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Regulation of AI

Code: 108220
Credits: 3
2026/2027
Degree programme Type Course
Bachelor in Artificial Intelligence OB 3

Contact lecturer

Name :
Susana Navas Navarro
Email :
susana.navas@uab.cat

Teaching staff

Bárbara Lirios Monllor Taltavull

Group languages

You can consult this information at the end of the document.

Prerequisites

There are no prerequisits at all.

Objectives

1.Understanding the Legal Framework of AI

2.Resolving Legal Cases

3.Learn to Argue Like a Lawyer

Learning outcomes

  • CM03 (Assess the risks of discrimination on any basis of AI applications.) Assess the risks of discrimination on any basis of AI applications.
  • KM09 (Identify potential social, cultural, economic, or any other biases of AI algorithms.) Identify potential social, cultural, economic, or any other biases of AI algorithms.
  • SM09 (Identify the impact of AI applications on fundamental ethical principles.) Identify the impact of AI applications on fundamental ethical principles.
  • SM10 (Analyse the impact of artificial intelligence applications on privacy and the right to privacy of individuals.) Analyse the impact of artificial intelligence applications on privacy and the right to privacy of individuals.
  • SM11 (Analyse the transparency and explanation mechanisms of artificial intelligence algorithms.) Analyse the transparency and explanation mechanisms of artificial intelligence algorithms.

Contents

TEMA I. INTRODUCTION TO THE LEGAL SYSTEM


1. Differences between law, ethics, morality and other rules.

2. International law, European law, national law. The legal sources

3. Human rights, fundamentals rights and digital rights


TEMA II. PERSONAL DATA PROTECTION. THE GDPR


  1. Fundamentals.The impact of the General Data Protection Regulation (GDPR) on AI
  2. How to use AI and personal data appropriately and lawfully: automated decisions and generation of profiles
  3. The rights of the data subject
  4. Protection of personal data


TEMA III. DISCRIMINATION AND HATE SPEECH ON ONLINE PLATFORMS


  1. AI and discriminatory bias: legal tools and legal obligations
  2. Moderation of contents and Codes of conduct
  3. Gender equality, parity, gender perspective, genderism.
  4. AI as a technology of gender: legal instruments and legal consequences



TEMA IV. THE AI ACT


  1. Risk-based AI Regulation. Scope of application
  2. Legal definitions
  3. Prohibited practices
  4. AI-systems. Legal requirements
  5. Obligations of providers and deployers of high-risk AI systems and other parties
  6. General purpose AI models. Legal requirements and obligations of providers and deployers.
  7. Cybersecurity
  8. Code of practices



TEMA V. CIVIL LIABILITY


  1. Contract Law. Consumer Law
  2. Liability. Contractual and non-contractual liability for the damages caused by AI-system and models.
  3. The European Regulation on the Producer's Liability for AI-systems and models: damages, causal link, defect, liable subjects.



TEMA VI. INTELLECTUAL PROPERTY


  1. Basics on Copyright Law
  2. The Software Regulation by Copyright Law
  3. Sui generis right on databases of the manufacturer
  4. The Regulation of Trade Secrets in Europe



Learning activities and methodology

Title Hours ECTS Learning outcomes
Topics learning 15 0.6 CM03, KM09, SM09, SM10, SM11
case study II 14 0.56 CM03, KM09, SM09, SM10, SM11
Lectures 10 0.4 CM03, KM09, SM09, SM10, SM11
case study I 14 0.56 CM03, KM09, SM09, SM10, SM11
final exam 2 0.08 CM03, KM09, SM09, SM10, SM11
midterm exam 2 0.08 CM03, KM09, SM09, SM11

The first 7 weeks will work on the first three topics. Then there will be an exam on this part in the 8th week. The following 7 weeks will work on topics 4-5-6. In the 16th week, the exam on the second part of the syllabus will be taken.


The methodology is twofold:


  1. Theoretical class where the key legal concepts are framed
  2. Case studies to see the practical application of the acquired concepts
  3. Discussions on current issues


In this subject, the use of AI tools is allowed to structure the topics, referencing bibliography and to write case studies.


Legal sanctions will apply in case of plagiarism, academic fraud or copying.

