DATA c104 Fall 2020 Syllabus

Data c104 / History c184d: Human Contexts and Ethics of Data

Why this course?

Data-driven services 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 revolutionizing how people act in and know the world. These new tools, systems, and infrastructures have profound consequences for how we think of ourselves, relate to one another, organize collective life, and envision desirable futures. 

This course teaches you to use the tools of applied historical thinking and Science, Technology, and Society (STS) to recognize, analyze, and shape the human contexts and ethics of data. It prepares you to engage as a knowledgeable, skillful, and responsible citizen and professional in the varied arenas of our datafied world. You will learn to address:

  • How does data science transform how people live and how societies function? 

  • How do cultures and values inform how data-driven tools are developed and deployed?

  • What assumptions do data-enabled algorithms and tools carry with them?

  • What projections does AI make on the future?

  • How can we shape the outcomes we want to see?

If you intend to major in Data Science, the course meets the Human Contexts and Ethics requirement of Berkeley’s Data Science major. It gives you systematic exposure and reflective practice in the human choices and social structures that intrinsically shape your work.

If you don’t intend to major 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 choices.

Fall 2020, MWF 3 - 4pm, plus sections / On-line, with synchronous and asynchronous components

Instructors: Margo Boenig-Liptsin, Ari Edmundson

The challenge for modern societies is to develop sufficiently powerful and systematic understanding of technology for us to know where the possibilities lie for meaningful political action and responsible governance.
Sheila Jasanoff, 'The Ethics of Invention,' 2016, p. 29
A defining ethical problem of the algorithm concerns not primarily the power to see, to collect, or to survey a vast data landscape, but the power to perceive and distill something for action.
Louise Amoore, 'Cloud Ethics,' 2020, p. 16

Unit 1 - Our Datafied World

1.1 Welcome and Overview: University Education in a World of Data

(Margo Boenig-Liptsin & Ari Edmundson)

1.2  What makes the world datafied - STS analytical lenses

(Margo Boenig-Liptsin)

Optional Reading:

  • G.C. Bowker and S.L. Star, Sorting Things Out: Classification and Its Consequences (Cambridge, MA: MIT Press, 2000), Introduction: "To Classify is Human," pp. 1-13.  [Links to PDF in bCourses]

1.3 Getting oriented in the datafied world -- Facial Recognition Technologies

(Ari Edmundson)

  • R. Benjamin, Race After Technology: Abolitionist Tools for the New Jim Code (Polity, 2019), Ch. 3 “Coded Exposure,” pp. 67-94.  [Links to PDF in bCourses]

  • J. Buolamwini “The Coded Gaze” YouTube, 2016, Video

  • Take a few minutes to explore Buolomwini and Gebru's Gender Shades online project: http://gendershades.org/

  • L. Amoore, Cloud Ethics: Algorithms and the Attributes of Ourselves and Others (Duke University Press, 2020) Introduction: Politics and Ethics in the Age of Algorithms,” pp. 15-18. [Links to PDF in bCourses]

Optional Reading:

1.4 Thinking-in-time about the datafied world I: Biometrics, physiognomy and scientific racism, 1880-1920

(Ari Edmundson)

  • S. Chinoy, “The Racist History Behind Facial Recognition” The New York Times, July 10, 2019

  • S. Browne, Dark Matters: On the Surveillance of Blackness (Duke University Press, 2015), Chapter 3, “B®anding Blackness: Biometric Technology and the Surveillance of Blackness,” pp. 89-130.

Optional Reading:

  • B. Aguera y Arcas et al. “Physiognomy’s New Clothes,”Medium, May 20, 2017. 

  • J. Finn, Capturing the Criminal Image: From Mug Shot to Surveillance Society (University of Minnesota Press, 2009), “Picturing the Criminal: Photography and Criminality in the Nineteenth Century”, pp. 1-30. [Links to PDF in bCourses]

1.5 Thinking-in-time about the datafied world II: The Cold War, computing, and cybernetics, 1945-1965

(Ari Edmundson)

  • P. Erickson et al. How Reason Lost its Mind (University of Chicago Press, 2013), Chapter 1, "Enlightenment Reason, Cold War Rationality, and the Rule of Rules," pp. 27-50. [Links to PDF in bCourses] [focus on pages 27-32]

  • O. Halpern, Beautiful Data: A History of Vision and Reason Since 1945 (Duke University Press, 2014) [selections] Chapter 1, "Archiving: Temporality, Storage, and Interactivity in Cybernetics," pp. 39-48, 61-66, and Chapter 4, “Governing: Designing Information and Reconfiguring Population circa 1959” pp. 199-207.  

