Artificial Intelligence and Society

Code:

M.IA001

Acronym:

IAS

Keywords
Classification	Keyword
OFICIAL	Computer Science
OFICIAL	Informatics Engineering

Instance: 2025/2026 - 1S

Active?	Yes
Responsible unit:	Department of Computer Science
Course/CS Responsible:	Master in Artificial Intelligence

Cycles of Study/Courses

Acronym	No. of Students	Study Plan	Curricular Years	Credits UCN	Credits ECTS	Contact hours	Total Time
M.IA	59	Syllabus	1	-	6	42	162

Teaching language

English

Objectives

This curricular unit aims to provide students with fundamental and technical knowledge about the impact and challenges of Artificial Intelligence (AI) in social and ethical contexts. To this purpose, various topics currently being discussed in both research and industry will be addressed, focusing on the development and implementation of responsible AI systems across different areas of everyday life.

Learning outcomes and competences

The curricular unit aims to prepare students to identify, mitigate, and address the challenges that arise during the development of AI systems with applications in society. Throughout the course, students are expected to develop the following skills:

• Understand the implications of integrating AI into contemporary society, from social, ethical, and technical perspectives;
  
  


• Critically assess the quality and complexity of data and its impact on AI systems;
  
  


• Quantify and mitigate challenges related to the intrinsic characteristics of data, particularly issues such as missing data and imbalanced data;
  
  


• Identify potential biases in AI applications and implement strategies to correct them, promoting the development of fair and equitable AI systems;
  
  


• Implement and discuss techniques that enhance the explainability of AI systems, fostering transparency and adoption;
  
  


• Identify privacy challenges in AI and propose solutions to ensure privacy protection.

Working method

Presencial

Pre-requirements (prior knowledge) and co-requirements (common knowledge)

Prior knowledge of the fundamentals of Artificial Intelligence and Data Science (e.g., data processing, modeling and evaluating supervised and unsupervised learning models). Intermediate proficiency in programming with Python.

Program

The curricular unit comprises 10 fundamental topics:

1. Introduction to AI and Society: implications, risks, benefits, and ethical and social impact.
   
   


2. Data-Centric AI: The role of data quality in AI model outcomes. Intrinsic characteristics of data. Strategies and tools for data profiling and validation.
   
   


3. Complexity of classification problems: Data complexity measures and meta-learning.
   
   


4. Handling imbalanced data: Quantification, key mitigation strategies, and evaluation.
   
   


5. Handling missing data: Quantification, key mitigation strategies, and evaluation.
   
   


6. Bias and Fairness in AI: Sources of bias in data and AI models. Strategies for identification and mitigation.
   
   


7. Explainability in AI: Strategies to promote the transparency of AI models.
   
   


8. Privacy and Data Security in AI: Privacy challenges and privacy preservation.
   
   


9. Synthetic Data: Fundamental concepts, tools, and applications.
   
   


10. AI and Social Impact: The potential of AI to amplify social inequalities. AI regulation and governance.

Mandatory literature

Alberto Fernández, Salvador García, Mikel Galar, Ronaldo C. Prati, Bartosz Krawczyk, Francisco Herrera; Learning from Imbalanced Datasets. ISBN: 978-3-319-98073-7
Molnar , Christoph; Interpretable machine learning : a guide for making black box models explainable. ISBN: 9798411463330
Barocas, Solon,; Fairness and machine learning : limitations and opportunities /. ISBN: 0262048612
O.Neil , Cathy; Weapons of math destruction : how big data increases inequality and threatens democracy. ISBN: 978-0-553-41883-5
Dignum , Virginia; Responsible artificial intelligence : how to develop and use AI in a responsible way. ISBN: 978-3-030-30371-6

Complementary Bibliography

D.Ignazio, Catherine; Data feminism /. ISBN: 0262044005
Broussard , Meredith; Artificial unintelligence : how computers misunderstand the world. ISBN: 9780262038003
Fry , Hannah; Hello World - How to be human in the age of the machine. ISBN: 9781784163068

Teaching methods and learning activities

The curricular unit is structured into theoretical-practical classes incorporating 4 main components:

• Theoretical component introducing key concepts, potentially including invited lectures when applicable.
  
  


• Technical component exploring AI tools and techniques, experimenting with the analysis of real datasets.
  
  


• Group research and project component, where students are challenged to work in teams to solve applied AI problems that address issues of data quality, privacy, and fairness.

The evaluation is structured as follows:

• (T) Individual assignment integrating the various topics discussed in the course: 35% (7 points). 
  
  


• (P) Group project: 30% (6 points).

• (E) Exam: 35% (7 points). 

Evaluation Type

Distributed evaluation with final exam

Assessment Components

designation	Weight (%)
Exame	35,00
Trabalho escrito	35,00
Trabalho prático ou de projeto	30,00
Total:	100,00

Amount of time allocated to each course unit

designation	Time (hours)
Apresentação/discussão de um trabalho científico	6,00
Elaboração de projeto	39,00
Estudo autónomo	39,00
Frequência das aulas	39,00
Trabalho escrito	39,00
Total:	162,00

Eligibility for exams

N/A

Calculation formula of final grade

FinalGrade =(0.35 * T) + (0.3 * P) + (0.35 * E)

T: Grade of the Individual Assignments
P: Grade of the Group Project
E: Exam Score

The evaluation components are not subjected to minimum grades.

Examinations or Special Assignments

N/A

Internship work/project

N/A

Special assessment (TE, DA, ...)

Students facing special circumstances should discuss their situation directly with the professor responsible for the course in the beginning of the course.

Classification improvement

Only the Exam component (E) is subject to improvement, during the Appeal Season.