Literature
Required and recommended readings, with lawful links
This page lists every work that the course cites. The reference list at the end gives the full details. The repository contains no copy of a copyrighted book. Each link leads to the publisher, to an open-access copy, or to a library record.
Required reading
Read this text before the course day.
- Emily M. Bender, Timnit Gebru, Angelina McMillan-Major, and Shmargaret Shmitchell, On the Dangers of Stochastic Parrots: Can Language Models Be Too Big? (2021), pp. 610–623. DOI and publisher page.
Lens 1 builds on this text. Its arguments about scale, environmental cost, and documentation of training data also return in Lenses 2 and 3.
Recommended readings
These readings are optional. Use them to go deeper into a lens before or after the course day. The table names the selected chapters and the lens that each reading supports.
| Reading | Selected part | Lens | Access |
|---|---|---|---|
| Buolamwini, Unmasking AI (2023) | Chapter 1, “Daughter of Art and Science”; chapter 11, “Gender Shades” | 2 | Library or bookshop |
| Crawford, Atlas of AI (2021) | Chapters 1–3: “Earth”, “Labor”, “Data” | 1, 3 | Publisher, library |
| Loukissas, All Data Are Local (2019) | Chapter 3, “Collecting Infrastructures” | 1 | Open access |
| O’Neil, Weapons of Math Destruction (2016) | Introduction; chapter 1, “Bomb Parts: What Is a Model?” | 3, 4 | Library or bookshop |
| Erscoi, Kleinherenbrink, and Guest, Pygmalion Displacement (2023) | Whole paper | 2 | Open preprint |
| Buschek and Thorp, Models All the Way Down (2024) | Whole investigation | 1, 2 | Open web publication |
| Birhane, Prabhu, and Kahembwe, Multimodal Datasets (2021) | Sections on the audit of LAION-400M | 2 | Open preprint |
| Widder, West, and Whittaker, Open (For Business) (2023) | Whole paper | 4 | SSRN preprint |
| Mitchell and Krakauer, The Debate over Understanding (2023) | Whole paper | 1 | Open access |
NoteFull publication versions
Widder, Whittaker, and West published a revised version of Open (For Business) in Nature as Why ‘Open’ AI Systems Are Actually Closed, and Why This Matters (2024). Either version works for Lens 4. The author order differs between the two versions.
CautionContent note
Birhane, Prabhu, and Kahembwe (2021) and the Stanford Internet Observatory report (2023) describe sexual violence, pornography, and child sexual abuse material in training datasets. The course discusses how such material enters datasets. It never shows the material. You can choose other readings for Lens 2.
Sources of the cases
Each lens uses one documented case. These sources support the case and the evidence on the lens page.
| Lens | Case | Main sources |
|---|---|---|
| 1 | The documented footprint and data governance of BLOOM | Luccioni et al. (2023); Laurençon et al. (2022); Jernite et al. (2022) |
| 2 | The LAION-5B pathway, from web links to generated images | Buschek and Thorp (2024); Birhane et al. (2021); Thiel (2023); LAION (2024); Bianchi et al. (2023) |
| 3 | Outsourced safety labeling for ChatGPT in Kenya | Perrigo (2023); Ouyang et al. (2022); Gray and Suri (2019) |
| 4 | “Open” model licenses and the EU AI Act | Meta (2023); Open Source Initiative (2024); Liesenfeld and Dingemanse (2024); European Parliament and Council of the European Union (2024) |
Methods and frameworks
- Documentation and disclosure: Datasheets for Datasets (Gebru et al. 2021) and Model Cards for Model Reporting (Mitchell et al. 2019).
- Risks across the lifecycle: Suresh and Guttag (2021) and Weidinger et al. (2022).
- Panarchy and Ecocycle: the Liberating Structures pages for Panarchy and Ecocycle Planning, Lipmanowicz and McCandless (2014), McCandless and White (2026), and Holling (2001) on the adaptive cycle.
Preparation time
The assignments page gives a plan for the eight hours of preparation.
References
Bender, Emily M., Timnit Gebru, Angelina McMillan-Major, and Shmargaret Shmitchell. 2021. “On the Dangers of Stochastic Parrots: Can Language Models Be Too Big?” Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency, FAccT ’21, 610–23. https://doi.org/10.1145/3442188.3445922.
Bianchi, Federico, Pratyusha Kalluri, Esin Durmus, et al. 2023. “Easily Accessible Text-to-Image Generation Amplifies Demographic Stereotypes at Large Scale.” Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency, FAccT ’23, 1493–504. https://doi.org/10.1145/3593013.3594095.
