Bias, Fairness and Privacy in AI Systems Winter 2025/2026 

Module Description

This seminar is an in-depth exploration of the central concerns at the intersection of artificial intelligence, fairness, and privacy. It addresses the emergence of bias in AI systems—from data and model design to deployment contexts—and discusses how such biases can lead to discriminatory consequences and undermine principles of data protection.

The seminar is structured around fundamental thematic areas, including algorithmic transparency, explainability, legal and ethical AI accountability frameworks, and privacy-preserving machine learning. Students are encouraged to delve into real-case scenarios from domains like healthcare, finance, law enforcement, or education and reflect on the social implications of AI systems.

Building on the core knowledge in information systems and digital technologies, this seminar combines academic research methods. Students conduct independent research, write papers, and present their findings. The seminar prepares students for master's theses and research-focused careers in technology governance, digital ethics, and AI regulation.

Objectives

• Critically analyze current topics in fairness, transparency, and accountability of AI systems, with special attention to data protection and privacy.
• Acquire theoretical and practical knowledge about algorithmic bias, its reasons, and measures to counter it on technical, ethical, legal, and socio-economic levels.
• Through case studies of practical applications and policy frameworks (e.g., GDPR, EU AI Act), students acquire the skills to analyze and recommend interventions to assure ethical AI use.

Topics

• Bias in Algorithmic Decision-Making: Causes, Consequences, and Mitigation
• Explainability and Trust: The Role of XAI in Fair AI
• Regulation of Bias in AI

Learning Goals and Competencies

• Ability to understand and perform a systematic literature review 
• Basic understanding of privacy regulations and legal frameworks (e.g., GDPR, EU AI Act) in AI contexts
• Awareness of ethical issues in machine learning and their societal implications
• Demonstrate good writing and presentation skills
• Demonstrate good organisational skills and collaboration in working in groups

Stage 1: Application

Deadline: 22 September 2025 – 06 October 2025

Module Application via QIS: My Functions > Lectures occupy/sign off

Stage 2: Exam Registration and Withdrawal

Deadline: 09 October 2025 – 22 October 2025

Exam Registration and Withdrawal via QIS: My Functions > Administration of exams

An acceptance in the application procedure entitles students to register for the seminar but does not replace an exam registration. Without an exam registration in stage 2, the seminar claim from stage 1 expires.

Note: There is no assignment of available capacities for this seminar. Exam registration is only possible within the deadline and with prior acceptance.

For Exchange Students

Module application and exam registration are not possible via QIS. Exchange students must register or withdraw using a form within the exam registration and withdrawal deadline (not the application deadline). Forms are available on the Faculty’s International Office website.

Assessment

Successful completion requires a term paper with a presentation and regular attendance.

Specific topics will be introduced during the kick-off, and methodologies will be discussed before topic allocation. Students must carefully work through the methodology of their topic.

Exam dates and retake exams will be published on this website at the beginning of the semester.

Please check your student emails (@stud.uni-frankfurt.de) regularly and stay updated.

Literature (How to conduct a literature review):

• PRISMA-S
• PRISMA
• From Bias to Balance—A Systematic Literature Review on Gender Bias in AI (example)
• Document Analysis as a Qualitative Research Method
• Analyzing the Past to Prepare for the Future

Timetable

Presentation

• The presentation should be at most 15 minutes.
• You should present your key findings.
• The structure can follow the seminar paper (e.g., title slide, agenda, introduction, background, methodology, results, discussion, and conclusion)
• Everyone is requested to be present on both days from 09:00, regardless of their presentation timing allocated.
• Please bring your presentation slides with you (on your own device and/or on a USB as a backup).
• Please note that the presentation on the 28th will take place in room RuW 2.202.

Course Evaluation

Exam Information:

• In order to successfully pass this seminar, you need to write a paper (60%) and make a presentation (40%). Each partial requirement needs to be passed with a grade of 4.0 or better. 
• For the paper, the formal requirements of the chair apply (use this Template).