sustainability-logo

Journal Browser

Journal Browser

Sustainable Artificial Intelligence for Societal, Business and Environmental Value

A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Sustainable Engineering and Science".

Deadline for manuscript submissions: closed (20 September 2023) | Viewed by 7006

Special Issue Editors


E-Mail Website
Guest Editor
Department of Management, School of Business, University of Nicosia, 24005 Nicosia, Cyprus
Interests: artificial intelligence; blockchain; trust; fintech
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
School of Management, Hefei University of Technology, Hefei 230009, China
Interests: artificial intelligence; big data; social media

E-Mail Website
Guest Editor
Sonora Institute of Technology (ITSON), 85000 Ciudad Obregon, Mexico
Interests: human–computer interaction; ubiquitous and mobile computing; mobile sensing; context awareness; behaviour and context sensing
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Department of Management, School of Business, University of Nicosia, 24005 Nicosia, Cyprus
Interests: digital transformation; AI; cloud computing; future of work; metaverse

E-Mail Website
Guest Editor
School of Management, University of Missouri, Kansas City, MO 64110, USA
Interests: business analytics; artificial intelligence; information security; online privacy

Special Issue Information

Dear Colleagues,

Artificial intelligence (AI) creates opportunities for social, business, and environmental sustainability. For example, it is expected to elevate the process of disease diagnosis, enabling patients to receive improved healthcare (Yu et al., 2018), to improve energy efficiency (Reinisch et al., 2011), to increase the security of critical systems (Karagiannis et al., 2020), and improve our understanding of behavior (Xu et al., 2022), to name but a few examples. At the same time, it also raises challenges regarding sustainability; for example ensuring the sustainability of data privacy, system liability, human autonomy, etc. (Polyviou & Zamani, 2022; Zarifis et al., 2021).

Sustainable AI refers to the movement aimed at transforming the lifecycle of AI products such that they become more sustainable. The AI product life cycle incorporates a wide spectrum of stages from idea generation to training, re-tuning, developing, implementing, and governing AI. Fostering sustainable AI can lead to greater ecological integrity and social justice.

This Special Issue goes beyond technical AI implementations and regards AI as a wider sociotechnical AI system that is compatible with societal, business, and environmental values at the macro-level (e.g., sustaining environmental resources, sustaining jobs, sustaining business continuity, etc.).

The purpose of this Special Issue is to build stronger bridges between the rich and diverse literatures on sustainability and AI with a focus on sustainable AI.

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but not limited to) the following:

  • Sustaining security and trust in the AI era
  • Building sustainable AI ecosystems
  • System liability and accountability for sustainable AI
  • New business models for sustainable AI
  • Sustaining datasets for AI algorithms
  • Sustainable AI and the future of work
  • Sustainable AI and education
  • Sustainable AI and recommender system
  • Geo-economic perspectives of sustainable AI
  • The synergies between AI, blockchain, and sustainability
  • The opportunities AI offers to enhancing renewable energy
  • Sustainable digital transformation
  • The opportunities AI offers to reducing pollution
  • Sustainability and fintech
  • Sustainability and decentralized finance

We look forward to receiving your contributions.

References

Karagiannis, S., Papaioannou, T., Magkos, E., & Tsohou, A. (2020). Game-Based Information Security/Privacy Education and Awareness: Theory and Practice. Lecture Notes in Business Information Processing, 402, 509–525. https://doi.org/10.1007/978-3-030-63396-7_34

Polyviou A., Zamani E.D. (2022). Are we nearly there yet? A desires & realities framework for Europe's AI strategy Information Systems Frontiers. https://doi.org/10.1007/s10796-022-10285-2

Reinisch, C., Kofler, M. J., Iglesias, F., & Kastner, W. (2011). Thinkhome energy efficiency in future smart homes. Eurasip Journal on Embedded Systems, 2011. https://doi.org/10.1155/2011/104617

Yu, K. H., Beam, A. L., & Kohane, I. S. (2018). Artificial intelligence in healthcare. Nature Biomedical Engineering, 2(10), 719–731. https://doi.org/10.1038/s41551-018-0305-z

Zarifis, A., Kawalek, P., & Azadegan, A. (2021). Evaluating If Trust and Personal Information Privacy Concerns Are Barriers to Using Health Insurance That Explicitly Utilizes AI. Journal of Internet Commerce, 20(1), 66–83. https://doi.org/10.1080/15332861.2020.1832817

