Artificial Intelligence and Society

Code:

M.IA001

Acronym:

IAS

Keywords
Classification	Keyword
OFICIAL	Computer Science
OFICIAL	Informatics Engineering

Instance: 2024/2025 - 1S

Active?	Yes
Responsible unit:	Department of Informatics Engineering
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	61	Syllabus	1	-	6	42	162

Teaching language

Portuguese
Obs.: Suitable for English-speaking students

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 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
Virginia Dignum; Responsible Artificial Intelligence: How to Develop and Use AI in a Responsible Way. ISBN: 978-3-030-30370-9
Christoph Molnar; Interpretable Machine Learning: A Guide For Making Black Box Models Explainable. ISBN: 979-8411463330
Solon Barocas and Moritz Hardt; Fairness and Machine Learning: Limitations and Opportunities. ISBN: 978-0262048613
Stef van Buuren; Flexible Imputation of Missing Data. ISBN: 978-1138588318

Complementary Bibliography

Cathy O'Neil; Weapons of Math Destruction. ISBN: 978-0553418811
Hannah Fry; Hello World: How to be human in the age of the machine. ISBN: 978-0857525246
Meredith Broussard; Artificial Unintelligence: How Computers Misunderstand the World. ISBN: 978-0262038003
Catherine D'Ignazio and Lauren F. Klein; Data Feminism. ISBN: 978-0262044004

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.


• Continuous assessment component, involving individual assignments designed to consolidate theoretical and practical concepts.

The evaluation is structured as follows:

• (TI) Individual assignments completed weekly, along with their respective reports: 30% (6 points). Minimum grade: 2 points.


• (TP) Group project integrating the various topics discussed in the course: 30% (6 points). Minimum grade: 2 points.


• (T) Test: 40% (8 points). Minimum grade: 3 points.

Evaluation Type

Distributed evaluation without final exam

Assessment Components

Designation	Weight (%)
Teste	40,00
Trabalho laboratorial	30,00
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 laboratorial	39,00
Total:	162,00

Eligibility for exams

To obtain course credit, the following requirements must be met:

• Submit at least half (25%) of the individual assignments;


• Collaborate in, submit, present, and defend the group project;


• Attend at least 70% of classes (a maximum of 4 absences is allowed).

Calculation formula of final grade

FinalGrade = (0.3 * TI) + (0.3 * TP) + (0.4 * T)

TI: Weighted average of the Individual Assignments
TP: Weighted average of the Group Project
T: Test Score

The evaluation components are subject to minimum grades, as follows: TI: 2 points; TP: 2 points; T: 3 points. Students who do not meet these requirements will fail due to insufficient component scores. In the case of an exam, it will replace the Test (T) component.

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.

Classification improvement

Only the Test component (T) is subject to improvement.