Lectures
The syllabus will be updated gradually.
Week 1
Week 1/2: No Class
Week 1/2: Introduction
Introduction
Sign-up Individual Leading Weekly Discussions:
sheet : [here] leave your first name and NetID in the discussion slot.
Week 2
Week 2 - Jan 23: Politics of Classification
Jones, Matthew. "What we talk about when we talk about (big) data." The Journal of Strategic Information Systems 28.1 (2019): 3-16.
Barrowman, Nick. "Why data is never raw." The New Atlantis 56 (2018): 129-135.
Week 2 - Jan 25: Politics of Classification
Chapter 3 of “Sorting Things Out”
Chapter 4 of “Sorting Things Out”
Case-2: https://spectrum.ieee.org/ai-tools-bias-hiring
Week 3: Politics of Measurement
Week 3 - Jan 30: Politics of Measurement
Kathleen H. Pine and Max Liboiron. 2015. The Politics of Measurement and Action. In Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems (CHI '15). Association for Computing Machinery, New York, NY, USA, 3147–3156. DOI: https://doi.org/10.1145/2702123.2702298
Boehner, Kirsten, et al. "How emotion is made and measured." International Journal of Human-Computer Studies 65.4 (2007): 275-291.
Week 3 - Feb 1: Politics of Measurement
Scheuerman, Morgan Klaus, Madeleine Pape, and Alex Hanna. "Auto-essentialization: Gender in automated facial analysis as extended colonial project." Big Data & Society 8.2 (2021): 20539517211053712.
Docherty, Niall, and Asia J. Biega. "(Re) Politicizing Digital Well-Being: Beyond User Engagements." In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems, pp. 1-13. 2022.
Case-3: https://www.princeton.edu/news/2017/04/18/biased-bots-artificial-intelligence-systems-echo-human-prejudices
Submit the Bi-weekly Case-Study Group Report #1 by February 5 11:59 PM
Week 4: Modeling the Reality
Week 4 - Feb 6: Modeling the Reality
Bender, Emily M., and Alexander Koller. "Climbing towards NLU: On meaning, form, and understanding in the age of data." Proceedings of the 58th annual meeting of the association for computational linguistics. 2020. [PDF] [Links to an external site.]
Heaven, Douglas. "Why deep-learning AIs are so easy to fool." Nature 574.7777 (2019): 163-166.
Raji, Inioluwa Deborah, et al. "AI and the everything in the whole wide world benchmark." arXiv preprint arXiv:2111.15366 (2021)
Week 4 - Feb 8: Modeling the Reality
Gordon, M. L., Lam, M. S., Park, J. S., Patel, K., Hancock, J., Hashimoto, T., & Bernstein, M. S. (2022, April). Jury learning: Integrating dissenting voices into machine learning models. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (pp. 1-19).
Kapania, Shivani, Alex S. Taylor, and Ding Wang. "A hunt for the Snark: Annotator Diversity in Data Practices." In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems, pp. 1-15. 2023.
Extra Reading
Succi, Sauro, and Peter V. Coveney. "Big data: the end of the scientific method?." Philosophical Transactions of the Royal Society A 377.2142 (2019): 20180145.
Heaven, Douglas. "Why deep-learning AIs are so easy to fool." Nature 574.7777 (2019): 163-166.
Case-4: https://www.theguardian.com/technology/2023/jul/25/techscape-meta-open-source-large-language-models-llm-ai-twitter-x-apple
Week 5: Data in Action
Week 5 - Feb 13: Data in Action
Passi, Samir, and Solon Barocas. "Problem formulation and fairness." Proceedings of the conference on fairness, accountability, and transparency. 2019.
Passi, Samir, and Phoebe Sengers. "Making data science systems work." Big Data & Society 7.2 (2020): 2053951720939605.
Week 5 - Feb 15: Data in Action
Sambasivan, Nithya, and Rajesh Veeraraghavan. "The Deskilling of Domain Expertise in AI Development." CHI Conference on Human Factors in Computing Systems. 2022.
Lu, Alex Jiahong, et al. "Data work in education: Enacting and negotiating care and control in teachers' use of data-driven classroom surveillance technology." Proceedings of the ACM on Human-Computer Interaction 5.CSCW2 (2021): 1-26.
Case-5: https://www.denkfabrik-bmas.de/fileadmin/Downloads/Publikationen/AI_and_Domain_Knowledge.pdf
Submit the Bi-weekly Case-Study Group Report #2 by February 21 11:59 PM
Week 6: Professor Sharifa out of town
Week 7: Data Work
Week 7 - Feb 27: Data Work
Law, Edith, et al. "Crowdsourcing as a tool for research: Implications of uncertainty." Proceedings of the 2017 ACM conference on computer supported cooperative work and social computing. 2017.
Irani, Lilly. "The cultural work of microwork." New media & society 17.5 (2015): 720-739.
Week 7 - Feb 29: Data Work
Miceli, Milagros, and Julian Posada. "The Data-Production Dispositif." arXiv preprint arXiv:2205.11963 (2022).
Wang, Ding, Shantanu Prabhat, and Nithya Sambasivan. "Whose AI Dream? In search of the aspiration in data annotation." CHI Conference on Human Factors in Computing Systems. 2022.
Week 8: Project pre-presentation
Week 8 - Mar 5: Project pre-presentation
Week 8 - Mar 7: Project pre-presentation
Week 9: Spring Break
Week 9 - Mar 12: Spring break
Week 9 - Mar 14: Spring break
Week 10: Big Data and Environment
Week 10 - Mar 19: Big Data and Environment
Lucivero, Federica. "Big data, big waste? A reflection on the environmental sustainability of big data initiatives." Science and engineering ethics 26.2 (2020): 1009-1030.
