Data C104

Semester: Spring 2025
Instructors: Ari Edmundson, Cathryn Carson, Danny Roddy, Massimo Mazzotti
Lecture: 2:00 - 3:00, Dwinelle 155

Why this course?

Data-driven analytics and artificial intelligence-powered devices now shape innumerable aspects of our lives. Beneath the surface of these technologies, computational and increasingly autonomous techniques that operate on large, ever-evolving datasets are transforming how people act in and know the world. These new analytic tools, algorithmic systems, and computational infrastructures draw from and reconstruct existing societal structures, patterns, and narratives. Sometimes this is obvious, and sometimes it is invisibly so. Data technologies have profound consequences for how we think of ourselves, relate to one another, organize collective life, and envision desirable futures. This course helps you identify and analyze these human stakes, reason about them with others, and shape opportunities to take action toward outcomes that can serve collective well-being.

If you intend to major or minor in Data Science, the course meets the Human Contexts and Ethics (HCE) requirement of Berkeley’s Data Science program. It gives you systematic exposure and reflective practice in engaging with the human actions, decisions, and social structures that intrinsically shape your work.

If you don’t intend to major or minor in Data Science, the course will jumpstart your knowledge and strengthen your capacity to take part in guiding our datafied world. The course carries the broad, inclusive spirit of Berkeley’s Data Science curriculum into the area of human society and collective world-making.

Breadth Requirements: This class has been approved for breadth in Philosophy & Values and Historical Studies and the EECS/LSCS Ethics requirement.

STS Minor: This class counts as an upper-division elective for the undergraduate minor in Science, Technology, and Society.

Scope and Objectives

How do we shape action together in our complex and changing datafied world, aiming at outcomes that improve the human condition? To help you on this path, this course provides an overall introduction to how data science and data technologies – including data analytics, algorithmic decision systems, machine learning (ML), and artificial intelligence (AI) – are entangled today with diverse human contexts (histories, institutions, and material bases) and ethics (domains of moral action, collective world-making, and justice).

We will bring historically-grounded perspectives, frameworks from Science, Technology, and Society (STS), and approaches from other disciplines in the humanities and interpretive social sciences to bear on topics that include:

• Doing ethical data science amid shifting definitions of human subjects, consent, and privacy;
• Understanding representation, power shifts, and justice in data-enabled technologies, including predictive analytics, precision (targeted) services, and surveillance technologies;
• Contemporary landscapes of labor and industry; and
• The changing relationship between data, democracy, and public life.

The course aims, first of all, to prepare you to recognize when, where, and how data, analytics, and associated technologies shape and govern the human condition. Further, it aims to provide you with a toolkit with which to think critically about human contexts and ethics issues of data science and data technologies when you encounter them in routine work or daily life, to influence how data science is used to achieve better outcomes for people, and to be able to articulate (for yourself, and to others) what “better” means.

