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DATA 4AC - Spring 2021 - public
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Image: W.E.B. Du Bois, "Assessed Valuation of All Taxable Property Owned by Georgia Negroes," 1900.

DATA 4AC: Data and Justice

UC Berkeley / Spring 2021
© 2021. This syllabus is licensed under a CC BY-NC 4.0 license.

Instructors: Ari Edmundson and Margo Boenig-Liptsin
Graduate Student Instructor: Janet Torres

Course format: Optional synchronous lecture (T, Th 2-3:30pm PST) with required synchronous discussion (Fridays).

Dear Student, WELCOME TO DATA 4AC! 

This is a brand new course, taught in the midst of a world in which the core pieces of the course -- data, justice, race, and American Cultures -- are evolving daily. We want the course to be responsive to this world and to your needs as students. This syllabus is subject to evolve as well. Please check back frequently and, if you are a student, consult the bCourses site which will have the most up-to-date information and links.

… And thank you for joining us on the inaugural DATA 4AC journey!

Your instructors,

Ari Edmundson, Margo Boenig-Liptsin, and Janet Torres

Course Description

From W.E.B. Du Bois’s pioneering visualizations of Black life in the post-Reconstruction United States to contemporary racial justice projects such as Data for Black Lives, practices of data collection, visualization, and analysis have been complexly entangled in the struggle for racial and social justice.

The relationship between data and justice is many-faceted and complicated. Data can make injustice visible, imaginable -- and thus actionable -- to wider publics. They have served as tools for public witnessing, advocacy, and activism in ways that have redefined the ways in which some people think about and pursue justice today.

At the same time, technology leaders who design and deploy data collection, predictive analytics, and autonomous and intelligent decision-making systems claim that these technologies will remove problematic biases from consequential decisions about the allocation of scarce resources. They aim to put a rational and objective foundation based on numbers and observations made by non-human sensors in the management of public life and to equip experts with insights-at-scale that, they believe, will translate into better outcomes (health, economic, educational, judicial) for all.

But these dreams and their pursuit through technology are as problematic as they are enticing. Throughout American history, data has often been employed as a tool of injustice. It has been used to oppress minoritized communities, manage populations, and institutionalize, rationalize, and naturalize systems of racial violence. The impersonality of data -- the same quality that makes it useful -- can silence voices and displace entire ways of knowing the world.

How do things stand with data and justice today? And how did we get here? This course examines the ways in which data, as a product of human design and currently one of the most powerful tools through which human beings build their worlds, has through the centuries of American history and in relation to the diverse communities that live together in the United States been used to advance, detract from, and transform the idea of justice.

The course engages students with these topics through two weekly lectures, weekly discussion sections, and frequent low-stakes assignments including readings, short response papers, and hands-on “modules” using data science tools to investigate questions related to the carceral system, health outcomes, and environmental justice. In lieu of a final exam, there will be a final project.

We invite students to become a community that, informed through the historical and structural ways the course offers to think about the relationship between data and justice, thinks together (and as part of our broader roles as students, faculty, children, neighbors, and parents) about how we might use the powerful data tools at our disposal to pursue the project of justice and belonging for all.

American Cultures

The course examines the lived experience of data and justice by different American races, ethnicities, and cultures, foregrounding in particular the experiences of African Americans, indigenous peoples of the United States, Chicanx/Latinx communities, and Asian Americans. It invites students to consider the ways in which groups' experiences are informed by their different positionings in an American society characterized by systemic racial oppression and in a context in which racial stratification co-evolves with data technologies. Students learn key theoretical concepts from critical race theory, queer and feminist studies, ethnic studies, and science, technology, and society (STS) that enable them to analyze and critique the power of technology in the constitution of American cultures.

Data Science

This course uses humanistic and social scientific lenses to engage students with key issues in the social context and ethical practice of data science. It provides entry-level, guided hands-on experience with easily accessible data science platforms and tools. It has no prerequisites. Students with previous computational or statistical experience will have no advantage over others.

For students who find they wish to do further studies in data science, Data 4AC can be followed by Data 6 (Introduction to Computational Thinking with Data, satisfying the L&S SBS breadth requirement) and/or Data 8 (Foundations of Data Science, satisfying the L&S Quantitative Reasoning requirement). For students in the Data Science major, Data 4AC can serve as a lower-division class for Domain Emphases in Inequalities in Society; Organizations and the Economy; Social Welfare, Health, and Poverty; and Social Policy and Law. Data 4AC is a lower-division course and does not meet the upper-division HCE requirement for the Data Science major.