Strubell, Emma, Ananya Ganesh, and Andrew McCallum. "Energy and policy considerations for deep learning in NLP." arXiv preprint arXiv:1906.02243 (2019).
Week 10 - Mar 21: Big Data and Environment
Vera, Lourdes A., et al. "When data justice and environmental justice meet: formulating a response to extractive logic through environmental data justice." Information, Communication & Society 22.7 (2019): 1012-1028.
Anthony, Lasse F. Wolff, Benjamin Kanding, and Raghavendra Selvan. "Carbontracker: Tracking and predicting the carbon footprint of training deep learning models." arXiv preprint arXiv:2007.03051 (2020)
Submit the Bi-weekly Case-Study Group Report #3 by March 27 11:59 PM
Week 11: Ethics of Data Visualization
Week 11 - Mar 26: Ethics of Data Visualization
Sultana, Sharifa, Syed Ishtiaque Ahmed, and Jeffrey M. Rzeszotarski. "Seeing in context: Traditional visual communication practices in rural bangladesh." Proceedings of the ACM on Human-Computer Interaction 4.CSCW3 (2021): 1-31.
Christen, Markus, Peter Brugger, and Sara Irina Fabrikant. "Susceptibility of domain experts to color manipulation indicate a need for design principles in data visualization." PloS one 16.2 (2021): e0246479.
Week 11 - Mar 28: Ethics of Data Visualization
Correll, Michael. "Ethical dimensions of visualization research." Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems. 2019.
Hill, Rosemary Lucy. "What is at stake in data visualization? A feminist critique of the rhetorical power of data visualizations in the media." Data Visualization in Society. Amsterdam University Press, 2020. 391-405.
Week 12: Data Colonialism
Week 12 - Apr 2: Data Colonialism
Sahbaz, Ussal. "Artificial intelligence and the risk of new colonialism." Horizons: Journal of International Relations and Sustainable Development 14 (2019): 58-71.
Roberts, Jennafer Shae, and Laura N. Montoya. "Decolonisation, Global Data Law, and Indigenous Data Sovereignty." arXiv preprint arXiv:2208.04700 (2022).
Week 12 - Apr 4: Data Colonialism
Appadurai, Arjun. "Number in the Colonial Imagination”, in eds. Carol A. Breckenridge and Peter van der Veer, Orientalism and the Post Colonial Predicament." (1993).
Couldry, Nick, and Ulises A. Mejias. "Data colonialism: Rethinking big data’s relation to the contemporary subject." Television & New Media 20.4 (2019): 336-349.
Submit the Bi-weekly Case-Study Group Report #4 by April 11 11:59PM
Week 13: Algorithmic Reparations
Week 13 - April 9: Algorithmic Reparations
Davis, Jenny L., Apryl Williams, and Michael W. Yang. "Algorithmic reparation." Big Data & Society. 8.2 (2021): 20539517211044808.
Hoffmann, Anna Lauren. "Where fairness fails: data, algorithms, and the limits of antidiscrimination discourse." Information, Communication & Society 22.7 (2019): 900-915.
Week 13 - April 11: Algorithmic Reparations
Chasalow, Kyla, and Karen Levy. "Representativeness in statistics, politics, and machine learning." Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency. 2021.
The limits of the quantitative approach to discrimination 2022 James Baldwin lecture, Princeton University (lightly edited transcript)1 Arvind Narayanan https://www.cs.princeton.edu/~arvindn/talks/baldwin-discrimination/baldwin-discrimination-transcript.pdf
Week 14: AI Regulation
Week 14 - April 16: AI Regulation
Kaminski, Margot E., and Jennifer M. Urban. "The right to contest AI." Columbia Law Review 121.7 (2021): 1957-2048.
Raji, Inioluwa Deborah, et al. "Closing the AI accountability gap: Defining an end-to-end framework for internal algorithmic auditing." Proceedings of the 2020 conference on fairness, accountability, and transparency. 2020.
Week 14 - April 18: AI Regulation
Scherer, Matthew U. "Regulating artificial intelligence systems: Risks, challenges, competencies, and strategies." Harv. JL & Tech. 29 (2015): 353.
Fournier-Tombs, Eleonore. "Towards a United Nations Internal Regulation for Artificial Intelligence." Big Data & Society 8.2 (2021): 20539517211039493.
Week 15
Week 15 - April 23
Thirunavukarasu, Arun James, Darren Shu Jeng Ting, Kabilan Elangovan, Laura Gutierrez, Ting Fang Tan, and Daniel Shu Wei Ting. "Large language models in medicine." Nature medicine 29, no. 8 (2023): 1930-1940.
Rahimzadeh, Vasiliki, Kristin Kostick-Quenet, Jennifer Blumenthal Barby, and Amy L. McGuire. "Ethics education for healthcare professionals in the era of ChatGPT and other large language models: Do we still need it?." The American Journal of Bioethics 23, no. 10 (2023): 17-27.
Week 15 - April 25: Guest Lecture
Submit the by Final Project Presentation Video April 29, 8 AM
Week 16: Project Presentation
Week 16 - April 30
Project Presentation
Week 16 - May 2
Project Presentation
Submit the Bi-weekly Case-Study Group Report #5 by May 3 11:59 PM
Submit the Final Project Paper by May 9 11:59 AM