards. By doing so, AI can significantly contribute to a more inclusive, equitable, and sustainable
future.
Keywords: AI, sustainability, SDGs, responsible AI, governance, ethical issues, algorithmic bias, data privacy,
social impacts, job displacement
Keywords: Artificial Intelligence, Machine Learning, Higher Education, Remote Learning, Pedagogical Innovation
Streamline by improving indicator causal links at all result levels;
Mitigate duplication of indicators;
Establish authentic contributions between different result levels;
Establish meaningful synergies among different results levels: no lower level result can contribute to more than one upper-level result;
Strengthen the program design;
Promote a common understanding among key actors and
Minimize cost and optimize the number of indicators included in the program.
It's Relevance includes:
Improving intended and unintended intervention results and makes foreign aid more focused with evidence-based results;
Establishing more effective, continuous and sustainable synergies among frontline forces, IPs, Funding Agencies, Stakeholders and Beneficiaries.
To strengthen the knowledge of IPs, PMs and other key stakeholders emphasizing sustainable engagement in program management and implementation.
To address existing nuances, highlighting the synergies that exist among the different result levels of the Strategic Framework and hence facilitating a common ground between potential evaluators and different interested partiesStreamline by improving indicator causal links at all result levels;
To mitigate duplication of indicators;
To Establish authentic contributions between different result levels;
To Establish meaningful synergies among different results levels: no lower level result can contribute to more than one upper upper-level result;Strengthen the program design;
To promote a common understanding among key actors and
To minimize cost and optimize the number of indicators included in the program.
when it is put into practice, it has led to relevant dependence, cost inefficiency, and a lack of attention on local
agendas. Conversely, AI is a responsible, culture-sensitive, and scalable approach to traditional aid modalities.
This paper then discusses the flaws of foreign aid and calls for using AI as a sustainable solution to meet
development goals in education, health, and economic growth. This work employed case studies and crosssectional studies of AI utilization in different countries to emancipate its capability to assist communities, enhance
independence, and push forward the United Nations Sustainable Development Goals (UN-SDGs). In particular,
the results highlight the orientation to ethical, inclusion, and partnership approaches to ensure AI’s favorable
developmental impact.
Keywords: Foreign Aid, Artificial Intelligence, Sustainable Development, Education, Healthcare, Economic
Growth, Dependency, Capacity Building
(AI) and its varied impacts on our future. The exploration is guided by
the question; how will the thematic dynamics of AI shape our future? To
achieve this, the paper provides a historical overview of AI, including
its evolution from theoretical beginnings to its present, whereby it is
applied in almost all fields. The paper also explores AI’s transformative
role in various sectors, including healthcare, finance, manufacturing,
and education. The process emphasizes how AI fosters efficiency and
innovation. The researcher has also given significant attention to the
socio-economic benefits of AI, including improved efficiency, healthcare
advancements, and educational accessibility. However, the paper
also addresses the darker aspects, including job displacement, ethical
concerns, and the risks of AI in warfare and security. The discussion
mainly dwells on how AI is dual-edged, a potential enhancer and
disruptor of operations in those areas where it applies. The paper
provides an in-depth analysis of the key themes of AI, especially
autonomy and intelligence augmentation, examining their influences on
societal norms. This scrutiny unfolds the complex relationship between
technological advancement and human existence by exploring its varied
aspects ranging from socio-economic benefits to ethical concerns. The
paper, therefore, aims to unravel the intricate tapestry of AI’s role in
shaping our future trajectory. An inquiry into the issue at hand provides
insights into AI’s potential to revolutionize our world while taking due
consideration of its ethical and social challenges. As such, the paper
advocates for a balanced approach to AI development, considering
its complex relationships with societal norms and ethical standards,
highlighting the need for global cooperation in AI governance. The
future of AI, as discussed in this paper, is a confluence of remarkable
possibilities and significant responsibilities, requiring collective efforts
to harness its full potential responsibly.
Keywords: Artificial Intelligence, Ethical Considerations, Historical
Evolution, Societal Impact, Regulation and Governance, Technological
AdvancementAI in Healthcare Prospects.
and main factors for public health strategy and evaluation. The application of Artificial
Intelligence (AI) in evaluating and interpreting statistics is a guarantee of making the process
much faster and with a lot more precision. Machine learning and deep learning, for example,
can provide methods for big data analysis, filter out variance that would otherwise be
confusing, and find correlations that otherwise would not have been seen with the naked eye
or other methods of analysis. Looking at the state of the topic of vital statistics analysis in the
present, the problems of gaps in data, their quality, and relevance come to the foreground. It
showcases AI solutions in data enrichment, live analysis, and inventive approaches such as
NLP and predictive analysis. Using data from elements that define clinical conditions like
blood pressure and heart rate, AI will give a better picture of population health in that it
enables monitoring of the onset of sicknesses such as hypertension. This paper employs case
studies across different countries to explain how AI has been useful in enhancing the quality
of collected public health data and helping make better policies. It is recommended that
future studies aim to eradicate the existing hurdles, including data aggregation from multiple
sources and AI model bias, to harness AI potential in the sphere of public health.
Keywords: Artificial Intelligence, Vital Statistics, Public Health, Data Analysis, Machine
Learning, Predictive Analytics, Data Privacy, Real-time Processing
innovation, research, and learning. The capability of AI to analyze enormous amounts of data at such
incredibly short times contributes to research advancement across natural sciences, humanities, social
sciences, engineering, and healthcare sciences. For instance, in natural sciences, AI algorithms support
various types of data analysis and simulation, helping to make new discoveries and provide methods and
new approaches to look at existing research methods. AI advances in social sciences employ prediction
modeling and machine learning to enhance economic models and other behavioral analyses. AI has
presented humanities advancements in text analysis and interpretation of history work, augmenting the
research based on historical data with data analysis. In engineering and technology, AI's role is twofold:
enhancing physical security and, at the same time, posing new threats in the form of complex cyber threats.