Course Calendar

Unit	Week	Date	Lecture	Readings
Unit 1: Making the Datafied World	Week 1: Foundations	Wed Jan 22	1. Making the Datafied World 1	Langdon Winnner, "Do Artifacts Have Politics," Daedalus
		Fri Jan 24	2. Making the Datafied World 2	Donald MacKenzie and Judy Wajcman, “Introductory Essay,” The Social Shaping of Technology
	Week 2: Making Data	Mon Jan 27	3. Making Data	G.C. Bowker and S.L. Star, “To Classify is Human,” Sorting Things Out: Classification and Its Consequences Bay Area Air Quality Management District (BAAQMD)
		Wed Jan 29	4. Making Personal Data	Ruha Benjamin, “Coded Exposure,” Race After Technology: Abolitionist Tools for the New Jim Code
		Fri Jan 31	5. Making People Out of Data	Ian Hacking, “Making Up People”, London Review of Books
	Week 3: Narratives and Metaphors of Data	Mon Feb 03	6. Data Futures	George Orwell, 1984 Mireille Hildebrandt, “Introduction: Diana's OnLife World,” Smart Technologies and the End(s) of Law: Novel Entanglements of Law and Technology Vlad Savov, “Google's Selfish Ledger is an unsettling vision of Silicon Valley social engineering,” The Verge
		Wed Feb 05	7. Making Decisions, Making Choices	No Readings
		Fri Feb 07	8. Data Capitalism	Jathan Sadowski, “Chapter 2: A Universe of Data,” Too Smart: How Digital Capitalism is Extracting Data, Controlling Our Lives, and Taking Over the World Billy Perrigo, “OpenAI Used Kenyan Workers on Less Than $2 Per Hour to Make ChatGPT Less Toxic,” Time Magazine
	Week 4: How Was the World Datafied?	Mon Feb 10	9. States and Populations	James C. Scott, “Nature and Space,” Seeing Like a State: How Certain Schemes to Improve the Human Condition Have Failed
		Wed Feb 12	10. Eugenics and Statistics	Aubrey Clayton, “How Eugenics Shaped Statistics,” Nautilus
		Fri Feb 14	11. Global Data	Andrew Brooks, “Why Are Certain Countries Poor? Dismantling Comparative Models of Development,” Bullshit Comparisons
Unit 2: Governance	Week 5: Governing with Data	Mon Feb 17	12. No Class - President's Day	No Readings
		Wed Feb 19	13. Global Health Data	Sara L. M. Davis, “Contested Indicators,” The Uncounted: Politics of Data in Global Health
		Fri Feb 21	14. Surveillance and Security	Michel Foucault, “Panopticism,” Discipline and Punish Gaby Del Valle, “The Most Surveilled Place in America,” The Verge
	Week 6: Privacy	Mon Feb 24	15. No Class - Midterm 1	No Readings
		Wed Feb 26	16. Privacy Foundations	Nathan Malkin, “Contextual Integrity, Explained: A More Usable Privacy Definition,” IEEE Security & Privacy
		Fri Feb 28	17. Privacy in the World	Sarah Igo, “Me and My Data,” Historical Studies in the Natural Sciences Jen Caltrider et al, “It's Official: Cars Are the Worst Product Category We Have Ever Reviewed For Privacy,” Mozilla Foundation
	Week 7: Governing with Algorithms	Mon Mar 03	18. Governing with Algorithms 1: Automated Decision Making and Fairness	J. Angwin, J. Larson, S. Mattu, and L. Kirchner, “Machine Bias: There's software used across the country to predict future criminals. And it's biased against Blacks,” ProPublica A. Feller, E. Pierson, S. Corbett-Davies, and S. Goel, “A computer program used for bail and sentencing decisions was labeled biased against Blacks. It's actually not that clear,” Monkey Cage
		Wed Mar 05	19. Governing with Algorithms 2: Fairness as Sociotechnical	Virginia Eubanks, “The Allegheny Algorithm,” Automating Inequality: How High-Tech Tools Profile, Police, and Punish the Poor
		Fri Mar 07	20. Governing with Algorithms 3: The Politics of Risk	Os Keyes, et al. “A Mulching Proposal: Analysing and Improving an Algorithmic System for Turning the Elderly into High-Nutrient Slurry,” CHI
	Week 8: Making Socially Robust Knowledge	Mon Mar 10	21. Making Robust Knowledge	Thomas Kuhn, “Introduction: A Role for History," The Structure of Scientific Revolutions
		Wed Mar 12	22. Working in the Open	Denisse Alejandra, “Reimagining Open Science Through a Feminist Lens,” Medium
		Fri Mar 14	23. Democracy and Expertise	Harry Collins and Trevor Pinch, “The Science of the Lambs:Chernobyl and the Cumbrian Sheep Farmers,” The Golem at Large: What You Should Know about Technology
Unit 3: Industry, Capitalism, and Labor	Week 9: Silicon Valley	Mon Mar 17	24. Silicon Valley, Part 1	Lee Vinsel and Andrew Russel, “Hail the maintainers,” Aeon Eisenhower Speech
		Wed Mar 19	25. Silicon Valley, Part 2	Fred Turner, “Machine Politics: The rise of the internet and a new age of authoritarianism,” Harper's
		Fri Mar 21	26. No Class	No Readings
	Spring Break	Mon Mar 24	27. No Class	No Readings
		Wed Mar 26	28. No Class	No Readings
		Fri Mar 28	29. No Class	No Readings
	Week 10: The Tech Workplace	Mon Mar 31	30. The Tech Workplace, Part 1	A. Wiener, “Uncanny Valley,” n+1
		Wed Apr 02	31. The Tech Workplace, Part 2	Alex Hanna, “On Racialized Tech Organizations and Complaint: A Goodbye to Google,” Medium
		Fri Apr 04	32. Environmental Contexts and Consequences of Data	Kate Crawford and Vladan Joler, “Anatomy of an AI System”
Unit 4: Sites of Data Ethics Today	Week 11: Labor and Industry	Mon Apr 07	33. Midterm Exam	No Readings
		Wed Apr 09	34. Labor in the Datafied World	Louis Hyman, “It's Not Technology That's Disrupting Our Jobs,” The New York Times
		Fri Apr 11	35. Automation and Labor	Kate Crawford, “Labor,” Atlas of AI
	Week 12: Ethics as Institutions	Mon Apr 14	36. Professional Ethical Codes	Luke Munn, “The Uselessness of AI Ethics,” AI Ethics Langdon Winner, “Brandy, Cigars, and Human Values,” The Whale and the Reactor: A Search for Limits in an Age of High Technology
		Wed Apr 16	37. Research Ethics - Checklists, Duties, and Routines	Joanna Radin, “Digital Natives': How Medical and Indigenous Stories Matter for Data,”Osiris
		Fri Apr 18	38. No Class	No Readings
	Week 13: AI Ethics, Ethics as World-Making	Mon Apr 21	39. Moral Philosophy	Madeleine Clare Elish, “Moral Crumple Zones: Cautionary Tales in Human-Robot Interactions,” Engaging Science, Technology, and Society MIT Moral Machine
		Wed Apr 23	40. AI Ethics	Amia Srinivasan, “Stop the Robot Apocalypse,” London Review of Books
		Fri Apr 25	41. No Class	No Readings
	Week 14: Conclusions	Mon Apr 28	42. Conclusions	Feminist Data Manifest-No
		Wed Apr 30	43. Ask Me Anything	No Readings
		Fri May 02	44. After Data C104: HCE Alumni Spotlight	No Readings