Xu, W., Sun, J., & Li, M. (2022). Guest editorial: Interpretable AI-enabled online behavior analytics. Internet Research, 32(2), 401–405. https://doi.org/10.1108/INTR-04-2022-683

Dr. Alex Zarifis
Dr. Jianshan Sun
Prof. Dr. Luis A. Castro
Dr. Ariana Polyviou
Dr. Roozmehr Safi
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sustainability is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • artificial intelligence
  • sustainability
  • circular economy
  • digital transformation
  • business models
  • security
  • privacy
  • fintech
  • pollution
  • renewable energy

Published Papers (2 papers)

Order results
Result details
Select all
Export citation of selected articles as:

Research

13 pages, 1869 KiB  
Article
The NFT Purchasing Process and the Challenges to Trust at Each Stage
by Alex Zarifis and Luis A. Castro
Sustainability 2022, 14(24), 16482; https://doi.org/10.3390/su142416482 - 9 Dec 2022
Cited by 6 | Viewed by 3324
Abstract
The unique features of Non-Fungible Tokens (NFT) are becoming increasingly appealing as we spend more of our time online. This increased popularity is nevertheless not free of controversies, and there is a lack of clarity over the final form this digital asset will [...] Read more.
The unique features of Non-Fungible Tokens (NFT) are becoming increasingly appealing as we spend more of our time online. This increased popularity is nevertheless not free of controversies, and there is a lack of clarity over the final form this digital asset will take. While there are some early adopters, the whole NFT ecosystem will have to be clarified for wider adoption, particularly the purchasing process. This research evaluates a model of the purchasing process of NFTs and the role of trust in this process. The validated model identified that the purchasing process of NFTs has four stages and each stage is affected by trust: (1) Trust in the cryptocurrency wallet, (2) trust in the cryptocurrency purchase, (3) trust in the NFT marketplace, and (4) trust in aftersales services. Full article
Show Figures

Figure 1

13 pages, 1350 KiB  
Article
CNN-Based Inspection Module for Liquid Carton Recycling by the Reverse Vending Machine
by Chang Su Lee and Dong-Won Lim
Sustainability 2022, 14(22), 14905; https://doi.org/10.3390/su142214905 - 11 Nov 2022
Cited by 1 | Viewed by 2937
Abstract
To protect our planet, the material recycling of domestic waste is necessary. Since the COVID-19 pandemic began, the volume of domestic waste has surged overwhelmingly, and many countries suffered from poor waste management. Increased demand for food delivery and online shopping led to [...] Read more.
To protect our planet, the material recycling of domestic waste is necessary. Since the COVID-19 pandemic began, the volume of domestic waste has surged overwhelmingly, and many countries suffered from poor waste management. Increased demand for food delivery and online shopping led to a huge surge in plastic and paper waste which came from natural resources. To reduce the consumption of resources and protect the environment from pollution, such as that from landfills, waste should be recycled. One of precious recyclable materials from household waste is liquid cartons that are made of high-quality paper. To promote sustainable recycling, this paper proposes a vision-based inspection module based on convolutional neural networks via transfer learning (CNN-TL) for collecting liquid packaging cartons in the reverse vending machine (RVM). The RVM is an unmanned automatic waste collector, and thus it needs the intelligence to inspect whether a deposited item is acceptable or not. The whole processing algorithm for collecting cartons, including the inspection step, is presented. When the waste is inserted into the RVM by a user after scanning the barcode on the waste, it is relocated to the inspection module, and the item is weighed. To develop the inspector, an experimental set-up with a video camera was built for image data generation and preparation. Using the image data, the inspection agent was trained. To make a good selection for the model, 17 pretrained CNN models were evaluated, and DenseNet121 was selected. To access the performance of the cameras, four different types were also evaluated. With the same CNN model, this paper found the effect of the number of training epochs being set to 10, 100, and 500. In the results, the most accurate agent was the 500-epoch model, as expected. By using the RVM process logic with this model, the results showed that the accuracy of detection was over 99% (overall probability from three inspections), and the time to inspect one item was less than 2 s. In conclusion, the proposed model was verified for whether it would be applicable to the RVM, as it could distinguish liquid cartons from other types of paper waste. Full article
Show Figures

Figure 1

Back to TopTop