Course description
DATA 4AC engages students with fundamental questions of justice in relation to data and computing in American society. Data collection, visualization, and analysis have been entangled in the struggle for racial and social justice because they can make injustice visible, imaginable, and thus actionable. Data has also been used to oppress minoritized communities and institutionalize, rationalize, and naturalize systems of racial violence. The course examines key sites of justice involving data (such as citizenship, policing, prisons, environment, and health). Along with critical social science tools, students gain introductory experience and do collaborative and creative projects with data science using real-world data.
After completing this course, students will be able to
See the co-production of justice with data in the United States, specifically in racial contexts
- See how justice is a historically contingent category (value-laden, institutionally-embedded), that is co-produced with data technologies
- Identify the fundamental constitutive role of race in matters of data in American life, and how data tools can embed or counteract racialized systems of power
- Recognize the many sites of individual and collective life where justice is at stake in relation to data, in both explicit and hidden ways
- Understand different approaches to justice (theoretical) and their attendant practices, including why we talk about "fairness" in relation to data technology
- Recognize how data science and technology have been used in the pursuit of justice at the same time as they have been tools for perpetuating injustice, particularly in relation to American cultures.
Recognize the mark of American histories in today's sociotechnical systems
- See the historical import of American race relations and history of slavery for the data science tools used today
- Articulate the importance of American history for assessing the importance of justice in today's world
Value and use diverse forms of knowing
- Learn to listen to, recognize, and respect ways of knowing and experiencing the world by people whose positionality is different from one's own, including voices and epistemologies not usually represented in the Academy
- Understand the value of interpretive and critical ways of knowing and gain experience bringing them into technical discussions
- Develop an expansive understanding of what data science expertise is: it involves ways of knowing about issues of power and values beyond domain knowledge, statistics, and computing
- Gain an introduction to the HCE toolkit, including concepts from STS, History, and Philosophy
Re-imagine and build just human-technology futures with others
- Understand how the questions we ask in data science shape the answers that we give
- Gain experience with basic data science tools and approaches, such as Jupyter notebooks and data visualization, that can be used in the service of understanding, critiquing, and changing the world
- Use this expertise to build more just data futures (including building technologies differently, debating in communities about justice in the contemporary world, and reimagining possibilities of our sociotechnical systems, e.g. policing, education, health)
DATA 4AC 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.
Relation to other courses and parts of the Data Science program
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.
Data 4AC Syllabus
A week-by-week plan of the semester. A detailed syllabus with readings and topic descriptions is linked here and also available to students through the DATA 4AC bCourses site.
1. Introduction
1.1 Why data and justice? Why now?
1.2 From Slavery to the 'New Jim Code'
2. Justice in the Datafied World
Data Science Module 1: Introduction to Jupyter Notebooks
2.1 Data, Power, and Racial Capitalism
2.2 Fairness and Justice in the Datafied World: An Intellectual History
2.3 Justice Beyond Fairness: Alternatives, Resistances, and the Future of Data Justice
3. Bodies and Identities
3.1 Biometrics, Eugenics, and Disability
3.2 Biometrics, Eugenics, and Race
3.3 Classifying Gender and Queering Justice
4. Citizenship
Data Science Module 2: Japanese-American Detention during WWII
4.1 Who Counts as Citizen?
4.2 Immigration and Surveillance Today
5. Crime, Policing and Prisons
Data Science Module 3: Prison Realignment
5.1 Statistics, Criminology, and the Criminalization of Blackness
5.2 Mass Incarceration and Big Data Today
6. Courtrooms
Data Science Module 4: Recidivism risk scores and the COMPAS algorithm
6.1 COMPAS, Recidivism, and Pre-trial Detention
6.2 Bias and Fairness in Computing and Statistics - panel
7. Public Health
Data Science Module 5: Bias in algorithmic risk scorse for health
7.1 Health Data and the Quantification of Health
7.2 Sovereignty and Systems of Oppression in Healthcare
8. Land and Environment
Data Science Module 6: Water management and responsibility