Annotation: within the schedule set by the centre or degree programme, 15 minutes of one class will be reserved for students to evaluate their lecturers and their courses or modules through questionnaires.

Assessment

Continuous assessment activities

Title Weight Hours ECTS Learning outcomes
case study II 15% 7 0.28 CM03, KM09, SM09, SM10, SM11
case study I 15% 7 0.28 CM03, KM09, SM09, SM10, SM11
midterm exam 40% 4 0.16 CM03, SM09, SM10, SM11

The assessment consists of:


a) continuous assessment:


  • midterm exam: 20% of the final grade for each part (40%). Individual activity
  • case study: 3 cases for each part. Each case is 5% of the final grade for each part (30%). Collective activity


b) final exam: 30% of the final grade (15% for each part). A minimum grade of 1.5 must be obtained to be averaged with the continuous assessment grade.


Both the midterm and final exams can be retaken.


This subject does not allow single assessment.

Bibliography

  • AEPD (2017): Protección de datos. Guía para el Ciudadano https://www.aepd.es/es/documento/guia-ciudadano.pdf
  • AEPD, APDCAT, AVPD (2018): Guía del Reglamento General de Protección de Datos para responsables de tratamiento (Document en línia) https://www.aepd.es/es/documento/guia-rgpd-para-responsables-de-tratamiento.pdf-0
  • Barrio, Moisés (2021): Manual de Derecho digital, Tirant Lo Blanch, Valencia, 2021.
  • Council of Europe (2023): Human rights by design future-proofing human rights protection in the era of AI, disponible en: chrome-extension://efaidnbmnnnibpcajpcglclefindmkaj/https://rm.coe.int/follow-up-recommendation-on-the-2019-report-human-Custers, Bart and Fosch-Villaronga, Eduard (eds.) (2022): Law and Artificial Intelligence. Regulating AI and Applying AI in Legal Practice, The Hague, Springer.
  • Ebers, Martin; Navas, Susana (eds.) (2020): Algorithms and Law, Cambridge University Press.
  • Fournier-Tombs, Eleonore y Castets-Renard, Celine (2021): Algorithms and the Propagation of Gendered
  • Cultural Norms, disponible en: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3980113
  • FRA (European Union Agency for Fundamental Rights) (2018): #BigData: Discrimination in data-supported decision making. Disponible en: https://fra.europa.eu/en/publication/2018/bigdata-discrimination-data-supported-decision-making
  • FRA (2020): Gettingthe future right - Artificial intelligence and fundamental rights Disponible en: https://fra.europa.eu/en/publication/2020/artificial-intelligence-and-fundamental-rights
  • Llorente Sánchez-Arjona, M. (25/03/2021) "Big Data, Inteligencia Artificial y Violencia de Género". Diario La Ley. Ciberderecho
  • Susana Navas Navarro, "Inteligencia artificial y responsabilidad civil",en Manuel Fondevila, Ana Cediel, La Sociedad dividida: polarización, populismo e inteligencia artificial, Colex, Madrid, 2026, pp. 69-85. Online: https://www.colexopenaccess.com/libros/la-sociedad-dividida-polarizacion-populismo-e-inteligencia-artificial-8461
  • Presno Linera, Miguel Angel (2022): "Derechos fundamentales e inteligencia artificial en el estado social, democrático y digital de derecho", El Cronista del Estado social y democrático de Derecho, núm. 100, 2022, disponible en: https://www.academia.edu/89821366/Derechos_fundamentales_e_inteligencia_artificial_en_el_Estado_social_democrático_y_.
  • https://www.gov.uk/government/publications/ai-regulation-a-pro-innovation-approach/white-paper.
  • https://www.whitehouse.gov/ostp/ai-bill-of-rights/
  • https://commission.europa.eu/system/files/2020-02/commission-white-paper-artificial-intelligence-feb2020_en.pdf


Software

No need

Course groups and languages

The information provided is provisional until November 30. After this date, you will be able to consult the language of each group through this link. To access the information, you will need to enter the course CODE

Type of teaching Group Language Semester Shift
(TE) Theory 1 English first semester afternoon
(PAUL) Classroom practices 1 English first semester afternoon
(PLAB) Practical laboratories 1 English first semester afternoon