Scope and Objectives

What students will be able to do after the course:

See the co-production of justice with data in the United States, specifically in racial contexts:

Recognize the mark of American histories in today's sociotechnical systems:

Value and use diverse forms of knowing:

Re-imagine and build just human-technology futures with others:

Assignments and Grading

Welcome to DATA 4AC! We have designed the assignments to be opportunities for you to show what you have learned, learn some more through the process of working through them, and support meaningful deliverables and relationships that will outlast the class and continue to serve you through your UC Berkeley studies and beyond. If at any time you find that, having put your energy and labor towards an assignment, this goal is not reached or if you feel stressed about grades, please come talk with us. We're always glad to discuss alternative options and find ways to support you through the work.

This is a 4-unit course, which translates into 12 hours of coursework per week (lecture + discussion + readings and assignments). Students will be graded on their ability to independently and creatively engage with the themes of this course. Students are not expected to memorize content associated with a particular topic or domain, but rather to demonstrate the capacity to collaborate with other students in using data tools together with social scientific and humanistic perspectives to situate, understand, and advance social justice.

Data persuasion essay (Midterm): For their Midterm evaluation (in lieu of an exam), students will write a 1,000 word essay that uses at least two data visualizations to make an argument about a salient injustice in today’s world related to one of the course’s “sites of justice.” For this assignment students do not have to generate a new visualization from a dataset. Instead, students will be asked to use existing visualizations and write original supporting text to make a persuasive argument to a specific audience about the nature or causes of a particular injustice.

Modules (6): Students will work through a sequence of data science modules during the semester. These are interactive homeworks in the form of Jupyter notebooks that prompt students to think about how data can be used to represent and make arguments about justice, as well as to explore how it has been used as an instrument of injustice. The modules will be graded on completeness: if you complete the module, you will receive full credit.

Weekly section attendance is mandatory. Absences will only be excused for documented medical and family emergencies. It is possible to make-up a missed section by attending another one during the same week on a one-time basis, but you will need to receive advance permission from both your GSI and the GSI whose section you plan to attend.

We realize that during the course of the semester, especially during a pandemic and remote-learning, unanticipated issues can come up and we want to support you to do well in the course. If you need anything, please reach out to us to explain your situation. The more open our communication and the earlier we learn about any challenges, the better we'll be able to work together to come up with a solution.

Course project 

Students will choose one of the two following collaborative assignments for their course project. Students will have the opportunity to present their course projects during the Data Science Showcase at the end of the Spring semester.

  1. “Narratives of Justice” - Multimedia Storytelling

Students will work in teams of 3-4 to create a multimedia narrative of justice about a case study of their own choosing. The project should not only make an argument, but also tell a story using data to describe a problem of justice in the datafied world.

We understand that it can be challenging to work as a group, especially when we cannot meet in-person, but team-work is also a skill that is central to working in the real-world and we believe that the relationships you form in class are an essential part of learning and can provide lasting satisfaction and meaning. We will support you through the team-making process by facilitating group work throughout the semester where you can learn about and from each other and by providing class time to work on your projects.

Each Narrative must include the following components:

  1. “Tools of Justice”

Students will work in teams of 3-4 to create an “tool of justice” that could be used to help communities fight for justice around a particular “site.” This tool:

  1. uses data to fight a particular injustice in American society/“democratizes data”
  2. and/or helps fight for the abolition of unjust data practices and systems

The "tool" will be accompanied by

 

Some possible templates:

We will work up to the completed course project through a series of assignments throughout the semester. These include:

Two reflections (individual): Students will write two short (300-500) word personal essays responding to prompts on their personal experiences or relationship to major questions of data justice in today’s world. These assignments will be in preparation for your course project.

        Reflection 1 due at 11:59pm PST on W 1/27. See bCourses for prompt.

        Reflection 2 due at 11:59pm PST on W 2/10. See bCourses for prompt.

Project proposal (group): A short (approx. 500 word) description of the project you and your group are planning to undertake.

Project proposal due at 11:59pm PST on W 3/10.

Project plan (group): A plan detailing the issues, deliverable, concept, data resources, any community partners, and team member responsibilities for your course project. Each team will present their plan to peers during section discussion and receive feedback from peers and instructors.

Project plan due at 11:59pm PST on W 3/31, presentations of plan will take place during section on F 4/2.

Completed course project and presentation (group): Submit your course project and deliver a 10-minute presentation in the last week of class during one of the lecture times to classmates, community members, Berkeley faculty and invited guests to celebrate your project.

Completed course projects are due on the day of your presentation, either 2pm PST T 4/27 or Th 4/29.