Optional Reading:

  • R. Kline, The Cybernetics Moment: Or, Why We Call Our Age the Information Age (Johns Hopkins University Press, 2015) Introduction, pp. 1-8. [Links to PDF in bCourses]

1.6  What makes data?

(Margo Boenig-Liptsin)

1.7 Data futures -- perspectives from past and present

(Ari Edmundson)

Optional Reading:

  • S. Cave and K. Dihal, “The Whiteness of AI,” Philosophy & Technology (2020) 

Unit 2 - Responsible Data

2.1 Ethics and professional practice

(Ari Edmundson)

Optional Readings:

2.2 Responsible institutions and challenges

(Margo Boenig-Liptsin)

Optional Readings:

2.3 Data ethics today -- what it is and how we got here

(Margo Boenig-Liptsin)

2.4 Aiming for the 'good life' with data

(Margo Boenig-Liptsin)

  • W. Keane, Four Lectures on Ethics: Anthropological Perspectives, (HAU Books, 2015), "Lecture 3: Varieties of Ethical Stance," Sections "Ethical Affordances," "Giving an Account of Oneself," "Ethical Reflexivity and Its Historical Objects," "Fist, Second, and Third-Person Stances," "Ethical Stances, Distant ant Committed" 

Optional reading:

  • L. Amoore, Cloud Ethics: Algorithms and the Attributes of Ourselves and Others (Duke University Press, 2020) Introduction: Politics and Ethics in the Age of Algorithms,” pp. 1-20.

2.5 Co-producing technical and social order 

(Margo Boenig-Liptsin)

Optional reading:

  • S. Jasanoff, The Ethics of Invention: Technology and the Human Future (W.W. Norton and Company, Inc., 2016), Ch. 2 ("Risk and Responsibility"), pp. 31-58.

2.6 What is critique? 

(Ari Edmundson)

Optional reading:

Unit 3 - When Data are Personal

3.1 Personal origins of data

(Margo Boenig-Liptsin) 

Optional Reading:

3.2 Selfhood in the age of data and social media

(Ari Edmundson)

  • D. Haraway,The Donna Haraway Reader (Routledge, 2004)“A Manifesto for Cyborgs: Science, Technology, and Socialist Feminism in the 1980’s” pp. 7-13. [Links to PDF in bCourses] 

  • C. Koopman, How We Became Our Data: A Genealogy of the Information Person (University of Chicago Press, 2019) Introduction, “Informational Persons and Our Information Politics,” pp. 1-8. [Links to PDF in bCourses] 

  • G. Wolf, in D. Nafus, Ed., (Cambridge, MA: MIT Press, 2016), Quantified: Biosensing Technologies in Everyday Life, Ch. 4 ("The Quantified Self: Reverse Engineering,") pp. 67-72. [Links to PDF in bCourses] 

Optional Reading:

  • S. Hong, Technologies of Speculation: The Limits of Knowledge in a Data-Driven Society (NYU Press, 2020) Chapter 4, “Data’s Intimacy,” pp. 76-113.

  • K. McCurdy, “When Data Renders Invisible Illnesses Visible,”Medium, July 21, 2020. 

  • S. Turkle (2012) "Connected, but alone?" TedTalk. 