In a related context, AI’s application for diagnosis and treatment planning has been observed in the
healthcare sector. It has shown the potential capability of improving the care of patients far beyond any
imagined capabilities. Nevertheless, the application of AI in academia comes with some challenges.
Privacy, protection, ethical views, and prejudice enhancement are some of the most significant issues that
should be considered. Despite these challenges, AI creates multi-professional collaboration and advances
in knowledge and performance in various scientific disciplines. AI continues to thrive in the future of
academia, as future advancement holds possible new research horizons, educational improvement, and
world problem-solving. With the rapid evolution of AI, its incorporation into academia and its abuses,
biases, and risks need to be constantly reviewed.
Keywords: Artificial Intelligence, Academia, Data Analysis, Predictive Modelling, Cybersecurity,
Personalized Learning, Ethical Considerations, Interdisciplinary Research
environmental degradation and foster economic growth. This study investigates the transformative potential of
artificial intelligence (AI) in achieving these Sustainable Development Goals (SDGs). Analyzing data from 44
sources, the research highlights AI's capacity to address critical challenges in healthcare, education, environmental
management, economic growth, and gender equality. AI applications in renewable energy, waste management,
disease detection, personalized education, and gender equality are examined. The study also emphasizes the ethical
issues associated with AI, such as algorithmic bias, data privacy breaches, and job displacement. To fully leverage
AI's potential, it is essential to develop intelligent automation governance systems, foster interdisciplinary research
combining AI and sustainability, and promote public-private partnerships. Additionally, enhancing public AI
literacy and implementing eco-friendly AI policies are crucial. The study advocates for a holistic ethical framework
to maximize AI's benefits while mitigating risks, promoting cross-disciplinary collaboration, and establishing
ethical AI standards. By doing so, AI can significantly contribute to a more inclusive, equitable, and sustainable
future.
Keywords: AI, sustainability, SDGs, responsible AI, governance, ethical issues, algorithmic bias, data privacy,
social impacts, job displacement
Keywords: Artificial Intelligence, Machine Learning, Higher Education, Remote Learning, Pedagogical Innovation
Context: In an attempt to improve results-based management (RBM) in general and intervention data specifically, and to make foreign aid more focused and strategic with compelling evidence-based results; donors have increasingly teamed up and progressively introduced ubiquitous evaluation processes as an integral component of any program. Over the course of streamlining program implementation, funding agencies in collaboration with recipient governments and other stakeholders have also promoted the availability and utilization of strategic frameworks (SFs). A critical component that continues to confront many development aid Stakeholders is their ability to establish equitable, standard and inclusive strategies that include donors, national governments, implementing agencies, program managers, beneficiaries and oversight systems.
Objective: The general objective of this model is to strengthen the knowledge of Implementing Agencies, Program Managers and other key and relevant stakeholders in LMICs; and to emphasize sustainable engagement by mitigating indicator redundancies and optimizing the results in program management.
Framework & Methodology: The model comprises a set of deterministic criteria simultaneously applied in an attempt to identify the most effective set of indicators in any thematic program area. At the same time, it mitigates many program management nuisances by making indicators and data more trustworthy. Using the model criteria, experts are required to conduct the assessment. The working groups of experts use an analytical approach synonymous with the Delphi methodology. Each indicator during the assessment process is assigned a binary outcome (0,1) based on its performance with the relevant criterion. The resulting composite scores are evaluated against a “gold-standard” or target established by these experts a priori. As a quality control measure, all the teams’ outcomes are finally evaluated based on the degree of intra-team and inter-team concordance. It is only after this level of concordance has been established that the final indicators are selected. The model is thematically generic with an inclusive target audience.
(Keywords: Indicator-screening-matrix, Results-based-management, Gold Standard, Concordance, Binary Outcome, Composite-score.)
in the society. The biggest and evolving challenge in BDCA is the ability to mitigate malware, phishing
and spams.
Keywords: Big data, Cloud computing, Data analytics, Thematic areas, Information technology.
Method: Review of anecdotal evidence and reports on the morbidity of COVID-19 in select countries.
Findings: The devastating effects of the coronavirus are felt across different vulnerable populations. These include the elderly (component of dependence ratio and exclude in review), front line workers, marginalized communities, visible minorities and more. Inadequate and sometimes conflicting remarks by “experts” have only contributed in exacerbating the confusion in the general population. However, compassion and empathy from different communities have had positive effects in mitigating some of the health outcomes like mental health and other health-related effects of the pandemic. Institutional support needs to be strengthened, especially with regard to individual risks and supply chain coordination. The challenge in Africa is especially daunting because of inadequate infrastructure.
Keywords: Coronavirus, front line workers, vulnerable populations, compassion and empathy, supply chain coordination
manufacturing industry in Québec. Numerical taxonomic techniques are
applied in an effort to (1) identify the parameters that can be suitably
used to describe a set of firms; (2) quantify and classify the parameters
in order of importance and (3) factor analytically situate or classify
the variables which make up this industry. The compames are then classified.
The classification is based on activities or variables which
collectively define the sectors' Production Systems. A field study, based
on interviews (questionnaires), is then présentée! and analyzed in an
attempt to develop or identify a functional production System applicable to
a given industry.