Data C104

Semester: Fall 2026
Instructors: Ari Edmundson, Cathryn Carson
Lecture: Mo, We, Fr 3:00 - 3:59 pm, Wheeler 150

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

Week	Date	Lecture	Readings
Week 1: Foundations	Wed Aug 26	1. Making the Datafied World 1	Langdon Winner, “Do Artifacts Have Politics”, Daedalus
	Fri Aug 28	2. Making the Datafied World 2	No new reading
Week 2: Making Data	Mon Aug 31	3. Making Data	G.C. Bowker and S.L. Star, Sorting Things Out: Classification and Its Consequences (Cambridge, MA: MIT Press, 2000), “The Case of Race Classification and Reclassification under Apartheid” Bay Area Air Quality Management District (BAAQMD), Public Data Center
	Wed Sep 02	4. Making Personal Data	No new reading Optional Reading: Ruha Benjamin, Ch. 3 “Coded Exposure”, Race After Technology: Abolitionist Tools for the New Jim Code Rebecca Lemov, “‘Big Data is People!’”, Aeon Sarah Igo, “Me and My Data,”Historical Studies in the Natural Sciences
	Fri Sep 04	5. Making People Out of Data	Ian Hacking, “Making Up People”, London Review of Books
Week 3: Making Robust Knowledge	Mon Sep 07	No Lecture	No reading
	Wed Sep 09	6. Making Robust Knowledge	Thomas Kuhn, “The Route to Normal Science” (selection) and “Resolution of Revolutions”, The Structure of Scientific Revolutions Optional Reading: Sergio Sismondo, “A Prehistory of Science and Technology Studies” and “The Kuhnian Revolution”, An Introduction to Science and Technology Studies Philip Ball, “Is AI Leading to a Reproducibility Crisis in Science?” Nature
	Fri Sep 11	7. 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
Week 4: How Was the World Datafied?	Mon Sep 14	8. States and Populations	James C. Scott, “Nature and Space”, Seeing Like a State: How Certain Schemes to Improve the Human Condition Have Failed
	Wed Sep 16	9. Eugenics and Statistics	Aubrey Clayton, “How Eugenics Shaped Statistics”, Nautilus
	Fri Sep 18	10. Global Data	Andrew Brooks, “Why Are Certain Countries Poor? Dismantling Comparative Models of Development”, Bullshit Comparisons
Week 5: Privacy	Mon Sep 21	11. Privacy 1	Nathan Malkin, “Contextual Integrity, Explained: A More Usable Privacy Definition”, IEEE Security & Privacy Optional Reading: Daniel J. Solove, Understanding Privacy, ch. 1, “Privacy: A concept in disarray.”
	Wed Sep 23	12. Privacy 2	Lowry Pressly, “Introduction”, *The Right to Oblivion: Privacy and the Good Life Optional Reading: “In Post Roe v. Wade Era, Mozilla Labels 18 of 25 Period and Pregnancy Tracking Tech with *Privacy Not Included Warning”, Mozilla Blog, August 17, 2022. Jen Caltrider et al, “It’s Official: Cars Are the Worst Product Category We Have Ever Reviewed For Privacy”, Mozilla Foundation, 6 September 2023.
	Fri Sep 25	No Lecture - Midterm 1	No readings
Week 6: Making Social Order	Mon Sep 28	13. Surveillance	Michel Foucault, “Panopticism,” Discipline and Punish Elizabeth Daniel Vasquez “The N.Y.P.D. Is Teaching America How to Track Everyone Every Day Forever”, The New York Times “Search Party from Ring: Be a Hero in Your Neighborhood” ad from Super Bowl 2026 Optional Reading: “NSA Whistleblower Edward Snowden: ‘I don’t want to live in a society that does these sorts of things’”, The Guardian Gaby Del Valle, “The Most Surveilled Place in America”, The Verge
	Wed Sep 30	14. Quantification	Theodore Porter, “Objectivity and Authority: How French Engineers Reduced Public Utility to Numbers” Poetics Today
	Fri Oct 02	15. Making Decisions	No new reading Optional Reading: Karen Yeung, “‘Hypernudge’: Big Data as a mode of regulation by design”, Information, Communication, and Society
Week 7: Automated Decision-Making	Mon Oct 05	16. Automated Decision-Making 1	Virginia Eubanks, Automating Inequality: How High-Tech Tools Profile, Police, and Punish the Poor (St. Martin’s Press, 2017), Chapter 4, “The Allegheny Algorithm,” Optional Reading: Os Keyes, et al. “A Mulching Proposal: Analysing and Improving an Algorithmic System for Turning the Elderly into High-Nutrient Slurry” CHI 2019 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, The Washington Post Anna Lauren Hoffmann, “Where Fairness Fails: Data, Algorithms, and the Limits of Anti-Discrimination Discourse”, Information, Communication, and Society
	Wed Oct 07	17. Automated Decision-Making 2	Marion Fourcade and Kieran Healy, Chapter 3: “Classification Situations” The Ordinal Society
	Fri Oct 09	18. Automated Decision-Making 3	No new reading Optional Reading: Caley Horan, “The Unisex Insurance Debate and the Triumph of Actuarial Fairness” Insurance Era: Risk, Governance, and the Privatization of Security in Postwar America Jathan Sadowski, “Total Life Insurance: Logics of Anticipatory Control and Actuarial Governance in Insurance Technology”, Social Studies of Science