Birhane, Abeba, Vinay Uday Prabhu, and Emmanuel Kahembwe. 2021. Multimodal Datasets: Misogyny, Pornography, and Malignant Stereotypes. arXiv preprint 2110.01963. https://doi.org/10.48550/arXiv.2110.01963.
Buolamwini, Joy. 2023. Unmasking AI: My Mission to Protect What Is Human in a World of Machines. Random House.
Buschek, Christo, and Jer Thorp. 2024. “Models All the Way Down.” Knowing Machines. https://knowingmachines.org/models-all-the-way.
Crawford, Kate. 2021. Atlas of AI: Power, Politics, and the Planetary Costs of Artificial Intelligence. Yale University Press. https://yalebooks.yale.edu/book/9780300252392/atlas-of-ai/.
Erscoi, Lelia, Annelies Véronique Kleinherenbrink, and Olivia Guest. 2023. Pygmalion Displacement: When Humanising AI Dehumanises Women. SocArXiv preprint. https://doi.org/10.31235/osf.io/jqxb6.
European Parliament and Council of the European Union. 2024. Regulation (EU) 2024/1689 Laying down Harmonised Rules on Artificial Intelligence (Artificial Intelligence Act). Official Journal of the European Union, L series, 12 July 2024. https://eur-lex.europa.eu/eli/reg/2024/1689/oj.
Gebru, Timnit, Jamie Morgenstern, Briana Vecchione, et al. 2021. “Datasheets for Datasets.” Communications of the ACM 64 (12): 86–92. https://doi.org/10.1145/3458723.
Gray, Mary L., and Siddharth Suri. 2019. Ghost Work: How to Stop Silicon Valley from Building a New Global Underclass. Houghton Mifflin Harcourt.
Hawkins, Will, Brent Mittelstadt, and Chris Russell. 2025. “Deepfakes on Demand: The Rise of Accessible Non-Consensual Deepfake Image Generators.” Proceedings of the 2025 ACM Conference on Fairness, Accountability, and Transparency, FAccT ’25, 1602–14. https://doi.org/10.1145/3715275.3732107.
Holling, C. S. 2001. “Understanding the Complexity of Economic, Ecological, and Social Systems.” Ecosystems 4 (5): 390–405. https://doi.org/10.1007/s10021-001-0101-5.
International Energy Agency. 2025. Energy and AI. International Energy Agency. https://www.iea.org/reports/energy-and-ai.
Jernite, Yacine, Huu Nguyen, Stella Biderman, et al. 2022. “Data Governance in the Age of Large-Scale Data-Driven Language Technology.” Proceedings of the 2022 ACM Conference on Fairness, Accountability, and Transparency, FAccT ’22, 2206–22. https://doi.org/10.1145/3531146.3534637.
LAION. 2024. “Releasing Re-LAION-5B: Transparent Iteration on LAION-5B with Additional Safety Fixes.” LAION, August 30. https://laion.ai/blog/relaion-5b/.
Laurençon, Hugo, Lucile Saulnier, Thomas Wang, et al. 2022. “The BigScience ROOTS Corpus: A 1.6TB Composite Multilingual Dataset.” Advances in Neural Information Processing Systems 35 (NeurIPS 2022), Datasets and Benchmarks Track. https://doi.org/10.52202/068431-2306.
Li, Pengfei, Jianyi Yang, Mohammad A. Islam, and Shaolei Ren. 2025. “Making AI Less ‘Thirsty’.” Communications of the ACM 68 (7): 54–61. https://doi.org/10.1145/3724499.
Liberating Structures. 2026a. “Ecocycle Planning.” Liberating Structures. https://www.liberatingstructures.com/ecocycle-planning.
Liberating Structures. 2026b. “Panarchy.” Liberating Structures. https://www.liberatingstructures.com/panarchy.
Liesenfeld, Andreas, and Mark Dingemanse. 2024. “Rethinking Open Source Generative AI: Open Washing and the EU AI Act.” Proceedings of the 2024 ACM Conference on Fairness, Accountability, and Transparency, FAccT ’24, 1774–87. https://doi.org/10.1145/3630106.3659005.
Lipmanowicz, Henri, and Keith McCandless. 2014. The Surprising Power of Liberating Structures: Simple Rules to Unleash a Culture of Innovation. Liberating Structures Press.
