A Non-Technical and Critical Introduction to Natural Language Processing, Language Models, and Text-Centered Artificial Intelligence
Instructor: Kyoungjin Jang-Tucci, Ph.D. Candidate, Department of Educational Policy Studies, University of Wisconsin-Madison
Slides
Google Colab Activities
Link: https://colab.research.google.com/drive/10l9_10cm9OH2HvxPp5QvXdzPPRX98NTE?usp=copy
Padlet 1
Link: https://padlet.com/kjang26/venn-diagram-1v6el2yoxr9rtifr
Padlet 2
Link: https://padlet.com/kjang26/my-shiny-sandbox-w63w02ldg9a3pacz
Resources
Natural Language Processing & Language Model Textbooks & Resource Books
Paaß, G., & Giesselbach, S. (2023). Foundation models for natural language processing: Pre-trained language models integrating media. https://link.springer.com/book/10.1007/978-3-031-23190-2
Sarkar, D. (2019). Text Analytics with Python: A Practitioner’s Guide to Natural Language Processing. Apress. https://link.springer.com/book/10.1007/978-1-4842-4354-1
Zhai, C., & Massung, S. (2016). Text data management and analysis: A practical introduction to information retrieval and text mining. ACM Books. https://dl.acm.org/doi/book/10.1145/2915031
NLP & LM in Research: Examples (Good & Bad)
Kozlowski, A. C., Kwon, H., & Evans, J. A. (2024). In Silico sociology: Forecasting COVID-19 polarization with large language models. arXiV preprint. https://arxiv.org/pdf/2407.11190
Stone, C., Quirk, A., Gardener, M., Hutt, S., Duckworth, A. L., & D’Mello, S. K. (2019, March). Language as thought: Using natural language processing to model noncognitive traits that predict college success. In Proceedings of the 9th International Conference on Learning Analytics & Knowledge (pp. 320-329). https://dl.acm.org/doi/pdf/10.1145/3303772.3303801?casa_token=lkJYYmJ4IJQAAAAA:jiOg5f7dnHAuAfpc8tI-WMy8hPRMkAupyPz74Y4MjzmLoheZqRAAo0BfJmOaa6uc_g7ZrppCgZnT1g
Chopra, F., & Haaland, I. (2023). Conducting qualitative interviews with AI. SSRN Preprint. https://papers.ssrn.com/sol3/Delivery.cfm?abstractid=4583756
Starters of AI Fairness Research
Bender, E. M., Gebru, T., McMillan-Major, A., & Shmitchell, S. (2021). On the dangers of stochastic parrots: Can language models be too big? In Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency (pp. 610–623). Association for Computing Machinery. https://dl.acm.org/doi/10.1145/3442188.3445922
Non-Technical Books & Articles on AI Bias and AI Fairness
Buolamwini, J. (2024). Unmasking AI: My mission to protect what is human in a world of machines. Random House.
Crawford, K. (2021). The Atlas of AI: Power, Politics, and the Planetary Costs of Artificial Intelligence. Yale University Press.
Narayanan, A., & Kapoor, S. (2024). AI snake oil: What artificial intelligence can do, what it can’t, and how to tell the difference. Princeton University Press.
Noble, S. U. (2018). Algorithms of oppression: How search engines reinforce racism. In Algorithms of oppression. New York university press.
AI Bias Research
Armstrong, L., Liu, A., MacNeil, S., & Metaxa, D. (2024, October). The Silicon Ceiling: Auditing GPT’s Race and Gender Biases in Hiring. In Proceedings of the 4th ACM Conference on Equity and Access in Algorithms, Mechanisms, and Optimization (pp. 1-18). https://dl.acm.org/doi/abs/10.1145/3689904.3694699
Buolamwini, J., & Gebru, T. (2018, January). Gender shades: Intersectional accuracy disparities in commercial gender classification. In Conference on fairness, accountability and transparency (pp. 77-91). PMLR. https://proceedings.mlr.press/v81/buolamwini18a.html?mod=article_inline&ref=akusion-ci-shi-dai-bizinesumedeia
Gándara, D., Anahideh, H., Ison, M. P., & Picchiarini, L. (2024). Inside the Black Box: Detecting and Mitigating Algorithmic Bias Across Racialized Groups in College Student-Success Prediction. AERA Open, 10, 23328584241258741. https://journals.sagepub.com/doi/pdf/10.1177/23328584241258741
Media Reports on AI Bias
Dastin, J. (Cotober 10, 2018). Insight – Amazon scraps secret AI recruiting tool that showed bias against women. https://www.reuters.com/article/world/insight-amazon-scraps-secret-ai-recruiting-tool-that-showed-bias-against-women-idUSKCN1MK0AG/
Algorithmic Audit & Mitigating Bias
Metaxa, D., Park, J. S., Robertson, R. E., Karahalios, K., Wilson, C., Hancock, J., & Sandvig, C. (2021). Auditing algorithms: Understanding algorithmic systems from the outside in. Foundations and Trends® in Human–Computer Interaction, 14(4), 272-344. https://www.nowpublishers.com/article/Details/HCI-083
Smith, G., & Rustagi, I. (2020). Mitigating bias in artificial intelligence: An equity fluent leadership playbook. Berkeley Haas Center for Equity, Gender, and Leadership. https://haas.berkeley.edu/wp-content/uploads/UCB_Playbook_R10_V2_spreads2.pdf
Vecchione, B., Levy, K., & Barocas, S. (2021, October). Algorithmic auditing and social justice: Lessons from the history of audit studies. In Proceedings of the 1st ACM Conference on Equity and Access in Algorithms, Mechanisms, and Optimization (pp. 1-9). https://dl.acm.org/doi/pdf/10.1145/3465416.3483294
Advocacy Groups
Algorithmic Justice League. https://www.ajl.org/
Distributed AI Research Institute (DAIR). https://www.dair-institute.org/
Kyoungjin Jang-Tucci 
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