  • Dan Bouk, “The History and Political Economy of Personal Data over the Last Two Centuries in Three Acts” Osiris 32 (1), 85-106

  • John Cheney-Lippold, We Are Data: Algorithms and the making of our digital selves (NYU Press, 2017). Selection

  • Rogers Brubaker, “Digital Hyperconnectivity and the Self,” Theory and Society (2020)

3.3 Privacy 

(pre-recorded lecture: Richmond Wong's lecture from Spring 2020 + optional live discussion during regular lecture time)

Optional Reading:

  • Daniel J. Solove, Understanding Privacy, ch. 1, “Privacy: A concept in disarray,” pp. 1-8. [Links to PDF in bCourses]

  • S. Igo, “Me an d My Data,” Historical Studies in the Natural Sciences 4(5) November 2018: 616-626

3.4 Analytics of personal data

(Margo Boenig-Liptsin)

Optional reading:

  • Nikolas Rose, Inventing Our Selves: Psychology, Power, and Personhood. "Introduction."

Unit 4 - Collective Life

4.1 Populations and states 

(Ari Edmundson)

  • James C. Scott, Seeing Like a State: How Certain Schemes to Improve the Human Condition Have Failed (New Haven, CT: Yale University Press, 1998), [selections] Ch. 1 ("Nature and Space") and Ch. 3 ("Authoritarian High Modernism") pp. 22-33, 87-102

  • Michelle Murphy, The Economization of Life (Duke University Press, 2017) Chapter One, “Economy as Atmosphere,” pp. 17-34.

4.2 Surveillance and security

(Ari Edmundson)

4.3 Predictive policing

(Ari Edmundson)

Optional Reading:

4.4 Making arguments with data

(Ari Edmundson)

4.5 Choice, influence, manipulation, and governance

(Ari Edmundson)

  • Michael Wintroub, “Sordid genealogies: a conjectural history of Cambridge Analytica’s eugenic roots” Humanities and Social Sciences Communications 7(1), 1-16. 

    • OR Marion Fourcade and Fleur Johns, “Loops, ladders and links: the recursivity of social and machine learning,” Theory and Society (2020).

  • Kramer et al. “Experimental evidence of massive-scale emotional contagion through social networks”PNAS June 17, 2014 111 (24) pp. 8788-8790. 

Optional Readings:

4.6 Algorithmic sentencing

(Margo Boenig-Liptsin)

Unit 5 - Data and Democracy

5.1 Commercial content moderation and the election

(Ari Edmundson)

  • TBD

Optional Readings:

5.2 Elections

(Ari Edmundson)

5.3 The Public sphere in the datafied world

(Ari Edmundson)

5.4 Expertise and democracy

(Ari Edmundson)

  • S. Epstein, Impure Science: Aids, Activism, and the Politics of Knowledge (University of California Press, 1996) Introduction, "Controversy, Credibility, and the Public Character of AIDS Research" pp. 1-26. [Links to PDF in bCourses]

Optional Readings:

5.5 Data, algorithms, and the law

(Margo Boenig-Liptsin)

Optional Readings:

  • S. Barocas and A. Selbst, “Big Data’s Disparate Impact,” 104 California Law Review 671 (2016). Especially section I. How Data Mining Discriminates and the Conclusion. 

5.6 Public witnessing and measuring 

(Margo Boenig-Liptsin)

5.7 Election debrief

(Ari Edmundson & Margo Boenig-Liptsin)

Unit 6 - Scientific Research

6.1 Transformations in the foundations of science

(Cathryn Carson and Fernando Perez)

6.2 Transformations in the disciplines

(Panel - TBD)

Optional Readings:

Unit 7 - Machines and Industry

7.1 Making Silicon Valley

(Cathryn Carson)

Optional Readings:

7.2 Industrial revolutions

(Ari Edmundson)

Optional Readings:

7.3 Environmental Contexts and Consequences of Data

(Margo Boenig-Liptsin)

7.4 Transformations of labor

(Ari Edmundson)

Unit 8 - The Ethos of Making

8.1 The tech workplace

(Ari Edmundson)

8.2 Making worlds together

(Margo Boenig-Liptsin)

  • E. Ullman, Life In Code: A Personal History of Technology (Farrar, Straus and Giroux, 2017) "Boom Two: A Farewell," pp. 272-303. [Links to PDF in bCourses]

  • H. Arendt, The Human Condition, (University of Chicago Press, 1958) “Prologue,” pp. 1-6.

8.3 Recap: STS analytical lenses and thinking in time

(Margo Boenig-Liptsin & Ari Edmundson)


UC Berkeley students can access full syllabus and course materials on bCourses