Data Cultures

Course Description

We know our world, in part, through the data we collect and share about it. We collect and share that data through seemingly never-ending innovation of communication and computational technologies. These technologies and the data they give us are not separate from our cultures. Gender, race, class, and sexuality all help shape and are shaped by the development of new technologies. As Anglea Haas argues, “technology is both integral to culture and always already cultural.” In that sense, technology can be understood as a “co-production” and, in many respects, a co-producer of gender and technology.  

To explore how data does this, we will look at topics such as: women in programming, programming algorithms, social media, home technologies, biotechnology, reproductive technologies, surveillance, and digital entertainment technologies. We will also think through the relationship between machine bias and systemic racism, the role of women in the production of some of the first computers (and their absence later on), how to teach the next generation of professionals who may or may all have access to technology, and how (as Sarah Williams asks in Data Action) to act so that voices of people represented in the data are neither marginalized nor left unheard. This course builds on the established work of feminist rhetorical scholars (Royster, Kirsch, Bizzell, Graban, Enoch, and others) and serves as a response to calls from Villanueva, Agboka, Risam, and McPherson for decolonizing the Western rhetorical canon, technical communication, and the digital humanities.

In addition to academic professionalization exercises and discussions, we will also look at career avenues for positive change outside of academia: Ethics owners in tech companies, careers in nonprofits, and other places where you can help influence positive change.  

Objectives

You will learn about and discuss:

  • A brief history of data, how it can be used for oppressive or negative outcomes, and how it can be used for good.

  • The relationship between machine bias and systemic racism.

  • The role of women in the production of some of the first computers (and their absence).

  • Careers and avenues for contributing to a world of data in positive, impactful ways. 

  • Current scholarly discussion regarding data, algorithms, analysis and visualization.

Goals

You will practice techniques for:

  • Writing an annotated bibliography that suits your research purposes and builds toward a bigger project.

  • Teaching students about data and how to write about data.

  • Conducting research and writing about data and ethics.

Major Assignments

1.     Reading Responses

Complete five (thoughtful) reading responses (~500 words) over the course of the semester, preferably on sections or books that you found particularly inspiring or important for your own work. There will be five total, but you will be able to pick one to skip without penalty.

 

2.    Annotated Bibliography

In consultation with your instructor, and hopefully with some idea for a final project in mind, you will complete an annotated bibliography of about 10-15 sources. Each source should have about 150-200 words which includes:

  • Citation

  • Summary

  • Evaluation of the source

  • Connections to other publications/sources in the annotated bibliography

 

3.     Final Project: Choose Your Own Adventure

Depending on your own personal or professional goals, you will build from your annotated bibliography to propose and complete one of the following avenues for your final project:

  • A substantial undergraduate syllabus or a substantial day-long workshop (modeled perhaps from Tactical Tech)

  • An academic research paper (the beginnings of a conference presentation or an article)

A public-facing output of some kind which may be:

  • the beginnings of a long-form article for a non-specialist audience (with a proposed venue)

  • a grant proposal (with a targeted grant program or opportunity)


 

 
 
 

Readings

Benjamin, Ruha. Race After Technology: Abolitionist Tools for the New Jim Code.

 Halpern, Orit. Beautiful Data: A History of Vision and Reason Since 1945. Duke University Press, 2015.

 Williams, Sarah. Data Action: How to Use Data for Public Good

 

Excerpts and articles:

 

Banks, Adam. Race, Rhetoric, and Technology: Searching for Higher Ground. Routledge, 2015.

Browne, Simone. Dark Matters: On the Surveillance of Blackness. Duke University Press, 2015.

Drucker, Johanna. “Humanities Approaches to Graphical Display.” Digital Humanities Quarterly (5.1), 2011. http://www.digitalhumanities.org/dhq/vol/5/1/000091/000091.html

McPherson, Tara. “Why are the Digital Humanities so White? Or Thinking the Histories of Race and Computation.” Matthew K. Gold, ed. Debates in the Digital Humanities. Minnesota, 2012.

Moss, Emanuel and Jacob Metcalf. Ethics Owners: A New Model of Organizational Responsibility in Data-Driven Technology Companies. Data + Society, 2020. https://datasociety.net/library/ethics-owners/

Noble, Safiya. Algorithms of Oppression. NYU Press, 2018.

Omizo, Ryan, and William Hart Davidson. “Finding Genre Signals in Academic Writing.” Journal of Writing Research (7.3), 2016. 

Perez, Caroline. Invisible Women: Data Bias in a World Designed for Men. Abrams Press, 2019.

Underwood, Ted. Distant Horizons: Digital Evidence and Literary Change. Chicago, 2019. 

Tactical Tech Collective. “Visualizing Information for Advocacy.” https://www.visualisingadvocacy.org/node/638.html