Week 8: Data/AI Futures	Mon Oct 12	19. Data Futures	M. Hildebrandt, Smart Technologies and the End(s) of Law: Novel Entanglements of Law and Technology (Elgar, 2015), “Introduction: Diana’s OnLife World” [selection] Optional Reading: S. Cave and K. Dihal, “The Whiteness of AI”, Philosophy & Technology Raffi Khatchadourian, “The Doomsday Invention,” The New Yorker
	Wed Oct 14	20. AI Futures	Ursula K. Le Guin, “The Carrier Bag Theory of Fiction (1986), Dancing at the Edge of the World: Thoughts on Words, Women, Places Marc Andreesen, “The Tech Optimist Manifesto”
	Fri Oct 16	21. Automation	David Noble, “Social Choice in Machine Design: The Case of Automatically Controlled Machine Tools”, The Social Shaping of Technology Optional Readings: Aaron Benanav, Dissent, Fall 2020. “A World Without Work?” Harry Braverman, “Machinery,” Labor and Monopoly Capitalism
Week 9: Silicon Valley	Mon Oct 19	22. Silicon Valley 1	Tom Nicholas, “Silicon Valley and the Emergence of Investment Styles,” VC: An American History Eisenhower Speech L. Vinsel and A. Russel, “Hail the maintainers”, Aeon
	Wed Oct 21	23. Silicon Valley 2	Tianyu M. Fang, “Silicon Valley Goes to War”, Noema Optional Reading: Leif Wenar, “The Deaths of Effective Altruism”, Wired Fred Turner, “Machine Politics: The rise of the internet and a new age of authoritarianism”, Harper’s Malcolm Harris, “The Silicon Valley Loop: How the dot-com crash created Palo Alto’s clueless investor class.” New York Magazine
	Fri Oct 23	24. Platforms, Labor, and Data Capitalism	Kate Crawford, “Labor,” Atlas of AI Mona Sloane, “The Business of AI,” Predicted: How AI Is Restructuring Social Life Optional Reading: L. Hyman, “It’s Not Technology That’s Disrupting Our Jobs”, The New York Times Veena Dubal, Dissent, Fall 2020. “Digital Piecework”
Week 10: The Tech Workplace	Mon Oct 26	25. The Tech Workplace 1	A. Wiener, “Uncanny Valley”, n+1 Optional reading: Sam Kriss, “Child’s Play,” Harper’s
	Wed Oct 28	26. The Tech Workplace 2	Theo Baker, “The Stanford Freshmen Who Want to Rule the World,” The Atlantic
	Fri Oct 30	No Lecture - Midterm 2	No new reading
Week 11: Applied Ethics	Mon Nov 02	27. Environment	Jasmine Sun, “no data centers in my backyard” Substack Optional Reading: Kate Crawford and Vladan Joler, “Anatomy of an AI System.”
	Wed Nov 04	28. Moral Philosophy	Amia Srinivasan, “Stop the Robot Apocalypse”, London Review of Books MIT Moral Machine (go to “start judging” and “browse scenarios”) Optional Reading: C. Fleddermann. Engineering Ethics (4th Edition), “Chapter 3: Understanding Ethical Problems” [selection]
	Fri Nov 06	29. AI Ethics	Mona Sloane, “The Guardrails of AI,” Predicted: How AI Is Restructuring Social Life Claude Constitution Optional Reading: Paul Bloom, “How Moral Can AI Really Be?” The New Yorker Roel Dobbe et al., “Hard Choices in Artificial Intelligence”, Artificial Intelligence
Week 12: Ethics as Institutions	Mon Nov 09	30. Codes of Ethics	Luke Munn, “The Uselessness of AI Ethics”, AI Ethics Langdon Winner, “Brandy, Cigars, and Human Values,” in The Whale and the Reactor: A Search for Limits in an Age of High Technology, 2nd ed. Optional Readings: Margo Boenig-Liptsin, Anissa Tanweer, and Ari Edmundson. “Data Science Ethos Lifecycle: Interplay of Ethical Thinking and Data Science Practice,” Journal of Statistics and Data Science Education John Naughton, “Will Computer Science Have an Ethical Reckoning?” American Statistical Association (ASA) and Association for Computing Machinery (ACM) codes of ethics
	Wed Nov 11	No Lecture	No new reading
	Fri Nov 13	31. Research Ethics	J. Radin, “‘Digital Natives’: How Medical and Indigenous Histories Matter for Big Data”, Osiris Optional Reading: “The Belmont Report: Ethical Principles and Guidelines for the Protection of Human Subjects of Research” Sections “Ethical Principles and Guidelines for Research Involving Human Subjects,” and “Part B: Basic Ethical Principles.” UC Berkeley Committee for Protection of Human Subjects, “About CPHS/OPHS” and “Guide to the IRB Review Process.” M. Zook et al. (2017) “Ten simple rules for responsible big data research” PLoS Comput. Biol.
Week 13: Open Science and Data from Below	Mon Nov 16	32. Open Science	Denisse Alejandra,“Reimagining Open Science Through a Feminist Lens,” Medium Optional Reading: Sabina Leonelli, “Philosophy of Open Science”, Cambridge Elements in the Philosophy of Science Amia Srinivasan, “Stop the Robot Apocalypse”, London Review of Books Claude Constitution
	Wed Nov 18	33. Data From Below	Gwen Ottinger, “Data Hopes,” The Science of Repair “Feminist Data Manifest-No”
	Fri Nov 20	34.
Week 14: Thanksgiving Break	Mon Nov 23	No Lecture
	Wed Nov 25	No Lecture
	Fri Nov 27	No Lecture
Week 15: Conclusions	Mon Nov 30	35. Conclusions
	Wed Dec 02	36. Ask Me Anything
	Fri Dec 04	37.
Week 16: RRR	Mon Dec 07	RRR Week
	Wed Dec 09	RRR Week
	Fri Dec 11	RRR Week
Week 17: Finals	Mon Dec 14
	Tue Dec 15	Final Exam, 7pm