Grade distribution:

Midterm: Data persuasion essay

15%

Modules (6)

20%

Section Attendance and Participation

25%

Course project

40%

Student Hours

We're eager to get to know you and speak with you about your interests in the course material, answer questions or brainstorm about assignments, discuss navigating Berkeley education, or whatever else is on your mind. We strongly encourage you to stop by our informal student hours, when our Zoom "door" is wide open! See you soon!

Prof. Margo Boenig-Liptsin

Wednesdays 11-12pm and 2:30-3:30pm PST.

My student hour will be an "open door" time, so you're welcome to stop by anytime and join whoever is in the zoom room. If you'd like to schedule a one-on-one appointment, please email me and I'll be glad to set that up.

Prof. Ari Edmundson

        Mondays 11am-1pm

GSI Janet Torres

Weekly Schedule of Lectures and Readings

Week 1: Justice in the Datafied World

1.1

T 1/19

Introduction: Why Data and Justice? Why now?

(Ari Edmundson and Margo Boenig-Liptsin)

  • Ruha, Benjamin, Race After Technology (Introduction)

1.2

Th 1/21

Race, Power, and Technology

(Ari Edmundson)

  • Omi and Winant, Racial Formation in the United States, Chapter 4, “The Theory of Racial Formation, Read pp. pp. 105-115 carefully, skim 115-124 (pp. 124-132 optional)

Optional/Texts we’re reading

D.1

F 1/22

Why justice? What does justice mean to you? How do you imagine a just world? In the context of racial and other social systems that pervasively structure experience?

Week 2: Race, Justice, and Data from the "New World" to BLM

2.1

T 1/26

Part I: "New World" to Reconstruction

(Ari Edmundson)

Optional/Texts we’re reading

  • W.E.B. Du Bois, Black Reconstruction in America, Chapter 1, “The Black Worker”
  • Walter Johnson, “To Remake the World:” Boston Review
  • Evelyn Nakano Glenn, Unequal Freedom, Chapter 4, “Blacks and Whites in the South”
  • David Roediger, Wages of Whiteness
  • Cedric Robinson, The Black Radical Tradition
  • Manning Marable, How Capitalism Underdeveloped Black America
  • Jodi Melamed, “Racial Capitalism,”                                        

W 1/27

Reflection #1 due 11:59pm PST in bCourses. Please see bCourses for reflection prompt.

2.2

Th 1/28

Part II: Jim Crow to BLM

(Ari Edmundson and Margo Boenig-Liptsin)

Optional/Texts we’re reading

D.2

Review Reflection #1. Axis exercise.

Week 3: Justice in Practice

Module 1: Introduction to Jupyter Notebooks

3.1

T 2/2

Social Justice, Justice as Fairness, and Public Computing

(Margo Boenig-Liptsin)

  • Langston Hughes, “Justice,” see bCourses "Poetry Page"
  • Toni Morrison, "Narrating the Other," in The Origin of Others: The Charles Eliot Norton Lectures, 2016. Cambridge, MA: Harvard University Press, 2017. pp. 75-91.
  • Patricia Williams, Keynote lecture, Symposium on the Foundations of Responsible Computing (FORC), June 2, 2020.

Optional readings:

3.2

Th 2/4

Data in the Practice of Justice: Examples, Alternatives, and the Future of Data Justice

(Ari Edmundson and Margo Boenig-Liptsin)

  • Ruha Benjamin, Race after Technology: Abolitionist Tools for the New Jim Code, Chapter Five, “Retooling Solidarity, Reimagining Justice.”

Recommended Reading:

D.3

F 2/5

Discussion of Module 1.

Week Four: Bodies and Identities

4.1

T 2/9

Biometrics and Eugenics

(Ari Edmundson and Margo Boenig-Liptsin)

  • Jenny Reardon and Kim TallBear – “‘Your DNA Is Our History’: Genomics, Anthropology, and the Construction of Whiteness as Property,” Current Anthropology, 12 (5) 2012, pp. 233-245.

Recommended Reading:

  • Alondra Nelson, The Social Life of DNA: Race, Reparations, and Reconciliation After the Genome (selection)

Further Reading:

W 2/10

Reflection #2 due at 11:59pm PST in bCourses. Please see bCourses for prompt.

4.2

Th 2/11

Classifying Gender and Queering Justice

(Ari Edmundson)

Further Reading:

D.4

F 2/12

How is the (racialized) body a site of justice? What role does technology, and in particular data technologies, play in making the body into a site of justice? How does rendering the body as data transform the claims of justice that can be made upon it?

Review reflection #2.

Week Five: Citizenship

Module 2: Japanese-American Detainment during WWII

5.1

T 2/16

Who Counts as a Citizen?

