AI in Society: Safety, Regulation, Fairness

Course manual 2025/2026

Course content

AI is rapidly becoming part of our daily lives in many visible and invisible ways.  Examples include ethical problems of AI systems, including their privacy and governance, questions of fairness and accountability, as well as data bias and model interpretability. This course provides an opportunity to engage with these research questions. It does so by offering a pathway into the societal, regulatory and ethical themes of AI . The course further provides an opportunity to experiment with what is discussed during lectures in group projects. It is expected that the students will develop a contextual and critical understanding of AI processes in action.

Topics per week:

• Week 1: Policy and Regulation 
• Week 2: Safety, Bias and Fairness in AI
• Week 3: AI for Society
• Week 4: AI, Society, and the Future

A full reading list will be provided. The interested students can get a sense of the course topics from these sources:

• Hacker, P. (2025). "AI Regulation in Europe: From the AI Act to Future Regulatory Challenges." Oxford Handbook of Algorithmic Governance and the Law.
• Wachter, S., Mittelstadt, B., & Russell, C. (2021). "Why fairness cannot be automated: Bridging the gap between EU non-discrimination law and AI." Computer Law & Security Review.
• Blanke, T., Venturini, T. & De Pryck, K. (2024). "A peek inside two black boxes—an experiment with explainable artificial intelligence and IPCC leadership." International Journal of Digital Humanities.
• Narayanan, A. & Kapoor, S. (2025). "AI as Normal Technology." Knight First Amendment Institute.
• Bender, E.M., Gebru, T., McMillan-Major, A., & Shmitchell, S. (2021). "On the Dangers of Stochastic Parrots: Can Language Models Be Too Big?" FAccT '21.

Objectives

• The student is able to describe the main contributions and applications of social and cultural research to dominant research problems in AI, including questions of fairness and transparency in AI as well as the impact of AI on societal developments.
• The student is able to explain the main opportunities and challenges of AI in society.
• The student is able to refer to practical as well as conceptual issues to the broader debate within the relevant state of the art.
• The student is able to do a critical societal and ethical analysis of their processes.
• The student is able to demonstrate the effects of including those societal and ethical prerequisites on a practical project in an area of choice.

Teaching methods

• Lecture
• Laptop seminar
• Self-study
• Working independently on e.g. a project or thesis
• Computer lab session/practical training

Lectures provide the foundations for each weekly theme. Through theoretical frameworks, case studies, and examples, lectures introduce students to key aspects in AI governance, fairness, societal impact, and futures thinking. Interactive elements encourage students to connect abstract concepts to their technical knowledge.

Lab sessions offer hands-on engagement through group-based work and formative assessments. Students work collaboratively on case studies and assignments, with TA support available for help and feedback. Labs in weeks 2 and 4 include formative assessments that allow students to test their understanding before summative deadlines.

Self-study involves engaging with the reading list and lecture materials. Students are expected to prepare for labs by completing assigned readings, enabling deeper discussion and more effective group work during contact hours.

Working independently applies to the individual essay assignment, where students develop and defend a position on AI's societal implications. This requires synthesizing course themes, conducting additional research, and constructing a coherent written argument demonstrating critical engagement with the material.

Learning activities

Attendance

Programme's requirements concerning attendance (TER-B Article B-4.10):

• For some course component attendance is obligatory. If attendance is required, this is stated in the course catalogue. The reasons for, and the implementation of, this attendance requirement may vary by course and are included in the course manual. Students who do not meet this attendance requirement cannot complete the course with a passing grade.
  

Assessment

• 2 group assignments (consisting of Python notebooks + a written report)
• 1 (~2.000 word) essay

Inspection of assessed work

Rubrics are shared on Canvas. Following grade publication, students can contact the course coordinator to arrange inspection of their assessed work and discuss the feedback provided.

Assignments

Assignment 1 (30%): Group assignment. Students apply explainability techniques (LIME) to a text classifier and assess algorithmic bias across demographic groups. Combines technical implementation with critical reflection. Graded with written feedback via Canvas.

Assignment 2 (30%): Group assignment. Students collect and analyze social media data using NLP techniques to map public discourse and imaginaries surrounding AI. Graded with written feedback via Canvas.

Essay (40%): Individual assignment (2000 words). Students write a policy memo grounded in one of the competing visions of AI futures, engaging with counterarguments and using UvA AI Chat as a critical interlocutor. Graded with written feedback via Canvas.

Fraud and plagiarism

The 'Regulations governing fraud and plagiarism for UvA students' applies to this course. This will be monitored carefully. Upon suspicion of fraud or plagiarism the Examinations Board of the programme will be informed. For the 'Regulations governing fraud and plagiarism for UvA students' see: www.student.uva.nl

Course structure

Contact information

Coordinator

• David Graus