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Course: 2022/2023

AI & Sustainable Development

(19218)

Master in Applied Artificial Intelligence (Plan: 475 - Estudio: 378)

EPI

Coordinating teacher: LEDEZMA ESPINO, AGAPITO ISMAEL

Department assigned to the subject: Computer Science and Engineering Department

Type: Electives

ECTS Credits: 3.0 ECTS

Course: 1º

Semester: 2º

Requirements (Subjects that are assumed to be known)

- Ethical and Legal Implications of AI

Objectives

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

Link to document

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

AF1 - Theoretical class AF3 - Theoretical-practical classes AF5 - Individual and group tutorials AF6 - Group work AF7 - Individual student work

Assessment System

The evaluation will consist of several continuous evaluation activities and a final exam. The continuous evaluation will account for 80% of the final grade of the course and will consist of:
- Class participation: 10%.
- Final Project: 70%. 

In addition, the student must take a final exam (20%) and obtain a grade of 4 or higher to pass the course.

% 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.