Selected Articles on Artificial Intelligence, Digital Transformation, Governance, Privacy, Data Protection and the SDGs
Open Access Articles
Alben, Alexander. "When artificial intelligence and big data collide—How data aggregation and predictive machines threaten our privacy and autonomy." The AI Ethics Journal 1.1 (2020).
Artificial Intelligence and Big Data represent two profound technology trends. Professor Alben’s article explores how Big Data feeds AI applications and makes the case that necessity to monitor such applications has become more immediate and consequential to protect our civil discourse and personal autonomy, especially as they are expressed on social media. Like many of the revolutionary technologies that preceded it, ranging from broadcast radio to atomic power, AI can be used for purposes that benefit human beings and purposes that threaten our very existence. The challenge for the next decade is to make sure that we harness AI with appropriate safeguards and limitations.
Appaya , Sharmista, David Porteous and Axel Rifon Pérez (2024), Powering progress: How Digital Public Infrastructure is transforming Latin America and the Caribbean. World Bank Blogs
As the digital revolution sweeps across Latin America and the Caribbean (LAC), Digital Public Infrastructure (DPI) is at the heart of this shift. DPI refers to the essential systems that make digital services accessible to everyone. It’s the common digital plumbing that supports digital identities, payment systems, and data-sharing networks. But how is the LAC region progressing in building these critical digital services? This blog provides a snapshot of the key insights from a joint report developed by the World Bank Group and the Inter-American Development Bank (IADB). The report delves into the specific context of DPI in LAC, highlighting its importance, current status, and the challenges and opportunities ahead.
Barretta, Shermaine A.M. and Eraldine S. Williams-Shakespeare. A Study of Digitalization of Higher Education Institutions in the Caribbean. (2024). Journal of Comparative & International Higher Education, 16(2).
As technology integration advances, higher education institutions (HEIs) are experiencing varying degrees of digitalization of their systems, processes and services. This qualitative study explores the status of technology integration and the digital infrastructure of five higher education institutions within the Caribbean. It seeks to answer three questions: i) what is the level of digitization in the institutions’ systems? ii) what is the status of technology integration in the teaching-learning processes in the institutions? iii) what types of digital infrastructures are in place to support the institutional functions? The analysis of the data reveals advances in the digitalization of a number of areas including communication processes, administrative processes, the student life cycle processes and in teaching and learning. This study provides important insights into the evolving landscape of digitalization of higher education within the Caribbean, and should serve to inform policy and practice in this important area.
Ezeugwa, F. A. et al. (2024), “Artificial Intelligence, Big Data, and Cloud Infrastructures: Policy Recommendations for Enhancing Women’s Participation in the Tech-Driven Economy,” Journal of Engineering Research and Reports, vol. 26, No. 6.
This study investigates the underrepresentation of women in Artificial Intelligence (AI), Big Data, and Cloud Infrastructures, exploring the barriers and challenges they face and assessing the effectiveness of current policies and initiatives to promote gender diversity within the tech industry. Employing quantitative research methods, the study used a survey distributed to 572 female professionals in tech-related roles across various industries, achieving a 67.9% response rate. Multiple regression analysis was utilized to test four main hypotheses concerning barriers to entry and advancement, the inclusivity of educational programs, the impact of diverse teams on innovation and performance, and the effectiveness of gender-inclusive policies. Key findings indicate that the type of organization and specific tech sectors significantly influence the barriers experienced by women. Notably, gender diversity within teams correlates strongly with improved innovation and performance. However, educational and training programs often fail to be sufficiently inclusive, underscoring the need for programs better tailored to women's needs in tech fields.
Heo, Jung Hwan. "Ethical Review in the Age of Artificial Intelligence." The AI Ethics Journal 2.2 (2021).
This paper serves to acquaint the layperson and other stakeholders involved in AI development with the current progress of AI and the ethical concerns that must be addressed before significant advancements. The subject of discussion is narrowed down to three fields of AI’s most prominent use: (1) the internet; (2) the automotive industry; and (3) the healthcare industry. For each sector, the foundation of the domain-specific AI technique is introduced, the benefits and ethical ramifications are discussed, and a final cost-benefit analysis is provided.
Martínez Pinto, C. (2024). Diversity, Equity, and Inclusion in Practice: Responsible AI Use Cases from Latin America and the Caribbean. Social Innovations Journal, 23.