(Margo Boenig-Liptsin)

  • Benedict Anderson, "Concepts and Definitions," Imagined Communities: Reflections on the Origins and Spread of Nationalism, New York, London: Verso. 1983, pp. 5-7.

Further Reading:

  • OR The Known Citizen: A History of Privacy in Modern America (selection)

5.2

Th 2/18

Data / Citizen Encounters

(Margo Boenig-Liptsin)

Suggested Reading:

Further Reading:

D.5

F 2/19

Discussion of Module 2.

Notebook 2 technical skills:

  • (guided) loading data
  • (guided) generate interactive graph
  • change variables in the graph
  • Interpret graph

Week Six: Crime, Policing and Prisons

Module 3: Prison Realignment (adapted from ES 21AC, taught by Victoria Robinson)

6.1

T 2/23

Statistics, Criminology, and the Criminalization of Blackness

(Ari Edmundson)

Readings:

Further Reading:

  • 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.
  • History of policing timeline: http://criticalresistance.org/policing-timeline/
  • Khalil Gibran Muhammad, The Condemnation of Blackness, (selections from Chapter 1 and 2)
  • Primary sources from Ida B Wells and W.E.B. Dubois

6.2

Th 2/25

Mass Incarceration and Big Data Today

(Ari Edmundson)

Readings:

Further Reading:

D.6

F 2/26

Discussion of Module 3.

Week Seven: Courtrooms

Module 4: Evaluating the Fairness of Algorithmic Risk Assessment Tools (by Eva Newsom, Alyssa Sugarman, Sammy Raucher)

M 3/1

Data persuasion essay prompt is made available today in bCourses. It will be due T 3/16 by 11:59pm PST.

7.1

T 3/2

Algorithms in the Courtroom

(Margo Boenig-Liptsin and Ari Edmundson)

7.2

Th 3/4

Fairness and Bias: Relating definitions and approaches from machine learning to justice

(Margo Boenig-Liptsin, Ari Edmundson, and Andrew Bray)

Further reading:

D.7

F 3/5

Discussion of Module 4.

What is the relationship among statistical and computational concepts of fairness and bias and broader conceptions of justice? What is the history of this relationship? How does the courtroom navigate among these?

Module 4 technical skills:

  • Understand sensitive features
  • (guided) practice removing sensitive features
  • (guided) Identifying differences between and making sense of metrics of fairness (FNR/FPR, PPV/NPV, ROC Curve)
  • (guided) quantifying and removing disparate impact

Final project structured group work.

Week Eight: Project Resources and Review

8.1

T 3/9

Guest lecture and discussion with Rachel Roberson on Participatory Action Research (PAR)

(Margo Boenig-Liptsin, Ari Edmundson, and guest, Rachel Roberson)

No Readings.

W 3/10

Course project proposal due by 11:59pm PST in bCourses.

8.2

Th 3/11

Mid-semester Review

(Margo Boenig-Liptsin, Ari Edmundson)

No Readings.

D.8

F 3/12

Adobe Creative Discovery Fellows share resources for student projects

Creators of COMPAS module visit discussion sections

Final project structured group work.

Week Nine: Land & Environment 

Notebook 6: Mapping TCE Plumes - Spatial Data and Environmental Justice (by Janet Torres, Varsha Vaidyanath, Camilia Kacimi)

9.1

T 3/16

Environmental Development in the U.S.

(Janet Torres)

Data persuasion essay is due today in bCourses by 11:59pm Pacific.

  • K. DeLuca, (2001), "Imagining Nature and Erasing Class and Race: Carleton Watkins, John Muir, and the Construction of Wilderness" Environmental History (2001): 541-560.

Further Readings:

9.2

Th 3/18

Climate (Science), Citizen (Science), Data (Science)

(Janet Torres)

  • J. Maantay, (2002) "Mapping environmental injustices: pitfalls and potential of geographic information systems in assessing environmental health and equity." Environmental Health Perspectives, 110(suppl 2), 161-171.

  • L. Sealey-Huggins (2017) "‘1.5° C to stay alive’: climate change, imperialism and justice for the Caribbean. Third World Quarterly, 38(11), 2444-2463.

Further Readings:

D.9

F 3/19

Course project structured group work.

Week Ten: Spring Recess 3/22-3/25 - enjoy!

