Responsible Artificial Intelligence

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Credits
3
Types
Elective
Requirements
This subject has not requirements, but it has got previous capacities
Department
CS
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The Responsible AI (RAI) seminar is designed to empower master students with the knowledge and skills necessary to investigate, identify, and promote responsible Artificial Intelligence techniques for industrial applications, while ensuring these technologies are developed and deployed in a manner that is aligned with European values, fundamental rights, and the latest regulatory frameworks.
This course places a strong emphasis on the EU's approach to Artificial Intelligence, which is founded on the principles of excellence, trust, and respect for human rights. Participants will explore the ethical, legal, economic, gender and societal implications of AI-based systems, guided by the core values enshrined in the EU ELSEC framework (Ethical, Legal, Societal, Economic, and Cultural), the European Charter of Fundamental Rights, and the requirements of the AI Act.
Key topics addressed include:
The ethical principles and standards that should guide AI research and development include transparency, fairness, non-discrimination, data protection, and human oversight.
The risk-based approach of the AI Act, which categorises AI systems according to their potential impact on safety and fundamental rights, imposes strict obligations for high-risk applications, including risk assessment, mitigation, and impact assessments on fundamental rights.
The importance of designing human-centric and trustworthy AI lies in ensuring that AI systems are explainable, robust, and respect the dignity and autonomy of all individuals.
The integration of societal values into AI-based systems and the challenges of embedding ethical reasoning and accountability into automated decision-making.
The responsibilities of AI developers, deployers, and other stakeholders are to ensure that AI-based technologies benefit society while minimising risks and preventing harm.
The broader societal and regulatory context includes the promotion of cross-disciplinary approaches that combine responsible AI-based techniques with other scientific and industrial fields to address complex challenges in sectors such as healthcare, education, and environmental sustainability.
By the end of the seminar, participants will have a comprehensive understanding of how to develop and apply AI in ways that are not only innovative and effective but also compliant with EU regulations, respectful of human rights, and supportive of democratic values. This course aims to cultivate a new generation of AI professionals who are equipped to lead in a rapidly evolving technological landscape while upholding the highest standards of responsibility and integrity.

Teachers

Person in charge

  • Ulises Cortés García ( )

Weekly hours

Theory
2
Problems
0
Laboratory
0
Guided learning
0
Autonomous learning
3.77

Objectives

  1. To identify the AI technologies, tools, architectures, and algorithms best suited for industrial applications, ensuring that these solutions rigorously uphold the principles of Responsible Artificial Intelligence, including ethics, transparency, fairness, accountability, and fully comply with legal requirements, societal norms, and gender equality standards.
    Related competences: CG1, CEP3, CT1, CT2, CT4, CT6,
  2. To be able to develop a set of criteria for AI-based applications development, and evaluate each of the identified applications in terms of these criteria
    Related competences: CG1, CEP4, CEP5, CEP7,
  3. To make short- and long-term ethical recommendations for the industrial AI applications development and work in a multidisciplinary team
    Related competences: CG1, CEP7, CEP8, CT3, CT6,

Contents

  1. Responsible AI
    This part of the seminar introduces the foundational principles of Responsible AI, emphasising ethical considerations and the responsible use of AI technologies across industries. It explores key topics such as fairness, transparency, accountability, and compliance with legal and societal standards, equipping participants to design and deploy AI-based systems that respect human rights and promote trust.
  2. Face to face with the Artificial Intelligence Industrial Applications
    AI-based technologies are transforming the business landscape, with their applications spanning management, administration, science, engineering, manufacturing, finance, law, defence, space exploration, medicine, and diagnostics. Senior managers increasingly rely on AI-driven strategic planning tools for competitive analysis, technology deployment, and resource allocation. AI also supports equipment configuration, product distribution, regulatory compliance, and personnel assessment, significantly enhancing organisational planning and operational control. As AI technologies continue to evolve, their influence in science and engineering continues to grow even stronger.
    In this segment of the seminar, students will critically examine controversial or ethically questionable AI applications currently used in various industries, fostering a deeper understanding of both the potential and the challenges of AI in business contexts.
  3. Introduction to Human Rights
    This part of the seminar introduces the relationship between human rights and artificial intelligence, highlighting how ethical AI development must prioritise dignity, fairness, privacy, and accountability to ensure technology benefits all and safeguards fundamental freedoms

Activities

Activity Evaluation act


Examples of Industrial Applications of Artificial Intelligence from R&D

The Artificial Intelligence applications developed for science and engineering are used to organize and manipulate the ever-increasing amounts of information available to scientists and engineers. The Artificial Intelligence is used in complex processes and it is the increased use of robotics in business. In this part of the seminar we will study edge applications of Artificial Intelligence born as R&D results. Most of the examples will come from European Union funded research.
  • Theory: Each one of the study cases will have a material that will provided in advance. The student has to read it to be able to discuss its contents. Also, the student has to prepare a paper of each one of them. The students will be asked to choice one among the study cases and prepare a more detailed paper.
  • Autonomous learning: To read the provided materials and prepare the discussion
Objectives: 1 3 2
Contents:
Theory
18h
Problems
0h
Laboratory
0h
Guided learning
0h
Autonomous learning
25h

Introduction

The student will learn the objectives of this seminar. He will receive the materials and learn the calendar of activities.
  • Theory: Introduction

Contents:
Theory
6h
Problems
0h
Laboratory
0h
Guided learning
0h
Autonomous learning
28h

Teaching methodology

There it will be invited speakers from the industry --at least 4-- and up to 7 case studies of Artificial Intelligence Industrial applications.
The format will be a seminar with direct participation and reporting tasks.

Evaluation methodology

Participants will submit a couple of essays during the course. There will be a single grade for the essays and for the final exam. The final exam will count for 60% of the grade.

Bibliography

Basic:

Complementary:

Previous capacities

We require the student to be knowledgeable on:

* Processes for developing large and complex software systems
* Roles and technologies to help you control such projects
* Research-level issues in areas such as software engineering, information systems, simulation modelling, digital media and games, network computing and artificial intelligence.