Atlas of AI book club questions

1. 1.

   Crawford argues that AI is fundamentally about extraction — of minerals, labor, and data. Does that framing match your prior understanding of what machine learning is?

2. 2.

   Where in your own work or daily life do AI systems classify or sort you? Do you have visibility into how those classifications are made?

3. 3.

   The book shows that large training datasets were often assembled without consent from the people in them. What would meaningful consent in this context even look like?

4. 4.

   How does learning about the supply chain of AI — mines, warehouses, annotation workers — change your relationship to the devices and services you use?

5. 5.

   Crawford is skeptical of emotion-detection AI. If emotions are not straightforwardly readable from faces, what are the systems actually classifying?

6. 6.

   Is the bias problem in AI a problem that better data and better techniques can solve, or does Crawford's argument suggest something more structural?

7. 7.

   The book's critical lens focuses on AI as a tool of power. Does that leave enough room to account for cases where AI has expanded access or reduced certain harms?

8. 8.

   Who currently has the institutional standing to constrain how AI is deployed by states and corporations? What would effective accountability look like?

9. 9.

   Crawford argues that AI is often used to automate discrimination that was previously expensive to scale. Does that seem right for the deployments you're aware of?

10. 10.

    The chapter on classification draws connections to nineteenth-century sciences like physiognomy. Does the history of classification matter for how we should regulate its computational equivalents?

11. 11.

    What would it mean for AI development to take the labor and environmental costs Crawford documents seriously from the start, rather than as externalities?

12. 12.

    Is there a form of AI governance or development practice that Crawford's analysis would endorse? Or does the book stop short of that?

How to run the discussion

• Pick six to eight, not all of them. An hour of real discussion covers fewer questions than anyone expects, and racing the list is what makes a book club feel like homework.
• Open with what people noticed, not what they concluded. Asking for a verdict first hardens opinions before anyone has heard each other.
• Let the disagreement run. The questions below that split a room are the ones worth the time; the ones everyone answers the same way can be skipped.
• Ask the quiet person a specific question. A direct, narrow question is far easier to answer than an open invitation to contribute.