Loukissas, Yanni Alexander. 2019. All Data Are Local: Thinking Critically in a Data-Driven Society. MIT Press. https://doi.org/10.7551/mitpress/11543.001.0001.
Luccioni, Alexandra Sasha, Sylvain Viguier, and Anne-Laure Ligozat. 2023. “Estimating the Carbon Footprint of BLOOM, a 176B Parameter Language Model.” Journal of Machine Learning Research 24 (253): 1–15. https://jmlr.org/papers/v24/23-0069.html.
Luccioni, Sasha, Yacine Jernite, and Emma Strubell. 2024. “Power Hungry Processing: Watts Driving the Cost of AI Deployment?” Proceedings of the 2024 ACM Conference on Fairness, Accountability, and Transparency, FAccT ’24, 85–99. https://doi.org/10.1145/3630106.3658542.
Maffulli, Stefano. 2023. “Meta’s LLaMa License Is Not Open Source.” Open Source Initiative, July 20. https://opensource.org/blog/metas-llama-2-license-is-not-open-source.
McCandless, Keith, and Nancy White. 2026. The Liberating Structures Fieldbook: Flipping the Script on Meetings, Planning and Progress. https://www.liberatingstructures.com/2026-fieldbook.
Meta. 2023. “Llama 2 Community License Agreement.” Meta, July 18. https://ai.meta.com/llama/license/.
Miceli, Milagros, and Julian Posada. 2022. “The Data-Production Dispositif.” Proceedings of the ACM on Human-Computer Interaction 6 (CSCW2): 1–37. https://doi.org/10.1145/3555561.
Mitchell, Margaret, Simone Wu, Andrew Zaldivar, et al. 2019. “Model Cards for Model Reporting.” Proceedings of the Conference on Fairness, Accountability, and Transparency, FAT* ’19, 220–29. https://doi.org/10.1145/3287560.3287596.
Mitchell, Melanie, and David C. Krakauer. 2023. “The Debate over Understanding in AI’s Large Language Models.” Proceedings of the National Academy of Sciences 120 (13): e2215907120. https://doi.org/10.1073/pnas.2215907120.
O’Neil, Cathy. 2016. Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy. Crown.
Open Source Initiative. 2024. “The Open Source AI Definition 1.0.” Open Source Initiative, October 28. https://opensource.org/ai/open-source-ai-definition.
Ouyang, Long, Jeff Wu, Xu Jiang, Diogo Almeida, et al. 2022. Training Language Models to Follow Instructions with Human Feedback. arXiv preprint 2203.02155. Published at NeurIPS 2022. https://doi.org/10.48550/arXiv.2203.02155.
Perrigo, Billy. 2023. “Exclusive: OpenAI Used Kenyan Workers on Less Than $2 Per Hour to Make ChatGPT Less Toxic.” TIME, January 18. https://time.com/6247678/openai-chatgpt-kenya-workers/.
Strubell, Emma, Ananya Ganesh, and Andrew McCallum. 2019. “Energy and Policy Considerations for Deep Learning in NLP.” Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, 3645–50. https://doi.org/10.18653/v1/P19-1355.
Suresh, Harini, and John Guttag. 2021. “A Framework for Understanding Sources of Harm Throughout the Machine Learning Life Cycle.” Equity and Access in Algorithms, Mechanisms, and Optimization, EAAMO ’21, 1–9. https://doi.org/10.1145/3465416.3483305.
Taylor, Astra. 2018. “The Automation Charade.” Logic(s), no. 5 (August). https://logicmag.io/failure/the-automation-charade/.
Thiel, David. 2023. Identifying and Eliminating CSAM in Generative ML Training Data and Models. Stanford Internet Observatory. https://doi.org/10.25740/kh752sm9123.
Weidinger, Laura, Jonathan Uesato, Maribeth Rauh, et al. 2022. “Taxonomy of Risks Posed by Language Models.” Proceedings of the 2022 ACM Conference on Fairness, Accountability, and Transparency, FAccT ’22, 214–29. https://doi.org/10.1145/3531146.3533088.
Widder, David Gray, Sarah Myers West, and Meredith Whittaker. 2023. Open (for Business): Big Tech, Concentrated Power, and the Political Economy of Open AI. SSRN preprint. https://doi.org/10.2139/ssrn.4543807.
Widder, David Gray, Meredith Whittaker, and Sarah Myers West. 2024. “Why ‘Open’ AI Systems Are Actually Closed, and Why This Matters.” Nature 635 (8040): 827–33. https://doi.org/10.1038/s41586-024-08141-1.