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Riding the Waves of Artificial Intelligence in Achieving Sustainable Development Goals: Challenges and Opportunities of AI and Big Data Technologies

A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Development Goals towards Sustainability".

Deadline for manuscript submissions: closed (1 May 2024) | Viewed by 31494

Special Issue Editors

Prof. Dr. Zhenzhong Ma

E-Mail Website
Guest Editor
1. Odette School of Business, University of Windsor, Windsor, ON N9B 3P4, Canada
2. School of Business, Nanjing Audit University, Nanjing 210017, China
Interests: business ethics; CSR; cross-cultural management; sustainability; technology management
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Kun Li

E-Mail Website
Guest Editor
School of Business, Nanjing Audit University, Nanjing 210017, China
Interests: digital transformation; strategic management; platforms; knowledge management; entrepreneurship

Special Issue Information

Dear Colleagues,

Artificial intelligence (AI) and big data (BD) technologies are transforming the world at an unprecedented pace (Gandomi & Haider, 2015). The fast emergence of generative pre-trained transformers further demonstrated the great potential of AI and BD technologies to reshape our society. These technologies offer an opportunity to tackle some of the world's most pressing problems, such as the sustainable development goals (SDGs) of ending poverty, protecting the planet, and ensuring peace and prosperity for all.

AI and BD capabilities can enable better data collection, analysis, and decision-making to assist in achieving the SDGs (Gandomi & Haider, 2015). As a result, AI and BD capabilities are already being used to achieve SDGs in various ways. For instance, AI and BD technologies have been used to enhance food security, improve the efficiency of supply chains, support the provision of health services, improve access to education, and reduce waste.

While AI and BD capabilities are expected to play an increasingly important role in achieving SDGs in the future, the use of AI and BD technologies also poses significant challenges and risks. One of the main challenges is the potential for biases in AI algorithms, which could perpetuate existing inequalities and discrimination. There are also concerns about the misuse of AI and BD technologies, such as the use of facial recognition technology for surveillance purposes. Additionally, there is a risk that AI and BD technologies could exacerbate job displacement and contribute to economic inequality.

This Special Issue aims to focus on the opportunities and challenges in AI and BD capabilities in achieving SDGs. While a few studies have investigated the benefits of AI and BD technologies in helping to achieve SDGs, relatively few have explored serious challenges AI may bring to our society (Perera et al., 2015). Given that the use of AI and BD technologies may pose significant challenges and risks, far more research is needed to address the balance between how Ai and BD technologies can be used to facilitate the obtention of SDGs and how it can be ensured that technologies such as various chatbots are used in a responsible and ethical manner. A broad spectrum of topics related to this theme are particularly welcomed. Policymakers, researchers, and practitioners must work together to develop appropriate governance frameworks and ensure that the benefits of these technologies are shared equitably in order to provide insights and tools which can improve decision-making, enhance efficiency, and promote sustainability in achieving SDGs.

We look forward to receiving your contributions.

Gandomi, A., & Haider, M. (2015). Beyond the hype: Big data concepts, methods, and analytics. International Journal of Information Management, 35(2), 137-144.

Perera, C., Ranjan, R., Wang, L., Khan, S. U., & Zomaya, A.Y. (2015). Big Data privacy in the Internet of Things Era, IT Professional, 17(3), 32-39.

Prof. Dr. Zhenzhong Ma
Prof. Dr. Kun Li
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
  • big data
  • business ethics
  • data analytics
  • information science
  • machine learning
  • organizational management
  • sustainable development goals

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Published Papers (3 papers)

