This track allows students to focus on the ways in which technology can assist in the analysis and understanding of literature and textual information.
FALL 2021 Course Descriptions
Course options WITH computational/digital focus:
CDT 30385 Data Feminism taught by Katherine Walden
Feminism isn't only about women, nor is feminism only for women. Feminism is about power - about who has it and who doesn't. And in today's world, data is power. Data can be used to create communities, advance research, and expose injustice. But data can also be used to discriminate, marginalize, and surveil. This course will draw intersectional feminist theory and activism to identify models for challenging existing power differentials in data science, with the aim of using data science methods and tools to work towards justice. Class meetings will be split between discussions of theoretical readings and explorations of data science tools and methods (such as Tableau, RStudio, and Python). Those readings may include chapters from texts that include Catherine D???Ignazio and Lauren Klein???s Data Feminism (2020), Virginia Eubanks???s Automating Inequality: How High-Tech Tools Profile, Police, and Punish the Poor (2018), Ruha Benjamin???s Race After Technology: Abolitionist Tools for the New Jim Code (2019), and Sasha Costanza-Chock???s Design Justice: Community-Led Practices to Build the Worlds We Need (2020). This course will also examine the data advocacy and activism work undertaken by groups like Our Data Bodies, Data for Black Lives, the Anti-Eviction Mapping Project, and Chicago-based Citizens Police Data Project. Over the course of the semester, students will develop original research projects that use data to intervene in issues of inequality and injustice.This course is not about gaining mastery of particular data science tools or methods, therefore familiarity with statistical analysis or data science tools (R, RStudio, Python, etc.) is NOT a prerequisite for this course.
CDT 40405 Critical Digital Studies taught by Ranjodh Dhaliwal
This class introduces students to the many forms in which critical thought has been applied to digital computational systems. Literary critics have long argued that computers are inscription machines (think about reading or writing on disks!), and we shall take a look at the long tradition of literary scholarship trying to understand and think about computation. Thinking critically about these new media technologies, however, is a multidisciplinary undertaking, and we shall also dive into some other variants of media technology studies. To understand these other stories and histories of digitality, we will reach out to approaches that question the role of race, class, and gender in how digital systems have been envisioned, developed, used, and abused. Some hands-on media archaeology will be accompanied by readings that are a mix of science fiction, e-lit, and critical theory.
SPRING 2021 Course Descriptions
Course options WITH computational/digital focus:
CDT 30390 Sport and Big Data taught by Katherine Walden
Sport is one of the most enduringly popular and significant cultural activities in the United States. Data has always been a central part of professional sport in the US, from Henry Chadwick's invention of the baseball box score in the 1850s to the National Football League's use of Wonderlic test scores to evaluate players. This course focuses on the intersecting structures of power and identity that shape how we make sense of the "datification" of professional sport. By focusing on the cultural significance of sport data, this course will put the datafication of sport in historical context and trace the ways the datafication of sport has impacted athletes, fans, media, and other stakeholders in the sport industry. The course will also delve into the technology systems used to collect and analyze sport data, from the TrackMan and PITCHf/x systems used in Major League Baseball to the National Football League's Next Gen Stats partnership to emerging computer vision and artificial intelligence research methods. Readings for this course will draw on texts like Christopher Phillips' Scouting and Scoring: How We Know What We Know About Baseball (2019), Ruha Benjamin's Captivating Technology: Race, Carceral Technoscience, and Liberatory Imagination in Everyday Life (2019), and Michael Lewis' Moneyball: The Art of Winning an Unfair Game (2004). Class meetings will be split between discussions of conceptual readings and applied work with sport data and technology systems. Coursework may include response papers, hands-on work with data, and a final project. Familiarity with statistical analysis, data science, or computer science tools and methods is NOT a prerequisite for this course.
CDT 30395 Race and Technologies of Surveillance taught by Katherine Walden
The United States has a long history of using its most cutting-edge science and technology to discriminate, marginalize, oppress, and surveil. The poorhouse and scientific charity of an earlier era have been replaced by digital tracking and automated decision-making systems like facial recognition and risk prediction algorithms. This course focuses on how automated systems are tasked with making life-and-death choices: which neighborhoods get policed, which families get food, who has housing, and who remains homeless. This course will examine black box tools used in K-12 education, social services, and the criminal justice system to better understand how these technologies reinforce and worsen existing structural inequalities and systems of oppression. Class meetings will be split between discussions of conceptual readings and applied work with technology systems. Readings for this course will draw on texts that include Safiya Noble's Algorithms of Oppression: How Search Engines Reinforce Racism (2018), Virginia Eubanks' Automating Inequality: How High-Tech Tools Profile, Police, and Punish the Poor (2018), Catherine D'Ignazio and Lauren Klein's Data Feminism (2020), and Meredith Broussard's Artificial Unintelligence: How Computers Misunderstand the World (2019). This course will also examine the advocacy and activism work undertaken by groups like Our Data Bodies, Data 4 Black Lives, Algorithmic Justice League, Auditing Algorithms, Big Brother Watch, and Chicago-based Citizens Police Data Project. Coursework may include response papers, hands-on work, and a final project. Familiarity with statistical analysis, data science, or computer science tools and methods is NOT a prerequisite for this course.