Checking date: 26/05/2023

Course: 2023/2024

AI & Sustainable Development
Master in Applied Artificial Intelligence (Plan: 475 - Estudio: 378)


Department assigned to the subject: Computer Science and Engineering Department

Type: Electives
ECTS Credits: 3.0 ECTS


Requirements (Subjects that are assumed to be known)
- Ethical and Legal Implications of AI
The objective of this course is to provide the student with the necessary knowledge about the role of Artificial Intelligence in Sustainable Development. The student should acquire knowledge about the implications of the application of AI in different aspects of development, using the United Nations Sustainable Development Goals as a common thread. The student should understand the main challenges facing the application of AI in developing countries and will know some of the main applications developed, the techniques applied, and the results obtained.
Skills and learning outcomes
Description of contents: programme
1. Introduction - Concept of sustainable development. - The Sustainable Development Goals (SDGs) of the United Nations. - Ethical aspects of Artificial Intelligence. - AI as a strategy for Sustainable Development. 2. AI for sustainable development - Introduction - AI as a driving tool for the 2030 Agenda. - Advantages and disadvantages. - Case studies. 3. Challenges of AI in developing countries. - Introduction. - Technological challenges. - Social challenges. - Economic challenges. - Environmental challenges. 4. AI applications for development in the world - Introduction. - AI Applications in Water and Energy Access in Sub-Saharan Africa. - AI Applications in Medicine in Latin America. - AI Applications in Agriculture in Southeast Asia. 5. Inclusive AI and weapon against catastrophes. - Introduction - Indigenous Communities and AI. - The role of AI in emergencies
Learning activities and methodology
Training Activities: -------------------------- AF1 - Theoretical class AF3 - Theoretical-practical classes AF5 - Individual and group tutorials AF6 - Group work AF7 - Individual student work Teaching methodology: ------------------------ MD1: Class lectures by the lecturer with the support of computer and audiovisual media, in which the main concepts of the subject are developed and the bibliography is provided to complement the students' learning. MD2: Critical reading of texts recommended by the subject teacher: press articles, reports, manuals and/or academic articles, either for subsequent discussion in class or to expand and consolidate knowledge of the subject. MD3: Resolution of practical cases, problems, etc... posed by the teacher individually or in groups. MD4: Presentation and discussion in class, under the moderation of the teacher, of topics related to the content of the subject, as well as practical cases. MD5: Preparation of individual or group work and reports.
Assessment System
  • % end-of-term-examination 20
  • % of continuous assessment (assigments, laboratory, practicals...) 80
Calendar of Continuous assessment
Basic Bibliography
  • Hassanien, Aboul Ella & Bhatnagar, Roheet & Darwish, Ashraf. Artificial Intelligence for Sustainable Development: Theory, Practice and Future Applications. Springer. 2021
  • Hui Lin Ong, Ruey-an Doong, Raouf Naguib, Chee Peng Lim, Atulya K. Nagar . Artificial Intelligence and Environmental Sustainability. Challenges and Solutions in the Era of Industry 4.0. Springer. 2022
  • Kamal Kant Hiran, Deepak Khazanchi, Ajay Kumar Vyas and Sanjeevikumar Padmanaban. Machine Learning for Sustainable Development. De Gruyter. 2021
  • Peter Dauvergne. AI in the Wild: Sustainability in the Age of Artificial Intelligence. MIT Press. 2020
  • William W. Hsieh. Machine Learning Methods in the Environmental Sciences. Neural Networks and Kernels. Cambridge Core. 2010
  • Zakaria Boulouard, Mariya Ouaissa, Mariyam Ouaissa, Sarah El Himer. AI and IoT for Sustainable Development in Emerging Countries. Challenges and Opportunities. Springer. 2022

The course syllabus may change due academic events or other reasons.