I. SUBJECT DESCRIPTION
II. SUBJECT REQUIREMENTS
III. COURSE CURRICULUM
SUBJECT DATA
OBJECTIVES AND LEARNING OUTCOMES
TESTING AND ASSESSMENT OF LEARNING PERFORMANCE
THEMATIC UNITS AND FURTHER DETAILS
Subject name
Artificial Intelligence and Law
ID (subject code)
BMEGT55VVV1000-00
Type of subject
Contact lessons
Course types and lessons
Type
Lessons
Lecture
2
Practice
0
Laboratory
0
Type of assessment
mid-term grade
Number of credits
3
Subject Coordinator
Name
Dr. Grad-Gyenge Anikó
Position
associate professor
Contact details
grad-gyenge.aniko@gtk.bme.hu
Educational organisational unit for the subject
Department of Business Law'
Subject website
Language of the subject
magyar - HU, angol - EN
Curricular role of the subject, recommended number of terms
Direct prerequisites
Strong
None
Weak
None
Parallel
None
Exclusion
None
Validity of the Subject Description
Approved by the Faculty Board of Faculty of Economic and Social Sciences, Decree No: 580389/21/2026 registration number. Valid from: 27.05.2026.

Objectives

The development of artificial intelligence (AI) must provide human-centric and ethical operation, transparency, and respect for fundamental rights. This course concerns the application of the law and ethics to AI. It addresses such topics as AI and human rights, privacy protection (GDPR) and cybersecurity, responsibility and liability, non-discrimination, intellectual property, and safety rules. Besides its apparent advantages, AI entails a number of potential risks, such as opaque decision-making or being used for criminal purposes. The human factor, the process of machine learning in case of algorithms and automated decision-making, handling uncertainties may lead to discriminative practices. AI technologies may present new safety risks for users when they are embedded in products and services. Moreover, the classical legal liability systems need to be rethought, in particular, because the absence of precise and clear statutory provisions may undermine legal certainty. Data protection has been the area of the law that has most engaged with AI. The objective of the course is to introduce the students to the legal environment of AI, including especially the basic principles and guidelines and the present and possible future framework of these laws (e.g., the Artificial Intelligence Act of the EU). The course helps recognise and mitigate the risks, know the accountability and governance implications of AI, and what we need to do to ensure lawfulness, fairness, and transparency in AI systems. The course is part of the Human Centred Artificial Intelligence Masters (HCAIM) and PANORAIMA programme.

Academic results

Knowledge
  1. The students are aware of
  2. - the social and economic functions of legislation
  3. - the basic functions of the main areas of law affecting technology, responsibility, safety
  4. - the main features of the legal, economic and business mechanisms that can influence technology, responsibility, safety
  5. - relevant approaches to illustrate the impact of regulators on certain questions of artificial intelligence methods
  6. - aspects of analysis of legislation affecting artificial intelligence.
Skills
  1. The students are able to
  2. - properly interpret and place rules in practice
  3. - analyse the role, motivations and activities of individual economic actors from a legal and economic point of view
  4. - grasp a multi-faceted context system for modeling public policy strategy planning in relation to the topic
  5. - to critically analyse the benefits and risks of artificial intelligence from a legal perspective.
Attitude
  1. The students
  2. - are well aware in the assessment of the legal regulation of the artificial intelligence, is informed by various sources, consciously seeking alternative solutions
  3. - are open to self-reflection, critical reception, and critical thinking when thinking about regulation of artificial intelligence
  4. - are open to critical self-assessment, based on activities, active, learning methods, experimental style
  5. - adopt as a starting point for regulation the implementation of legal standards and requirements.
Independence and responsibility
  1. The students
  2. - are open to accept reliable critical remarks,
  3. - are able to solve practical professional problems independently.

Teaching methodology

Lectures and written communication, use of ICT tools and techniques.

Materials supporting learning

  • Előadásanyagok

General Rules

A 2.2. pontban megfogalmazott tanulási eredmények értékelése két zárthelyi írásbeli teljesítménymérés alapján történik. A teljesítésnek további feltétele, hogy a hallgató részt vegyen az előadások 70%-án. A tantárgy teljesítéséhez szükséges a legalább 50% teljesítés.

Performance assessment methods

Szorgalmi időszakban végzett teljesítményértékelések részletes leírása: A képesség típusú kompetenciaelemeinek komplex, írásos értékelési módja írásbeli dolgozat formájában. A dolgozat állhat tesztkérdésekből, melyek az egyes fogalmak értelmezését és az azok közötti összefüggések felismerését; esszékérdésekből, melyek a lexikális tudást, valamint a szintetizáló képességet vizsgálják. A rendelkezésre álló munkaidő 30-90 perc.

Percentage of performance assessments, conducted during the study period, within the rating

  • részteljesítmény értékelés (házi feladatok): 40
  • Zárthelyi dolgozat I.: 30
  • Zárthelyi dolgozat II.: 30

Percentage of exam elements within the rating

Conditions for obtaining a signature, validity of the signature

A teljesítésnek további feltétele, hogy a hallgató részt vegyen az előadások 70%-án. A tantárgy teljesítéséhez szükséges a zárthelyi dolgozatok legalább 50%-os teljesítése.

Issuing grades

%
Excellent 91-100
Very good 85-90
Good 76-84
Satisfactory 63-75
Pass 50-62
Fail 0-49

Retake and late completion

A zárthelyi dolgozatok javítására és pótlására a TVSZ szerint van lehetőség. A házifeladat nem javítható vagy pótolható.

Coursework required for the completion of the subject

Nature of work Number of sessions per term
részvétel a kontakt tanórákon 28
házi feladat elkészítése 10
felkészülés a teljesítményértékelésre 52
összesen 90

Approval and validity of subject requirements

Consulted with the Faculty Student Representative Committee, approved by the Vice Dean for Education, valid from: 04.05.2026.

Topics covered during the term

A 2.2. pontban megfogalmazott tanulási eredmények eléréséhez a tantárgy a következő tematikai blokkokból áll. Az egyes félévekben meghirdetett kurzusok sillabuszaiban e témaelemeket ütemezzük a naptári és egyéb adottságok szerint.

Lecture topics

Additional lecturers

Name Position Contact details
Dr. Grad-Gyenge Anikó egyetemi docens grad-gyenge.aniko@gtk.bme.hu
Dr. Nagy Krisztina egyetemi adjunktus nagy.krisztina@gtk.bme.hu
Dr. Tomasovszky Edit egyetemi adjunktus tomasovszky.edit@gtk.bme.hu

Approval and validity of subject requirements