AI and Public Governance

POL-32450, Proseminar II, Universität Konstanz, winter semester 2026/27
Tuesdays, 11:45–13:15, Room G307

Carina Hausladen

A Proseminar II trains advanced academic writing
You develop your own research question
You build theory, hypotheses and a research design step by step
- No data collection or analysis
You give and receive peer feedback on each other's writing
Goal: a short exposé (Dec 9) and a long exposé (Feb 3)

What is a Proseminar II?

AI labs no longer just build models
- They hire economists, fund universities, train teachers, write policy
- explicit strategy, and it shapes how AI should be governed
First we understand what AI changes, then we think about how to govern it
- Five areas: work, science, accountability, rules, power
Each reading is a specific study with a research question, a hypothesis and data
Policy documents matter too, but in a Proseminar II we learn from research designs

How should AI be governed?

Paper presentation: graded, 30%
Short exposé: graded, 30%
Long-exposé presentation: ungraded
Long exposé: graded, 40%
AI Observatory: ungraded

Activities and Assessment

10 minutes
- core of your paper (7 min)
- your extension (idea) (3 min)
5 minutes class discussion

Paper Presentation

AI Observatory

5–10 minutes every week: how did we actually use AI for this week's task?
One student leads with real examples: the prompt, the output, what they kept or threw away & summarises the discussion
Overleaf/AI_observatory.tex
Then everyone shares: what worked, what failed, what was useless
"I didn't use AI" counts as an answer
Sign up on Oct 27; for some weeks 2 students can sign up

Sessions

Date	Session	Due before class
Oct 20	Introduction	–
Oct 27	Extension ideas and literature gap	Read your paper; first extension idea
Nov 3	1. Politics of AI and work	Readings; literature gap
Nov 10	2. Who shapes AI science	Readings; hypothesis
Nov 17	3. Voluntary vs. mandatory accountability	Readings; causal research design
Nov 24	4. Designing rules	Readings; dummy figure + results
Dec 1	5. States vs. AI power	Readings; introduction + conclusion
Dec 8	Short exposé presentation + group feedback	Short-exposé draft; ILIAS: revision unit; submit by Dec 9
Dec 15	Theoretical model and DAG	Long exposé: literature-gap figure
Dec 22	1:1 feedback (online)	Long exposé: model and DAG
Jan 12	1:1 writing session	Long exposé: first draft; ILIAS: writing reflection
Jan 19	Presentation: social media thread	Thread on slides
Jan 26	Final presentations + feedback I	–
Feb 2	Final presentations + feedback II	–

Sessions

Date	Session	Due before class
Oct 20	Introduction	–
Oct 27	Extension ideas and literature gap	Read your paper; first extension idea
Nov 3	1. Politics of AI and work	Readings; literature gap
Nov 10	2. Who shapes AI science	Readings; hypothesis
Nov 17	3. Voluntary vs. mandatory accountability	Readings; causal research design
Nov 24	4. Designing rules	Readings; dummy figure + results
Dec 1	5. States vs. AI power	Readings; introduction + conclusion
Dec 8	Short exposé presentation + group feedback	Short-exposé draft; ILIAS: revision unit; submit by Dec 9
Dec 15	Theoretical model and DAG	Long exposé: literature-gap figure
Dec 22	1:1 feedback (online)	Long exposé: model and DAG
Jan 12	1:1 writing session	Long exposé: first draft; ILIAS: writing reflection
Jan 19	Presentation: social media thread	Thread on slides
Jan 26	Final presentations + feedback I	–
Feb 2	Final presentations + feedback II	–

6 ECTS × 30 h = 180
14 class sessions × 1.5 h = 21 h in class
→ 159 h of work outside class
- 13 weeks between sessions × 12 h (1.5 working days) = 156 h

Logistics

Github: https://github.com/carinahausladen/ai-public-governance
Overleaf
Zotero
Workflow: Speech to text, Grammarly, ...

Topics

1. Politics of AI and work

econ.st/4u9RbFT

economist.com

2. Who shapes AI science?

"The issue is not whether my students are valuable. In the long run, they are invaluable. The issue is that their value emerges slowly, whereas AI delivers immediate returns. I feel somewhat embarrassed to admit how tempting this is.

Yet I see these calculations shaping the labs around me. Close colleagues are quietly refraining from taking on as many students as they used to."

3. Voluntary vs. mandatory accountability

4. Designing rules

5. States vs. AI power

Paper Karaoke

2' per paper

Tell us your name !
read the title aloud
read the last name of first author aloud
answer any or all
1. what is the main question?
2. which data did they use to answer it?
3. what did they find?
4. why was this paper interesting?