Week Eleven: Public Health and Welfare

Module 5: Bias in algorithmic risk scores for health (adapted by Jason Jiang and team from notebook by Nick Merrill, Samuel Greenberg, Inderpal Kaur)

11.1

T 3/30

Sovereignty and Systems of Oppression in Healthcare

  • Boston Women's Health Collective, 1973. Women and Their Bodies: A Course. "Course Introduction" and "Women, Medicine, and Capitalism: An Introductory Essay."
  • A. Nelson, 2013. Body and Soul: The Black Panther Party and the Fight Against Medical Discrimination. "Introduction: Serving the People, Body and Soul," University of Minnesota Press. Pp. 1-22.

Further Reading:

11.2

Th 4/1

The Modern Welfare State and Algorithms

  • Frederick Wiseman, Welfare, 1975 (watch any 15 minutes of the film, and in particular from 1:00:00 - 1:10:00, the computer scene)
  • Michelle Murphy, The Economization of Life (Duke University Press, 2017) (selection)
  • Virginia Eubanks, Automating Inequality (Allegheny Algorithm or LA homelessness)

Optional Readings:

  • Cybelle Fox, Three Worlds of Relief: Race, Immigration, and the American Welfare State from the Progressive Era to the New Deal
  • Dan Bouk, How Our Days Became Numbered

D.11

F 4/2

Module 6 (Environment) discussed

In what ways does data science contribute to our understanding of the environment and support the capacity to act towards environmental justice at the same time as it exacerbates environmental problems and uneven distribution of environmental harms? What unique opportunities does data science present for public participation in the doing of science and for public's  trust in science? What is the relationship among trust in science, lay knowledges, and justice?

Module 6 technical skills:

  • Spatial data libraries (geopandas, rasterio, matplotlib)
  • (guided) loading a vector and a raster
  • (guided) checking CRS
  • (guided) differences and benefits of linking visual data and table data
  • (guided) plotting data with multiple libraries
  • (guided) calculations on spatial data

Student presentations of course project plans in section, with feedback from peers and instructors

Week Twelve: Housing

12.1

T 4/6

Class, Housing, and Credit

Further Readings:

  • Colin Koopman, “Segregating Data: The Informatics of Racialized Credit, 1923-1937” How We Became Our Data
  • Josh Lauer, Creditworthy: A History of Consumer Surveillance and Financial Identity in America (Columbia University Press, 2017) selection
  • Erin McElroy, “Property as Technology,” City, 24: 1-2, 2020,  pp. 112-129                                                

W 4/7

Course project plan due 11:59pm PST in bCourses.

12.2

Th 4/8

Tech in the Neighborhood, and Housing Justice Activism

No additional readings.

D.12

F 4/9

Module 5 (Public Health) discussed

Module 5 technical skills:

  • (guided) create and interpret scatter plots
  • (guided) use dataframes
  • (guided) fit Generalized Linear Model

Week Thirteen: Labor and the Workplace 

13.1

T 4/13

Managing Labor

  • Caitlin Rosenthal, Accounting for Slavery (selection)
  • Chaplin, Modern Times (selection)

Further reading:

  • Roediger, Wages of Whiteness
  • Nakano Glenn, Unequal Freedom, Chapter 3, “Labor: Freedom and Coercion”
  • Lundy Braun, ch 6
  • Anson Rabinbach, The Human Motor
  • Charles Maier "Between Taylorism and Technocracy" (1970)
  • Gramsci "Americanism and Fordism" (1934)

13.2

Th 4/15

Labor Justice in Silicon Valley and Beyond

Optional Readings:

D.13

F 4/16

Week Fourteen: Education

14.1

T 4/20

Meritocracy and the American University

Further readings:

  • Zeus Leonardo, Race, Whiteness, and Education (selection)
  • Paolo Freire, Pedagogy of the Oppressed (selection)
  • John Carson "The Science of Merit and the Merit of Science: Mental Order and Social Order in Early Twentieth-Century France and America"
  • Stephen Jay Gould "Mismeasure of Man" (selection on IQ testing)

14.2

Th 4/22

Know Data, Know Justice: Data science education and social justice activism at the university

Further readings:

  • Seymour Papert on the democratizing and justice potential of "computer fluency"
  • Spring Ma, "Our Broken Society: How ethics in tech can originate from college initiatives," undergraduate essay

Possible guest speakers:

  • Berkeley student group leaders asking for anti-racism and social justice curricular changes in computing, data science, and statistics education

D.14

F 4/23

Week Fifteen: Re-imagining the Future: Justice With and Beyond Technology

15.1

T 4/27

Student presentations of course projects, Day 1

15.2

Th 4/29

Student presentations of course projects, Day 2

D.15

F 4/30

DATA 4AC Spring 2021 -
© 2021. This syllabus is licensed under a CC BY-NC 4.0 license.