Socio-ethical implications of using AI in accelerating SDG3 in Least Developed Countries

Wakunuma, Kutoma; Jiya, Tilimbe; Aliyu, Suleiman Onimisi

doi:10.1016/j.jrt.2020.100006

Cited by 32 publications

(10 citation statements)

References 22 publications

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“…According to [14], several factors prevent broader AI adoption, such as difficulties with data quality and legal issues. The findings from the study also corroborate with that of [28] and [29] who opined that AI is reliant on infrastructures such as energy and internet connectivity. There are few datasets accessible for AI projects, and the provided data is of uncertain quality.…”

Section: Factors Challenging the Adoption Of Ai In The Energy Sectorsupporting

confidence: 85%

The Awareness and Adoption of Artificial Intelligence for Effective Facilities Management in the Energy Sector

Mobayo

Aribisala

Yusuf

et al. 2021

JD-FEWS

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Digitalization and artificial intelligence (AI) have infiltrated most sectors of the economy, including the energy sector, where they have been extensively investigated. The aim of the study is primarily to assess the awareness of AI in facility management, and to identify the prospects and challenges of the adoption of AI in the energy sector. The study adopted the quantitative methodology approach, using a structured questionnaire to a sample size of 384 respondents. The questionnaire was administered to professionals such as mechanical, civil, electrical, computer, and mechatronics engineers, and project managers within the North-central geopolitical zone of Nigeria. Data gathered was analysed using descriptive analysis (mean value, weighted total, and relative importance index). The study based on findings concludes that there exists high awareness level about the concept of AI in the energy sector. However, regarding the awareness about some selected AI technologies, machine & deep learning, robotics, and speech recognition had high awareness level. The study also concludes that improved energy management, efficiency and transparency, remote reading of energy meters, and improved planning, operation & control of power systems were prevalent prospects of AI adoption. The major challenging factors to the adoption of AI in the Nigerian energy sector are outdated power system infrastructure, cellular technologies, lack of qualified experts and data science skills, and growing threat from cyber-attacks. The study recommends improved awareness and technical know-how of energy sector personnel, and provision of adequate power system infrastructure to provide stable power supply.

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Section: Factors Challenging the Adoption Of Ai In The Energy Sectorsupporting

confidence: 85%

The Awareness and Adoption of Artificial Intelligence for Effective Facilities Management in the Energy Sector

Mobayo

Aribisala

Yusuf

et al. 2021

JD-FEWS

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“…Ensuring health and well-being for all (Goal 3) is also positively impacted by AI, as indicated by a mean score of 7. AI can be harnessed in various ways to strengthen global health [56]. AI-enhanced diagnostics and predictive algorithms can enable healthcare professionals to intervene early, reducing risks and improving maternal health outcomes [56].…”

Section: Discussionmentioning

confidence: 99%

“…AI can be harnessed in various ways to strengthen global health [56]. AI-enhanced diagnostics and predictive algorithms can enable healthcare professionals to intervene early, reducing risks and improving maternal health outcomes [56]. Target 3.2 (T2), which possibly addresses the prevention and treatment of substance abuse, demonstrates the capabilities of AI.…”

Section: Discussionmentioning

confidence: 99%

“…Target 3.2 (T2), which possibly addresses the prevention and treatment of substance abuse, demonstrates the capabilities of AI. AI can enhance the precision of early detection of substance misuse and analyze vast datasets to provide tailored treatment recommendations, leading to more personalized and effective therapeutic approaches [56]. Furthermore, Target 3.3 (T3), potentially centered on reducing traffic-related fatalities, highlights the transformative potential of AI in urban planning and traffic management.…”

Section: Discussionmentioning

confidence: 99%

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The Impact of AI in Sdg Implementation: A Delphi Study

Ametepey,

Aigbavboa,

Thwala

et al. 2024

Preprint

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Artificial Intelligence (AI) emerges as a powerful catalyst poised to reshape the global sustaina-bility landscape by facilitating the attainment of Sustainable Development Goals (SDGs). This comprehensive Delphi study meticulously probes the insights of domain experts, shedding light on the strategic utilization of AI to advance these critical sustainability objectives. Employing rigorous statistical techniques, encompassing measures of central tendency and interquartile de-viation, this research scrutinizes consensus dynamics among experts and elucidates potential variations in their viewpoints. The findings resoundingly convey experts' collective positive perspective regarding AI's pivotal role in propelling the SDGs forward. Through two iterative rounds of extensive discussions, a compelling consensus crystallizes—AI indeed exerts an overall positive impact, exemplified by a robust mean score of 78.8%. Intriguingly, distinct SDGs manifest varied propensities toward AI intervention, with Goals 6, 7, 8, 9, 11, 13, 14 and 15 basking in the radiance of highly positive impacts. Goals 1, 2, 3, 4, 5, 10 and 12 exhibit positive impact scores, indicating a juncture ripe for positive advancements. Meanwhile, Goal 16 and Goal 17 languish with neutral scores, signifying a juncture demanding nuanced deliberations about AI's impact on peace, justice, and strong institutions as well as on partnerships for the goals respectively. This paper underscores AI as a formidable instrument poised to address humanity's most pressing challenges while harmonizing seamlessly with the overarching SDG objectives. It gracefully dovetails into established practices across pivotal domains such as health, education, and resilient infrastructures, amplifying the collective global endeavor to navigate the path toward a more sustainable future.

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“…Economical production needs for a more innovative switching to develop outcome-based commercial model. Study using a SWOT (strengths, weaknesses, opportunities, threats) analysis shows that AI involved in the acceleration of Sustainable Development Goal 3 (SDG3), to highlight socio-ethical implications [17]. Advanced healthcare device that tells us quick clinical Artificial intelligence become useful technology with enormous capacity and performance.…”

Section: Delivering Mass Healthcare With Sociofinancial Attributesmentioning

confidence: 99%

Transforming Paradigm of Artificial Intelligence for Scalable Clinical Healthcare

Tiwari¹

2021

Adv. Mater. Lett.

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Numerous categories of AI utilized by customers and providers of care with functions consist of diagnosis, treatment recommendations, organizational actions, patient engagement and adherence [1][2][3]. Currently, use of the deep-learning technology, computing process and cloud storage with big data analytics enhanced the decision power in healthcare sector. Convergence of TechnologiesRevolutionize the healthcare ecosystem developed through several additional technologies such as Cloud, IOT, AI, big data & blockchain etc., which supplements building of electronic health records and services in easily pattern [4][5][6]. In medicine, AI impact at three levels: for clinicians by precise image understanding; for health practices, by enhancing workflow; and for patients, by allowing them to practice their own data [7]. Artificial Intelligence is steadily becoming a use-to technology in health care, from diagnosing to prediction performance. Some diseases such as cardiac, cancers and diabetes etc. found to be suitable for AI based technology due to clinical identification of patients accurately while, avoiding risk of critical care. How technologies and analytics can revolutionize the healthcare ecosystem, this can be understandable through market and business [8,9].

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Socio-ethical implications of using AI in accelerating SDG3 in Least Developed Countries

Cited by 32 publications

References 22 publications

The Awareness and Adoption of Artificial Intelligence for Effective Facilities Management in the Energy Sector

The Awareness and Adoption of Artificial Intelligence for Effective Facilities Management in the Energy Sector

The Impact of AI in Sdg Implementation: A Delphi Study

Transforming Paradigm of Artificial Intelligence for Scalable Clinical Healthcare

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