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Research

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23 pages, 6256 KiB  
Article
Improved Machine Learning Model for Urban Tunnel Settlement Prediction Using Sparse Data
by Gang Yu, Yucong Jin, Min Hu, Zhisheng Li, Rongbin Cai, Ruochen Zeng and Vijiayan Sugumaran
Sustainability 2024, 16(11), 4693; https://doi.org/10.3390/su16114693 - 31 May 2024
Cited by 1 | Viewed by 1921
Abstract
Prediction tunnel settlement in shield tunnels during the operation period has gained increasing significance within the realm of maintenance strategy formulation. The sparse settlement data during this period present a formidable challenge for predictive Artificial Intelligence (AI) models, as they may not handle [...] Read more.
Prediction tunnel settlement in shield tunnels during the operation period has gained increasing significance within the realm of maintenance strategy formulation. The sparse settlement data during this period present a formidable challenge for predictive Artificial Intelligence (AI) models, as they may not handle non-stationary relationships effectively or have the risk of overfitting. In this study, we propose an improved machine learning (ML) model based on sparse settlement data. We enhance training data via time series clustering, use time decomposition to uncover latent features, and employ Extreme Gradient Boosting (XGBoost) v1.5.1 with Bayesian Optimization (BO) v1.2.0 for precise predictions. Comparative experiments conducted on different acquisition points substantiate our model’s efficacy, the in-training set yielding a Mean Absolute Error (MAE) of 0.649 mm, Root Mean Square Error (RMSE) of 0.873 mm, Mean Absolute Percentage Error (MAPE) of 3.566, and Coefficient of Determination (R2) of 0.872, and the in-testing set yielding a MAE of 0.717 mm, RMSE of 1.048 mm, MAPE of 4.080, and R2 of 0.846. The empirical results show the superiority of the proposed model compared to simple ML models and a complex neural network model, as it has a lower prediction error and higher accuracy across different sparse settlement datasets. Moreover, this paper underlines that accurate settlement predictions contribute to achieving some Sustainable Development Goals (SDGs). Specifically, preventive tunnel maintenance strategies based on predictive results can enhance tunnels’ long-term operational reliability, which is in accordance with SDG 9 (Industry, Innovation, and Infrastructure) and SDG 11 (Sustainable Cities and Communities). Full article
(This article belongs to the Special Issue Riding the Waves of Artificial Intelligence in Achieving Sustainable Development Goals: Challenges and Opportunities of AI and Big Data Technologies)
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24 pages, 5675 KiB  
Article
Fusion of Deep Sort and Yolov5 for Effective Vehicle Detection and Tracking Scheme in Real-Time Traffic Management Sustainable System
by Sunil Kumar, Sushil Kumar Singh, Sudeep Varshney, Saurabh Singh, Prashant Kumar, Bong-Gyu Kim and In-Ho Ra
Sustainability 2023, 15(24), 16869; https://doi.org/10.3390/su152416869 - 15 Dec 2023
Cited by 22 | Viewed by 6858
Abstract
In recent years, advancements in sustainable intelligent transportation have emphasized the significance of vehicle detection and tracking for real-time traffic flow management on the highways. However, the performance of existing methods based on deep learning is still a big challenge due to the [...] Read more.
In recent years, advancements in sustainable intelligent transportation have emphasized the significance of vehicle detection and tracking for real-time traffic flow management on the highways. However, the performance of existing methods based on deep learning is still a big challenge due to the different sizes of vehicles, occlusions, and other real-time traffic scenarios. To address the vehicle detection and tracking issues, an intelligent and effective scheme is proposed which detects vehicles by You Only Look Once (YOLOv5) with a speed of 140 FPS, and then, the Deep Simple Online and Real-time Tracking (Deep SORT) is integrated into the detection result to track and predict the position of the vehicles. In the first phase, YOLOv5 extracts the bounding box of the target vehicles, and in second phase, it is fed with the output of YOLOv5 to perform the tracking. Additionally, the Kalman filter and the Hungarian algorithm are employed to anticipate and track the final trajectory of the vehicles. To evaluate the effectiveness and performance of the proposed algorithm, simulations were carried out on the BDD100K and PASCAL datasets. The proposed algorithm surpasses the performance of existing deep learning-based methods, yielding superior results. Finally, the multi-vehicle detection and tracking process illustrated that the precision, recall, and mAP are 91.25%, 93.52%, and 92.18% in videos, respectively. Full article
(This article belongs to the Special Issue Riding the Waves of Artificial Intelligence in Achieving Sustainable Development Goals: Challenges and Opportunities of AI and Big Data Technologies)
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Review

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12 pages, 281 KiB  
Review
Riding the Waves of Artificial Intelligence in Advancing Accounting and Its Implications for Sustainable Development Goals
by Yixuan Peng, Sayed Fayaz Ahmad, Ahmad Y. A. Bani Ahmad, Mustafa S. Al Shaikh, Mohammad Khalaf Daoud and Fuad Mohammed Hussein Alhamdi
Sustainability 2023, 15(19), 14165; https://doi.org/10.3390/su151914165 - 25 Sep 2023
Cited by 48 | Viewed by 21811
Abstract
Artificial intelligence (AI) is emerging as a disruptive force in many sectors, and using it in accounting isn’t an exception. This conceptual paper explores the role of AI in accounting, for financial reporting, auditing, and financial decision-making and provides accountants an opportunity to [...] Read more.
Artificial intelligence (AI) is emerging as a disruptive force in many sectors, and using it in accounting isn’t an exception. This conceptual paper explores the role of AI in accounting, for financial reporting, auditing, and financial decision-making and provides accountants an opportunity to improve efficiency, accuracy, and decision support. AI, through data analytics, algorithms, automation, etc. has an important role in the field of accounting with some challenges also. The study also highlights the implications of AI in accounting for achieving several Sustainable Development Goals (SDGs). Firstly, AI-driven automation can restructure financial activities, reducing time and resource consumption, and contributing to SDG 8 (Decent Work and Economic Growth). In addition, by providing real-time data analysis, AI empowers businesses to make sustainable decisions based on real-time data, aligning with SDG 9 (Industry, Innovation, and Infrastructure) and SDG-16 (Peace, Justice, and Strong Institutions) and SDG 17 (Partnerships for the Goals). The paper has implications for policy makers, technology developers, financial institutions and business firms. Full article
(This article belongs to the Special Issue Riding the Waves of Artificial Intelligence in Achieving Sustainable Development Goals: Challenges and Opportunities of AI and Big Data Technologies)
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