Lens 4: Political Economy

Power and Markets

Laura Wagner and Moritz Mähr

Learning question

Which incentives preserve an unsafe or inequitable practice?

The first three lenses showed costs, harms, and hidden labor. This lens asks why they persist.

Slides: CC BY 4.0. Lesson page and worksheet: maehr.github.io/stochastic-parrots-and-beyond

Ask: “Who decided which AI tool you can use at your institution?” Collect answers. Most decisions were made at a level above the researcher.

1. Concentrated resources

• AI is made of models, data, labor, development frameworks, and computational power.
• A few companies control most of these resources.
• “Openness alone does not perturb the concentration of power in AI.”

Widder, Whittaker, and West (2024), Nature.

Point out that this is a material analysis: it starts from what AI is made of, not from what companies say about it.

2. “Open” is a claim to check

• More than 45 generative AI systems, 14 dimensions of openness.
• Many “open” systems are “open weight” at best.
• Many providers withhold information on training and fine-tuning data.
• Openness is composite and comes in degrees.

Liesenfeld and Dingemanse (2024), FAccT.

Ask: “What would you need to inspect, document, and cite a model in a publication?” Weights alone are rarely enough.

3. Models encode the goals of their makers

• Every model simplifies the world according to the priorities of its makers (O’Neil 2016, ch. 1).
• A system optimized for engagement, market share, or a fast release can conflict with the goals of research.

4. Institutions set defaults

• Universities decide which tools to license, which to recommend, and which data may leave the institution.
• These defaults shape practice more than individual intentions.
• Procurement and policy are places of leverage, not only of compliance.

This concept prepares Panarchy: the university is one of the levels.

5. Regulation: the EU AI Act as one example

From 2 August 2025, providers of general-purpose AI models must, under Article 53:

• keep technical documentation,
• inform providers who build on the model,
• have a policy to comply with EU copyright law,
• publish “a sufficiently detailed summary about the content used for training”.

Free and open-source models with public weights are exempt from the first two duties, unless they have systemic risk.

Fines are possible from 2 August 2026. Models placed on the market before 2 August 2025 have until 2 August 2027.

Regulation (EU) 2024/1689, arts. 53, 101, 111(3), and 113. Not legal advice.

Do not answer whether the Act applies to a specific project. Note such questions for the legal service.

Case: an “open” license

Documented case

Question	Llama 2 license (2023)	Open Source AI Definition 1.0 (2024)
Any purpose, without permission?	No: 700 million user threshold, use policy	Required
Outputs may improve other models?	Not other large language models	No such restriction
Weights available?	Yes, under the license	Required
Detailed information on training data?	Not required	Required

Sources: Meta (2023); Open Source Initiative (2024); Maffulli (2023).

The OSI stated in July 2023 that the Llama 2 license is not open source. Ask: “Why does the word matter?” Reputation, and in the EU fewer regulatory duties.

Who gains, who pays?

Original diagram. CC BY 4.0.

Read one arrow of each kind: money, data, rules. Ask which arrow is missing for the research participants.

Activity · 15 minutes

Map stakeholders and incentives. Groups of three or four, on paper or on a laptop.

1. Choose a research-use card from your group. Use Llama 2 only if no card fits. (3 min)
2. Place the stakeholders. For each: gains, costs or risks, leverage. (7 min)
3. Circle one incentive that keeps an unsafe or inequitable practice in place. (2 min)
4. Write one feasible governance change: who acts, at which level, by when. (3 min)

Worksheet: Lens 4, stakeholder and incentive map

Collect the governance changes on the wall. They are the first material for Panarchy.

Expected output

A stakeholder and incentive map for each group:

• stakeholders with gains, costs, and leverage,
• one circled incentive,
• one feasible governance change with an actor, a level, and a date.

Sources

• Widder, West, and Whittaker (2023): Open (For Business); published version in Nature (2024).
• Liesenfeld and Dingemanse (2024): Rethinking Open Source Generative AI.
• Open Source Initiative (2024): The Open Source AI Definition 1.0.
• Regulation (EU) 2024/1689, the AI Act.

Takeaway

A practice persists when someone gains from it. Find the incentive, then find the level where it can change.

Transition to the integration block: “We now have four maps. Before lunch, we agree on one challenge for the afternoon.”