Instructor: Dr. David B. Smith
Credits: 6 ECTS
Recommended Prerequisite: Legal Aspects of Data Science
Course Type: Core Data Science Curriculum (Semester 3)
Moodle Site: https://moodle.modul.ac.at/course/view.php?id=8146
Assignment 8: Bias, Decision-Making, and Harm in AI-Mediated Systems — select a new AI system, construct three deployment scenarios, and map where bias enters and travels through a mediation pathway, how it shapes decisions, and what harms it produces.
Supporting materials:
- System Selection Set — curated systems to choose from
Previous Assignments
- Assignment 7: EU AI Act System Evaluation — classify a system within EU AI Act risk categories and analyze how risk is produced and distributed through mediation pathways.
- Assignment 6: Mediation Pathways Across Physical and Virtual Space — map how agency, information, authority, and accountability travel through mediated systems.
- Assignment 5: System Structuring and Ethical Analysis
- Assignment 4: Ethical Frameworks and the Evaluation of Harm
SEID 2364 explores the societal, cultural, and ethical dimensions of data science. The course examines how intelligent systems shape decision-making, communication, and human values. It is grounded in the Balanced Blended Space (BBS) framework, which models how ethics and agency operate across human, computational, and institutional systems.
Students learn to recognize ethical ambiguity, analyze complex sociotechnical systems, and construct their own personal ethical frameworks. The course integrates both human–human and human–AI collaboration to foster reflective and responsible data practices.
- Develop literacy in ethical reasoning, data governance, and social accountability.
- Apply the BBS model to visualize responsibility and mediation between agents.
- Engage in case-based learning, including Quantum Musico, EU AI Act, and Cultural Heritage & AI.
- Build practical experience working with AI collaborators in real-world ethical analysis.
The course uses a blended, multimodal format combining:
- Face-to-Face sessions (Vienna residencies)
- Synchronous online seminars
- Asynchronous reflective activities and AI-assisted dialogues
- Embedded social-media environments (private Discord/Mastodon labs)
The semester follows a 14-week sequence progressing from individual inquiry → team collaboration → AI-integrated practice → final ethical synthesis.
| Type | Focus | Deliverable Examples |
|---|---|---|
| Individual Inquiry | Personal ethics, self-awareness | Personal Ethical Compass Essay |
| Team Collaboration | Human-only & Human–AI projects | Team Framework Proposal |
| Case Studies | Real-world complexity & ambiguity | Quantum Musico Analysis, EU AI Act Policy Brief |
| Multi-Agent Practice | Distributed collaboration ethics | BBS Mediation Maps |
| Final Reflection | Integration and synthesis | Reflective Portfolio |
| Component | Description | Weight |
|---|---|---|
| Participation & Reflection Logs | Ongoing engagement and reflective journals | 15% |
| Case Study Analysis | Written or multimedia BBS-based analysis | 20% |
| Midterm Policy Exercise | EU AI Act simulation and negotiation | 20% |
| Team Project | Mediated ethics framework or prototype | 30% |
| Final Portfolio | Personal ethical framework and synthesis | 15% |
AI is integrated as both a tool and a collaborator. Students are encouraged to use AI systems (e.g., ChatGPT, Copilot, NotebookLM) responsibly, documenting their interactions and reflecting on the ethical implications of their use. All AI participation must be transparent and properly attributed.
- SYLLABUS.md — Official course syllabus with learning outcomes
- Assignments — All course assignments
- Case Studies — Quantum Musico, social media ethics, and more
- Resources — BBS frameworks, glossaries, and dataset packages
case-studies/contains SEID 2364 case-study materials.assignments/contains all course assignments (4 main assignments + variants).resources/contains BBS frameworks, Moral Machine dataset, and educational glossaries.- For Moral Machine dataset work, use the DOI source as canonical and, if a newer official Dryad release appears, use the latest version and record the version date in your methods appendix.
- Curriculum design and planning materials are maintained in the AI-Curriculum repository.
- All course materials are synced with the official Spring 2026 SYLLABUS.md at repository root.
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