Stochastic Parrots and Beyond

Orientation · Normative Dimensions of Generative AI

Laura Wagner and Moritz Mähr

License

These slides are licensed under CC BY 4.0. You can copy, change, and teach them if you give credit.

Source and handouts: maehr.github.io/stochastic-parrots-and-beyond

Press S for the presenter notes and ? for the keyboard shortcuts.

Open with the license slide only briefly. It tells participants that they may keep and reuse everything. Then move to the question.

Our question

How can we make — and defend — a responsible decision about generative AI in our own research?

• Not a list of permitted and forbidden tools.
• A transparent position that you can explain to colleagues, reviewers, and the people your research affects.

Ask: “Who has used a generative AI system for research this month?” Do not ask for details yet. Transition: “Before we judge a use, we need ways of looking at it. We use four lenses.”

Four lenses

1. Scale and environment. What does a model cost, and where do its data come from?
2. Representation and Pygmalion. Whose perspectives become normal?
3. Lifecycle and labor. Who does the work, and who carries the risk?
4. Political economy. Which incentives keep a practice in place?

In the afternoon, Panarchy connects the lenses to the levels where change is possible.

Name each lens with its question. Point out that the lenses overlap on purpose. A single use can raise questions in all four.

One lifecycle, six stages

Original diagram. CC BY 4.0.

Read the stages from left to right. Say that we will mark risks at each stage. Transition: “Let us start with one research use and look at it through all four lenses.”

A short case

Hypothetical example

A doctoral candidate in history wants to use a hosted chatbot to summarize 200 digitized newspaper articles for a literature review.

Lens	First question
Scale and environment	What is known about the model’s training data and energy use?
Representation	Whose voices does the summary make typical, and whose does it drop?
Lifecycle and labor	Who labeled the data, and who checks the summaries?
Political economy	What does the provider gain, and what does the university pay?

This example is invented for teaching.

Ask the group to add one question per lens. Write them on the wall. They come back in the lessons.

Concepts we use all day

• Stochastic parrot: Bender et al.’s term for a system that stitches together linguistic forms from its training data “without any reference to meaning” (2021, 617). Whether such systems understand is debated (Mitchell and Krakauer 2023).
• Documented vs. hypothetical: we mark every example. A product name is not evidence.
• Known, unknown, avoidable: three columns for every cost.
• Levels: a change in a project, a department, a university, an industry, or a law.

Stress the difference between documented and hypothetical. Ask participants to challenge any claim that has no source. Keep four questions apart: how a system is trained, what it can do for a task, whether it understands, and whether a use is justified. The course needs no answer to the third question to ask the fourth.

Activity: your research use · 7 minutes

1. Alone, 2 minutes. On a card, write one actual or planned use of generative AI in your research: the task, the system, why you might use it, and what you do not know about it.
2. Pairs, 5 minutes. Read your cards to each other. Add one question from a lens.

Do not write confidential or personal data on the card.

Hand out cards. Keep strict time. Collect the cards on the wall only if the authors agree. The cards stay anonymous.

Expected output

• One card for each participant, with one research use.
• A wall of anonymous research uses that we return to in every lens.

Each lens adds one worksheet. The afternoon adds a Panarchy chart, an Ecocycle diagnosis, and your own next action.

Transition: “Your card is the thread of the day. In the clinic at the end, you turn it into a position.”

The day

1. Four lenses, one after the other.
2. Integrate the lenses and agree on the Panarchy challenge.
3. Panarchy: a facilitated discussion across levels.
4. Ecocycle: a short adaptation to sort the priorities.
5. Research-use clinic and peer review.
6. Next actions and the follow-up task.

The edition page gives the times.

Do not give times on this slide. They belong to the edition page, so that the deck works for every edition.

Sources

• Bender, Gebru, McMillan-Major, and Shmitchell (2021): On the Dangers of Stochastic Parrots.
• Mitchell and Krakauer (2023): The Debate over Understanding in AI’s Large Language Models.
• Course website: lenses, handouts, and literature.

Takeaway

We do not look for permission. We look for reasons that we can defend.

Stochastic Parrots and Beyond · Laura Wagner and Moritz Mähr · CC BY 4.0

Transition to Lens 1: “The first lens asks what a model’s performance hides.”