AI Ethics for Sustainable Development Goals

Astobiza, Aníbal Monasterio; Toboso, Mario; Aparicio, Manuel; López, Daniel

doi:10.1109/mts.2021.3056294

Cited by 28 publications

(13 citation statements)

References 8 publications

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“…The article contributes to the development of the concept of environmental economics and management by substantiating the new role that AI should play in its framework. Unlike Astobiza et al (2021), Bolton et al (2021), Choi et al (2021), Minkkinen et al (2022), Osipov et al (2022), Popkova et al (2020), Saetra (2021), Visvizi (2022), and Wilson and van der Velden (2022), it has been proved that the existing interpretation of sustainable AI does not allow to unlock the potential of AI to fight new global challenges and therefore needs to be revised. Distinctive features of the proposed new authors' interpretation are: → The transition from the practice of flexible redirection of AI from one industry to another (for example, in healthcare, in the context of the COVID-19 pandemic) to a fixed, stable, and long-term application of sustainable AI in environmental economics and management; → Sustainable AI should be given a new role related to preventive environmental crisis management (as opposed to dealing with current threats, which is a characteristic of the existing interpretation).…”

Section: Discussionmentioning

confidence: 99%

“…The existing interpretation of sustainable AI defines it as smart technology for maintaining stable and balanced development of economic systems (Popkova et al, 2020;Saetra, 2021;Minkkinen et al, 2022;Osipov et al, 2022). Threats to healthcare have become more acute in the context of a pandemic; therefore, based on the existing interpretation, medicine has become the main area of application of sustainable AI (Astobiza et al, 2021;Bolton et al, 2021;Choi et al, 2021;Visvizi, 2022;Wilson and van der Velden, 2022). The advantage of the current interpretation is that it allows flexibly rebuilding a stable AI, applying it in the most demanded areas (Popkova et al, 2022a;Popkova et al, 2022b;Popkova and Sergi, 2022).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Sustainable AI in environmental economics and management: Current trends and post-COVID perspective

Lobova

Bogoviz²,

Alekseev³

2022

Front. Environ. Sci.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Sustainable AI in environmental economics and management: Current trends and post-COVID perspective

Lobova

Bogoviz²,

Alekseev³

2022

Front. Environ. Sci.

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“…Even though power companies collect and retain data, it is difficult to digitize it with modern management software. Data loss, improper configuration, device failure, and illegal access are all risks that are linked . The most significant impediment to the energy sector’s modernization is outdated infrastructure.…”

Section: Challenges and Perspectivesmentioning

confidence: 99%

Recent Advances in Machine Learning Research for Nanofluid-Based Heat Transfer in Renewable Energy System

Sharma¹,

Said

Kumar³

et al. 2022

Energy Fuels

222

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Nanofluids have gained significant popularity in the field of sustainable and renewable energy systems. The heat transfer capacity of the working fluid has a huge impact on the efficiency of the renewable energy system. The addition of a small amount of high thermal conductivity solid nanoparticles to a base fluid improves heat transfer. Even though a large amount of research data is available in the literature, some results are contradictory. Many influencing factors, as well as nonlinearity and refutations, make nanofluid research highly challenging and obstruct its potentially valuable uses. On the other hand, data-driven machine learning techniques would be very useful in nanofluid research for forecasting thermophysical features and heat transfer rate, identifying the most influential factors, and assessing the efficiencies of different renewable energy systems. The primary aim of this review study is to look at the features and applications of different machine learning techniques employed in the nanofluid-based renewable energy system, as well as to reveal new developments in machine learning research. A variety of modern machine learning algorithms for nanofluid-based heat transfer studies in renewable and sustainable energy systems are examined, along with their advantages and disadvantages. Artificial neural networks-based model prediction using contemporary commercial software is simple to develop and the most popular. The prognostic capacity may be further improved by combining a marine predator algorithm, genetic algorithm, swarm intelligence optimization, and other intelligent optimization approaches. In addition to the well-known neural networks and fuzzy- and gene-based machine learning techniques, newer ensemble machine learning techniques such as Boosted regression techniques, K-means, K-nearest neighbor (KNN), CatBoost, and XGBoost are gaining popularity due to their improved architectures and adaptabilities to diverse data types. The regularly used neural networks and fuzzy-based algorithms are mostly black-box methods, with the user having little or no understanding of how they function. This is the reason for concern, and ethical artificial intelligence is required.

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“…Astobiza, et al [38] suggested that we live in a golden age, whereby global issues such as climate change exist, and responses to such issues must be organized at the global scale. Astobiza, et al [38] believe that, as AI progresses, many academics are investigating the prospect of using it to solve societal problems, which is a concept known as AI for social good. AI for social good refers to the use of AI-powered technologies and capabilities to promote public welfare.…”

Section: Empirical Literature Reviewmentioning

confidence: 99%

Human-Centered Artificial Intelligence: The Superlative Approach to Achieve Sustainable Development Goals in the Fourth Industrial Revolution

Mhlanga

2022

Sustainability

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Artificial intelligence (AI) is currently being developed by large corporations, and governments all over the world are yearning for it. AI isn’t a futuristic concept; it is already here, and it is being implemented in a range of industries. Finance, national security, health care, criminal justice, transportation, and smart cities are all examples of this. There are countless examples of AI having a substantial impact on the world and complementing human abilities. However, due to the immense societal ramifications of these technologies, AI is on the verge of disrupting a host of industries, so the technique by which AI systems are created must be better understood. The goal of the study was to look at what it meant to be human-centred, how to create human-centred AI, and what considerations should be made for human-centred AI to achieve sustainability and the SDGs. Using a systematic literature review technique, the study discovered that a human-centred AI strategy strives to create and implement AI systems in ways that benefit mankind and serve their interests. The study also found that a human-in-the-loop concept should be used to develop procedures for creating human-centred AI, as well as other initiatives, such as the promotion of AI accountability, encouraging businesses to use autonomy wisely, to motivate businesses to be aware of human and algorithmic biases, to ensure that businesses prioritize customers, and form multicultural teams to tackle AI research. The study concluded with policy recommendations for human-centred AI to help accomplish the SDGs, including expanding government AI investments, addressing data and algorithm biases, and resolving data access issues, among other things.

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AI Ethics for Sustainable Development Goals

Cited by 28 publications

References 8 publications

Sustainable AI in environmental economics and management: Current trends and post-COVID perspective

Sustainable AI in environmental economics and management: Current trends and post-COVID perspective

Recent Advances in Machine Learning Research for Nanofluid-Based Heat Transfer in Renewable Energy System

Human-Centered Artificial Intelligence: The Superlative Approach to Achieve Sustainable Development Goals in the Fourth Industrial Revolution

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