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LAW42310

Academic Year 2024/2025

AI Regulation (LAW42310)

Subject:
Law
College:
Social Sciences & Law
School:
Law
Level:
4 (Masters)
Credits:
10
Module Coordinator:
Dr Bernd Justin Jütte
Trimester:
Spring
Mode of Delivery:
On Campus
Internship Module:
No
How will I be graded?
Letter grades

Curricular information is subject to change.

The adoption of Articifical Intelligence (AI) technology in different sectors has led to increased efforts by national governments and international bodies to design specific regulatory frameworks that address the unique challenges created by AI technology. This module explores the regulation of Artificial Intelligence by analyzing existing AI regulatory frameworks (US, EU, regional). It introduces students to emerging regulatory approaches, for example the risk-based approach adopted by the European Union with the 2024 AI Act, as well as other national and international initiatives. It examines the impact of AI on industries and public policies, highlighting both its potential benefits and risks, such as misinformation, privacy violations, and increased bias.
After a general introduction to the emerging regulatory frameworks, the application of AI in specific areas and sectors will be discussed. These might include:
- Data Protection & Privacy
- Media & Entertainment Industry
- Healthcare
- Legal & Administrative decision-making
- Migration & Border Control
- Competition Law

About this Module

Learning Outcomes:

At the end of the module students should be able to:
- identify and discuss the specific risks posed by the use of artificial intelligence;
- understand the different regulatory approaches in selected jurisdictions;
- critically asses the application of AI is specific and sensitive sectors.

Indicative Module Content:

The module is separated into two parts. The first part will introduce the systematic approach to AI regulation in the EU under the AI Act, complemented with comparative approaches from selected foreign jurisdictions. The second part will examine several case studies not he use and application of AI in different contexts. These might include the health sector, judicial decision making, immigration and comparable topics.

Student Effort Hours:
Student Effort Type Hours
Autonomous Student Learning

200
Total

200

Approaches to Teaching and Learning:
The first part of the module will be taught in guided seminars, during which the lecturer will introduce the topic and discuss critical aspects with the seminar group. The second part will require students to actively prepare the sessions with short presentations in small groups. The following guided discussions will require advance preparation and might include smaller group activities.

Requirements, Exclusions and Recommendations

Not applicable to this module.


Module Requisites and Incompatibles
Not applicable to this module.
 

Assessment Strategy
Description Timing Component Scale Must Pass Component % of Final Grade In Module Component Repeat Offered
Individual Project: Individual in-class presentation on assigned topic. Week 1 Graded No
30
 No
Participation in Learning Activities: Students will be assessed based on frequency and quality of interventions during weekly classes/seminars. Week 1 Graded No
20
 No
Assignment(Including Essay): Reflective essay on selected topic. Week 12 Graded No
50
 No

Carry forward of passed components
No
 

Resit In Terminal Exam
Autumn No
Please see Student Jargon Buster for more information about remediation types and timing. 

Feedback Strategy/Strategies

• Feedback individually to students, post-assessment
• Group/class feedback, post-assessment

How will my Feedback be Delivered?

Not yet recorded.

Timetabling information is displayed only for guidance purposes, relates to the current Academic Year only and is subject to change.
Spring Lecture Offering 1 Week(s) - 20, 21, 22, 23, 24, 25, 29, 30, 31, 32 Thurs 14:00 - 15:50
Spring Lecture Offering 1 Week(s) - 20, 21 Tues 16:00 - 17:50
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