Special Issue "Artificial Intelligence (AI) and Sustainable Development Goals (SDGs): Exploring the Impact of AI on Politics and Society"

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

Deadline for manuscript submissions: 30 November 2021.

Special Issue Editor

Prof. Dr. Anna Visvizi
E-Mail Website
Guest Editor
1. Effat College of Business, Effat University, Jeddah 22332, Saudi Arabia
2. SGH Warsaw School of Economics, 02-554 Warszawa, Poland
Interests: smart cities; smart villages; international political economy (IPE); information and communication technology (ICT), esp. artificial intelligence (AI) and blockchain; innovation driven growth and development; evidence-driven policy-making; effective government and governance
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Special Issue Information

Dear Colleagues,

In popular discourse, artificial intelligence (AI) has turned into the issue that will challenge politics and society. The debate and, as a matter of fact, confusion about AI, are fueled by the fact that the mechanisms behind AI remain unclear not only to the general audience, but also to the key actors involved in the debate. As a result, the fact that AI is in fact around us and citizens benefit from its advantages on a daily basis is unrecognized. This in turn limits the propensity of several stakeholders to embrace and effectively support AI-based solutions at diverse levels of the policy-making process and the functioning of society. The objective of this Special Issue is to encourage research showcasing the real and the potential impact of AI on politics and society and its role in promoting sustainable growth and development.

The Guest Editor of this Special Issue welcomes submissions that address, but are not limited to, the following issues:

  • AI and politics: issues, policies, tools, techniques and applications, legal and ethical concerns;
  • AI and its application in daily life: issues, developments, challenges, opportunities, legal and ethical concerns;
  • AI and safety and security: issues and developments, legal and ethical concerns;
  • AI and modern warfare: issues and developments, legal and ethical concerns;
  • AI and citizen well-being: issues, techniques, applications;
  • AI and smart cities: issues, techniques, applications;
  • AI and the business sector: issues, techniques, applications;
  • Country and regional case studies, including Belt and Road Initiative, the Single Market, the WTO and DSM, etc.;
  • Conceptual approaches;
  • Comparative insights;
  • Covid-19 inflicted implications.

Prof. Anna Visvizi
Guest Editor

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 papers will be 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 1900 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 (AI)
  • Sustainable Development Goals (SDGs)
  • society
  • politics
  • policy-making
  • well-being
  • ICT
  • business
  • BRI
  • WTO
  • EU

Published Papers (6 papers)

