Course description
Summary, audience, objectives, and the four lenses
Summary
Generative AI is used increasingly in research, yet its environmental costs, training data, labor conditions, discriminatory representations, commercial incentives, and governance structures are often considered separately. This workshop combines four critical lenses with practical risk-analysis tools. Participants investigate the design, data, training, deployment, evaluation, and governance of text- and image-generating systems; then use Panarchy and Ecocycle Planning to identify feasible changes in their own research environments. The aim is a defensible, transparent approach to responsible generative AI use rather than a universal list of permitted and forbidden tools.
Audience
The workshop is for doctoral candidates and postdocs from all disciplines. Each edition sets its own eligibility, capacity, and credits. The edition pages give these facts. No programming experience is required.
Learning outcomes
After the course, participants should be able to:
- Analyze environmental, social, cultural, and institutional issues in generative AI through four distinct analytical lenses.
- Identify normative risks at design → data → training → deployment → evaluation → governance stages, including affected people and potential mitigation.
- Apply risk maps, documentation and disclosure heuristics, evaluation questions, and governance checks to their own research.
- Formulate and defend a context-specific position on responsible research use of generative AI, informed by scholarship and governance frameworks (including the EU AI Act as one example).
The four lenses
The four lenses give the critical vocabulary. Each lens has its own lesson page, slide deck, documented case, activity, and worksheet.
| Lens | Essential teaching points | Output |
|---|---|---|
| 1. Stochastic Parrots: Scale and Environment | Scale is not a sufficient criterion for quality or social benefit. Energy, water, minerals, and infrastructure. Data sourcing, scraping, consent, filtering, and exclusions. | Material-and-provenance map |
| 2. Representation and Pygmalion | Text-to-image datasets. Cultural categories, aesthetics, stereotypes, and representational harm. Humanizing models while dehumanizing women. Non-consensual synthetic imagery. | Representation and harm pathway |
| 3. Lifecycle and Labor: Materiality and Extraction | The model lifecycle and the people it affects. Data labeling, content moderation, RLHF, and gig work. “Fauxtomation.” Copyright, consent, provenance, and invisible labor. | Lifecycle risk matrix |
| 4. Political Economy: Power and Markets | Concentrated infrastructure and platform power. Incentives for safety and release decisions. Open versus proprietary claims. Procurement, policy, and the EU AI Act. | Stakeholder and incentive map |
Panarchy and Ecocycle
Panarchy is the central method of the afternoon. It is not an optional extra. The group maps how changes at fast, local levels and at slow, institutional levels influence each other. The central challenge is:
What changes across interconnected levels would make generative AI use in academic research more responsible?
A short adaptation of Ecocycle Planning then sorts the priority initiatives. A research clinic turns the findings into a personal position on responsible use.
Format
The course uses short lectures, guided discussion, sourced cases, small-group mapping, a collaborative Panarchy and Ecocycle exercise, and a research project clinic.
Bring these items:
- A laptop.
- A short description of one actual or proposed use of generative AI in your research. The description must not be confidential.
- Do not disclose protected research data. Use a description, not the data.
- Do not create a commercial AI account. No mandatory exercise needs one.
The website is in English. The language of instruction is not stated on this site. Ask the course administration of your edition if you need to know it.
What the course is not
The course does not give a list of permitted and forbidden tools. It does not replace the rules of your university, your faculty, or your funder. It gives you questions, evidence, and methods, so that you can make and defend your own decision.