Public Interest Technology (PIT) offers an opportunity for professionals with different backgrounds and a range of technical and/or core skills to leverage digital technologies to the service of the common good. Among its key features, PIT advocates for a human rights-centered design, development, and deployment of technology, as well as to ground a responsible use of tech in the principles of Diversity, Equity, and Inclusion (DEI). Through a Global South lens, this article provides three examples of how DEI principles have gone from theory to practice in the Latin America and the Caribbean (LAC) region.
Özbek, Mehmet Erdal. "The Age of Digitalization in Industry: From Digital Twins to Digital Product Passport" Journal of Artificial Intelligence and Data Science 4.1 (2024): 11-21.
In the age of digitalization, new tools are emerging everyday accommodating Metaverse. Integration of digital world to the real world requires a flexible transition framework demonstrated by digital twins (DTs). By definition, DTs constitute a foundation enabling seamless connection using the existing and upcoming emerging technologies. The industry does not exempt from this shift as the digital transformation of industry is already on the way of updating from Industry 4.0 to 5.0. Currently, any transformation attempt is tightly associated to the sustainability development goals. The circular economy requirements challenge the efficiency of the ongoing production mechanisms phrased as smart manufacturing.
Pacheco, B. G., & Pacheco, M. H. (2020). Digital business service transformation of Caribbean economies : a path to sustainability. Small States & Territories, 3(2), 413-432.
The convergence of economic globalisation and the rise of automation has shifted the economic drivers of many countries away from manufacturing to knowledge-intensive service industries. Caribbean states however, continue to lag their counterparts in other emerging economies, many of whom have embraced the opportunities provided by digital technologies to engage in the global economy. Thus far, attempts to spur innovation and diversify beyond the traditional sectors of tourism and primary commodities that drive most Caribbean economies have met with only modest success. Many of these efforts have been stymied by the institutional, location and capacity constraints characteristic of small island states. This paper analyses the opportunities offered by Information and Communications Technology (ICT) to overcome the limitations of thin resource endowments by revolutionizing existing business models and altering how economic value is created. It identifies several challenges that policy makers will have to overcome and provides recommendations for implementing a developmental model that applies ICT to transform the non-tourism service sector.
Sayfullaev, S. (2023). The Current State of Computer Security in Corporate Networks. Digital Transformation and Artificial Intelligence, 1(4), 130–134.
A meaningful analytical review of the state of affairs in the field of information protection in recent decades has been carried out. The reasons for the origin of computer crimes are explained. Possible threats to the data are being considered. Four levels of data protection are presented in detail. Special attention is paid to the problems of network security. Provides and describes basic information on the levels of safe network systems in accordance with the criteria.
Schneider, J. et al. (2023), “Artificial Intelligence Governance for Businesses,” Information systems management, vol. 40, No. 3, Boston, Taylor & Francis.
While artificial intelligence (AI) governance is thoroughly discussed on a philosophical, societal, and regulatory level, few works target companies. We address this gap by deriving a conceptual framework from literature. We decompose AI governance into governance of data, machine learning models, and AI systems along the dimensions of who, what, and how "is governed." This decomposition enables the evolution of existing governance structures. Novel, business-specific aspects include measuring data value and novel AI governance roles.
Vinuesa, R. et al. (2020), “The role of artificial intelligence in achieving the Sustainable Development Goals,” Nature communications, vol. 11, No. 1, London, Nature Publishing Group UK.
The emergence of artificial intelligence (AI) and its progressively wider impact on many sectors requires an assessment of its effect on the achievement of the Sustainable Development Goals. Using a consensus-based expert elicitation process, we find that AI can enable the accomplishment of 134 targets across all the goals, but it may also inhibit 59 targets. However, current research foci overlook important aspects. The fast development of AI needs to be supported by the necessary regulatory insight and oversight for AI-based technologies to enable sustainable development. Failure to do so could result in gaps in transparency, safety, and ethical standards.
Zhang, J. & Zhang, Z.-M. (2023), “Ethics and governance of trustworthy medical artificial intelligence,” BMC Medical Informatics and Decision Making, vol. 23, No. 1, England, BioMed Central Ltd.
The growing application of artificial intelligence (AI) in healthcare has brought technological break‑throughs to traditional diagnosis and treatment, but it is accompanied by many risks and challenges. These adverse effects are also seen as ethical issues and affect trustworthiness in medical AI and need to be managed through identification, prognosis and monitoring. We adopted a multidisciplinary approach and summarized five subjects that influence the trustworthiness of medical AI: data quality, algorithmic bias, opacity, safety and security, and responsibility attribution, and discussed these factors from the perspectives of technology, law, and healthcare stakeholders and institutions. The ethical framework of ethical values ethical principles ethical norms is used to propose corresponding ethical governance countermeasures for trustworthy medical AI from the ethical, legal, and regulatory aspects.
