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AI for Social Good-Explainability and Sustainability of Decision-Making Practices

A special issue of Sustainability (ISSN 2071-1050).

Deadline for manuscript submissions: closed (31 May 2025) | Viewed by 1839

Special Issue Editors


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Guest Editor
Department of Information Technology, Peter Faber Business School, Australian Catholic University, Sydney, NSW 2060, Australia
Interests: explainable Artificial Intelligence; computational intelligence; intelligent modelling; expert system design; evolutionary optimisation
Special Issues, Collections and Topics in MDPI journals
Data Science Institute, University of Technology Sydney, Sydney, NSW 2007, Australia
Interests: AI for social good; AI fairness; AI explainability; smart agriculture; visual analytics; behavior analytics; human-computer interaction
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
School of Professional Practice and Leadership, University of Technology Sydney, Ultimo, NSW 2007, Australia
Interests: digital transformation; digital processing visualization; E-commerce; business intelligence; m-Health
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

AI and technology are changing the world we live in. The United Nations (UN) has published an initiative that laid out 17 Sustainable Development Goals (SDGs) covering far-reaching challenges related to poverty, inequality, climate change, environmental degradation, peace, and justice. AI can be used as a tool to address the challenges related to SDGs. For example, AI can be applied to provide unbiased insights into the candidate pool for job applications. This would address the inequality challenge. Additionally, it is important to apply the AI algorithms ethically and responsibly for social good. Academia has the responsibility to pave the way towards discovering decision-making solutions that are fair, transparent, and explainable and making sure that AI solutions are sustainable. Explainable AI is one of the ways to bring transparency to the decision-making process to ensure the solutions provided through technological systems are sustainable.

This Special Issue focuses on providing solutions and algorithms to increase the explainability and transparency of the AI systems to ensure sustainability. Both theoretical and actual applications of AI algorithms and XAI that highlight the alignment with sustainability and applicability for social good are welcome. This includes local and global XAI methods, their application for problem solving, and solutions to enhance the performance of the decision-making algorithms. The solutions addressing problems utilizing different forms of data (images, numerical, etc.) that incorporate XAI algorithms and demonstrate the contribution to sustainability and social good, are welcome.

We are mainly interested in manuscripts that apply AI algorithms and decision-making processes to address challenges related to sustainable, socio-economic, scientific, and integrated approaches for sustainable development, including applying AI to SDG challenges and sustainability issues.

Dr. Niusha Shafiabady
Dr. Jianlong Zhou
Dr. Robert M.X. Wu
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

  • explainable AI
  • AI for social good
  • XAI applications
  • XAI algorithms
  • AI transparency
  • sustainability

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Published Papers (1 paper)

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Research

21 pages, 1261 KiB  
Article
Exploring the Determinants of the Sustainable Use of Artificial Intelligence in Peruvian University Teachers: A Structural Equation Modeling Analysis
by Benicio Gonzalo Acosta-Enriquez, Moises David Reyes-Perez, Olger Huamani Jordan, Leticia Carreño Saucedo, Jesús Emilio Agustín Padilla-Caballero, Antony Esmit Franco Fernández-Altamirano, Abraham José García Yovera, Roxita Nohely Briceño-Hernandez and Johannes Michael Alarcón Bustíos
Sustainability 2025, 17(7), 2834; https://doi.org/10.3390/su17072834 - 22 Mar 2025
Viewed by 1253
Abstract
This study examines the determinants of the sustainable use of artificial intelligence (AI) among university professors in Peru. This research adopted a quantitative approach through a cross-sectional empirical–explanatory study, employing a structural equation model. Data were collected from 368 professors from eight Peruvian [...] Read more.
This study examines the determinants of the sustainable use of artificial intelligence (AI) among university professors in Peru. This research adopted a quantitative approach through a cross-sectional empirical–explanatory study, employing a structural equation model. Data were collected from 368 professors from eight Peruvian universities using a structured questionnaire that assessed six main constructs: attitude toward AI, prejudice against AI, facilitating conditions, use of AI, teaching concerns, and ethical perception. While the results reveal significant correlational relationships—with attitude toward AI, facilitating conditions, and prejudice against AI showing a significant association with its sustainable use, and the use of AI showing a significant relationship with professors’ ethical perceptions—the cross-sectional nature of this study precludes causal inferences. No significant relationship was found between the use of AI and teaching concerns. Additionally, demographic variables such as gender and age did not exhibit significant moderating effects. These findings contribute to understanding the factors related to the sustainable adoption of AI in higher education and provide valuable insights for the development of effective institutional strategies in the Latin American context. Full article
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