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Research

Article
AI in Context and the Sustainable Development Goals: Factoring in the Unsustainability of the Sociotechnical System
Sustainability 2021, 13(4), 1738; https://0-doi-org.brum.beds.ac.uk/10.3390/su13041738 - 05 Feb 2021
Cited by 2 | Viewed by 1526
Abstract
Artificial intelligence (AI) is associated with both positive and negative impacts on both people and planet, and much attention is currently devoted to analyzing and evaluating these impacts. In 2015, the UN set 17 Sustainable Development Goals (SDGs), consisting of environmental, social, and [...] Read more.
Artificial intelligence (AI) is associated with both positive and negative impacts on both people and planet, and much attention is currently devoted to analyzing and evaluating these impacts. In 2015, the UN set 17 Sustainable Development Goals (SDGs), consisting of environmental, social, and economic goals. This article shows how the SDGs provide a novel and useful framework for analyzing and categorizing the benefits and harms of AI. AI is here considered in context as part of a sociotechnical system consisting of larger structures and economic and political systems, rather than as a simple tool that can be analyzed in isolation. This article distinguishes between direct and indirect effects of AI and divides the SDGs into five groups based on the kinds of impact AI has on them. While AI has great positive potential, it is also intimately linked to nonuniversal access to increasingly large data sets and the computing infrastructure required to make use of them. As a handful of nations and companies control the development and application of AI, this raises important questions regarding the potential negative implications of AI on the SDGs. The conceptual framework here presented helps structure the analysis of which of the SDGs AI might be useful in attaining and which goals are threatened by the increased use of AI. Full article
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Article
Poverty Classification Using Machine Learning: The Case of Jordan
Sustainability 2021, 13(3), 1412; https://0-doi-org.brum.beds.ac.uk/10.3390/su13031412 - 29 Jan 2021
Cited by 1 | Viewed by 981
Abstract
The scope of this paper is focused on the multidimensional poverty problem in Jordan. Household expenditure and income surveys provide data that are used for identifying and measuring the poverty status of Jordanian households. However, carrying out such surveys is hard, time consuming, [...] Read more.
The scope of this paper is focused on the multidimensional poverty problem in Jordan. Household expenditure and income surveys provide data that are used for identifying and measuring the poverty status of Jordanian households. However, carrying out such surveys is hard, time consuming, and expensive. Machine learning could revolutionize this process. The contribution of this work is the proposal of an original machine learning approach to assess and monitor the poverty status of Jordanian households. This approach takes into account all the household expenditure and income surveys that took place since the early beginning of the new millennium. This approach is accurate, inexpensive, and makes poverty identification cheaper and much closer to real-time. Data preprocessing and handling imbalanced data are major parts of this work. Various machine learning classification models are applied. The LightGBM algorithm has achieved the best performance with 81% F1-Score. The final machine learning classification model could transform efforts to track and target poverty across the country. This work demonstrates how powerful and versatile machine learning can be, and hence, it promotes for adoption across many domains in both the private sector and government. Full article
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Article
Cultural Identity Distance Computation through Artificial Intelligence as an Analysis Tool of the Amazon Indigenous People. A Case Study in the Waorani Community
Sustainability 2020, 12(22), 9513; https://0-doi-org.brum.beds.ac.uk/10.3390/su12229513 - 15 Nov 2020
Viewed by 856
Abstract
Cultural identity is a complex concept that includes subjective factors such as ideology, family knowledge, customs, language, and acquired skills, among others. Measuring culture involves a significant level of difficulty, since its study and scope differ from the point of view, the time [...] Read more.
Cultural identity is a complex concept that includes subjective factors such as ideology, family knowledge, customs, language, and acquired skills, among others. Measuring culture involves a significant level of difficulty, since its study and scope differ from the point of view, the time and the place where the studies are carried out. In the Amazon, indigenous communities are in an accelerated process of acculturation that results in a loss of cultural identity that is not easy to quantify. This paper presents a method to measure the cultural distance between individuals or between groups of people using Artificial Intelligence techniques. The distance between individuals is calculated as the distance of the minimum path in the self-organizing map using Dijkstra’s algorithm. The experiments have been carried out to measure the cultural identity of indigenous people in the Waorani Amazon community and compares them with people living in cities who have a modern identity. The results showed that the communities are still distant in terms of identity from the westernised cities around them, although there are already factors where the distances are minimal concerning these cities. In any case, the method makes it possible to quantify the state of acculturation. This quantification can help the authorities to monitor these communities and take political decisions that will enable them to preserve their cultural identity. Full article
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Article
Design Pattern Elicitation Framework for Proof of Integrity in Blockchain Applications
Sustainability 2020, 12(20), 8404; https://0-doi-org.brum.beds.ac.uk/10.3390/su12208404 - 13 Oct 2020
Cited by 1 | Viewed by 595
Abstract
An emerging technology with a secure and a decentralized nature, blockchain has the potential to transform conventional practices in an efficient and dynamic manner. However, migrating to blockchain can be challenging due to the complexity of its infrastructure and processes. The complexity of [...] Read more.
An emerging technology with a secure and a decentralized nature, blockchain has the potential to transform conventional practices in an efficient and dynamic manner. However, migrating to blockchain can be challenging due to the complexity of its infrastructure and processes. The complexity of building applications on blockchain has been highlighted by many studies, thus stressing the need to investigate practical solutions further. A commonly known software engineering concept, software design pattern contributes to the acceleration of software development. It offers a holistic reusable solution for commonly occurring problems in a given context. It helps to identify problems that occur repetitively and describes best practices to address them. The present study is one of the first investigations to inquire into design patterns for blockchain application. Seeking to reduce the complexity in understanding and building applications on blockchain, this paper identifies a design pattern elicitation framework from similar blockchain applications. Next, it provides a demonstration of the Proof of Integrity (PoI) pattern elicited from two different applications on the blockchain. The applicability of the pattern is evaluated by building a blockchain application to verify the integrity of the academic certificates and by explaining how this integrity has been achieved empirically. Full article
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Article
Impact of Artificial Intelligence Research on Politics of the European Union Member States: The Case Study of Portugal
Sustainability 2020, 12(17), 6708; https://0-doi-org.brum.beds.ac.uk/10.3390/su12176708 - 19 Aug 2020
Cited by 5 | Viewed by 1254
Abstract
Currently, artificial intelligence (AI) is at the center of academic and public debate. However, its implications on politics remain little understood. To understand the impact of the AI phenomenon on politics of the European Union (EU), we have carried out qualitative multimethod research [...] Read more.
Currently, artificial intelligence (AI) is at the center of academic and public debate. However, its implications on politics remain little understood. To understand the impact of the AI phenomenon on politics of the European Union (EU), we have carried out qualitative multimethod research by performing a systematic literature review and a case study. The first method was performed according to the preferred reporting items for systematic reviews and meta-analyses (PRISMA), in order to report the state-of-the-art in the existing literature and explore the most relevant research areas. The second method contained contributions from experts in data science and AI of the Portuguese scientific community. The results showed that solutions such as intelligent decision support systems are improving the political decision-making process and impacting the Portuguese society at local, regional, and national levels. We also found that practitioners and scientists are currently shifting their interests from environmental and biological sciences to healthcare services, which is bringing new challenges in terms of protecting patient/citizen data and growing concerns about handling of critical information. Future research may focus on comparative studies with other EU States to obtain a comprehensive and holistic understanding of the AI phenomenon. Full article
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Article
Beware Thy Bias: Scaling Mobile Phone Data to Measure Traffic Intensities
Sustainability 2020, 12(9), 3631; https://0-doi-org.brum.beds.ac.uk/10.3390/su12093631 - 01 May 2020
Viewed by 841
Abstract
Mobile phone data are a novel data source to generate mobility information from Call Detail Records (CDRs). Although mobile phone data can provide us with valuable insights in human mobility, they often show a biased picture of the traveling population. This research, therefore, [...] Read more.
Mobile phone data are a novel data source to generate mobility information from Call Detail Records (CDRs). Although mobile phone data can provide us with valuable insights in human mobility, they often show a biased picture of the traveling population. This research, therefore, focuses on correcting for these biases and suggests a new method to scale mobile phone data to the true traveling population. Moreover, the scaled mobile phone data will be compared to roadside measurements at 100 different locations on Dutch highways. We infer vehicle trips from the mobile phone data and compare the scaled counts with roadside measurements. The results are evaluated for October 2015. The proposed scaling method shows very promising results with near identical vehicle counts from both data sources in terms of monthly, weekly, and hourly vehicle counts. This indicates the scaling method, in combination with mobile phone data, is able to correctly measure traffic intensities on highways, and thereby able to anticipate calibrated human mobility behaviour. Nevertheless, there are still some discrepancies—for one, during weekends—calling for more research. This paper serves researchers in the field of mobile phone data by providing a proven method to scale the sample to the population, a crucial step in creating unbiased mobility information. Full article
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