ECLAC Caribbean Library – Selected References
Chen, F.-H. (2024), “Role and Challenges of Generative Artificial Intelligence in Higher Education: A Case Study of Sustainable Development Goals Curriculum,” Journal of Education Research (Print), No. 365, Taipei, Angle Publishing Co., Ltd.
Generative Artificial Intelligence (GAI) has an undeniable impact on higher education. This article aims to explore its innovative applications and practices in the context of Sustainable Development Goals (SDGs) courses at universities. Firstly, we elucidate the basic principles and potential of GAI, and conduct an in-depth analysis of the challenges faced in implementing SDGs courses. Furthermore, this article examines the feasibility of utilizing GAI's text generation techniques to customize teaching materials and facilitate interactive learning among students. A case study of a course template is shared to demonstrate the concrete application and effectiveness of GAI in university SDGs courses. Such a teaching methodology helps enhance students' understanding of complex problems and improves the quality of instruction. Simultaneously, we discuss the ethical, technical, and resource-related challenges that may arise when applying this technology.
Foster, M. N. & Rhoden, S. L. N. H. (2020), “The integration of automation and artificial intelligence into the logistics sector: A Caribbean perspective,” Worldwide hospitality and tourism themes, vol. 12, No. 1, Bingley, Emerald Group Publishing Limited.
Artificial intelligence (AI) and automation are technologies that make a global impact by optimizing manual and time-intensive processes using data analytics and robotics, thus making the task more efficient, effective and less time-consuming…The purpose of this paper is to examine statistical data on the understanding of automation and AI within education, the port authority and major operators in the shipping and logistics sector throughout the Caribbean.
Goralski, M. A. & Tan, T. K. (2020), “Artificial intelligence and sustainable development,” The international journal of management education, vol. 18, No. 1, Elsevier Ltd.
Artificial intelligence (AI) is rapidly opening up a new frontier in the fields of business, corporate practices, and governmental policy. The intelligence of machines and robotics with deep learning capabilities have created profound disrupting and enabling impacts on business, governments, and society. They are also influencing the larger trends in global sustainability. As the AI revolution transforms our world, it could herald a utopian future where humanity co-exists harmoniously with machines, or portend a dystopian world filled with conflict, poverty and suffering. More immediately, would AI accelerate our progress on the United Nations (UN) Sustainable Development Goals (SDGs) or bring us further down the path toward greater economic uncertainty, environmental collapse, and social upheaval? What are some of the implications for business leadership and the education of future business leaders? This article aims to address these questions by analyzing the impacts of AI in three case studies. It draws some preliminary inferences for management education and the business of leading corporations in the midst of rapid technological and social change. This study combines the perspectives of business strategy and public policy to analyze the impacts of AI on sustainable development with a specific focus on the advancement of the SDGs.
Janssen, M. et al. (2020), “Data governance: Organizing data for trustworthy Artificial Intelligence,” Government information quarterly, vol. 37, No. 3, Elsevier Inc.
The rise of Big, Open and Linked Data (BOLD) enables Big Data Algorithmic Systems (BDAS) which are often based on machine learning, neural networks and other forms of Artificial Intelligence (AI). As such systems are increasingly requested to make decisions that are consequential to individuals, communities and society at large, their failures cannot be tolerated, and they are subject to stringent regulatory and ethical requirements. However, they all rely on data which is not only big, open and linked but varied, dynamic and streamed at high speeds in real-time. Managing such data is challenging. To overcome such challenges and utilize opportunities for BDAS, organizations are increasingly developing advanced data governance capabilities. This paper reviews challenges and approaches to data governance for such systems, and proposes a framework for data governance for trustworthy BDAS.
Schneider, J. et al. (2023), “Artificial Intelligence Governance for Businesses,” Information systems management, vol. 40, No. 3, Boston, Taylor & Francis.
While artificial intelligence (AI) governance is thoroughly discussed on a philosophical, societal, and regulatory level, few works target companies. We address this gap by deriving a conceptual framework from literature. We decompose AI governance into governance of data, machine learning models, and AI systems along the dimensions of who, what, and how "is governed." This decomposition enables the evolution of existing governance structures. Novel, business-specific aspects include measuring data value and novel AI governance roles.
Last updated: 23 December 2024