Simon Fraser University
Engaging the World

Course Outlines

Fall 2024 - CMPT 985 G100

Special Topics in Graphics, HCI, Visualization, Vision, Multimedia (3)

Data Feminism

Class Number: 6413

Delivery Method: In Person

Overview

  • Course Times + Location:

    Sep 4 – Oct 11, 2024: Tue, 2:30–5:20 p.m.
    Burnaby

    Oct 16 – Dec 3, 2024: Tue, 2:30–5:20 p.m.
    Burnaby

Description

CALENDAR DESCRIPTION:

Examines current research topics in computer graphics, human computer interaction (including audio), computer vision and visualization.

COURSE DETAILS:

To describe this course, I quote Catherine D’Ignazio and Lauren Klien the authors of Data Feminism. In this course we will discuss “a new way of thinking about data science and data ethics that is informed by the ideas of intersectional feminism.

Today, data science is a form of power. It has been used to expose injustice, improve health outcomes, and topple governments. But it has also been used to discriminate, police, and surveil. This potential for good, on the one hand, and harm, on the other, makes it essential to ask: Data science by whom? Data science for whom? Data science with whose interests in mind? The narratives around big data and data science are overwhelmingly white, male, and techno-heroic. In Data Feminism, Catherine D'Ignazio and Lauren Klein present a new way of thinking about data science and data ethics—one that is informed by intersectional feminist thought.

Illustrating data feminism in action, D'Ignazio and Klein show how challenges to the male/female binary can help challenge other hierarchical (and empirically wrong) classification systems. They explain how, for example, an understanding of emotion can expand our ideas about effective data visualization, and how the concept of invisible labor can expose the significant human efforts required by our automated systems. And they show why the data never, ever “speak for themselves.”

Data Feminism offers strategies for data scientists seeking to learn how feminism can help them work toward justice, and for feminists who want to focus their efforts on the growing field of data science. But Data Feminism is about much more than gender. It is about power, about who has it and who doesn't, and about how those differentials of power can be challenged and changed.”

COURSE-LEVEL EDUCATIONAL GOALS:

Notes
- Recommended but not required prerequisites include visualization and courses taught from a human centered perspective

Topics covered (tentative)

  1. Why data science needs feminism
  2. Ways of knowing
  3. Intersectional feminism
  4. A critical perspective on data science, and visualization
  5. Power and data
  6. Working with data (collect, analyze, imagine, teach)
  7. Issues with comprehension and mis-representation in data visualization
  8. Issues with colonialism and elitism
  9. What gets counted counts
  10. Embracing Pluralism
  11. Considering Context. Data feminism asserts that data are not neutral or objective
  12. Making data work visible
  13. Data activism

Grading

NOTES:

Grading (tentative)

  • Class activities and assignments 40%
  • Final Project 60%

Materials

MATERIALS + SUPPLIES:

 

 

 

 

REQUIRED READING:

Data feminism
D'ignazio, Catherine, and Lauren F. Klein
MIT press
2023
https://data-feminism.mitpress.mit.edu/
ISBN: 9780262044004

RECOMMENDED READING:

Decolonizing Research
Indigenous Storywork as Methodology

Bloomsbury Publishing,2019
https://www.bloomsbury.com/ca/decolonizing-research-9781350348172/


ISBN: 9781350348172

Invisible women: Data bias in a world designed for men
Perez, Caroline Criado
Abrams, 2019
https://www.abramsbooks.com/product/invisible-women_9781419735219/
ISBN: 9781419729072

Weapons of math destruction: How big data increases inequality and threatens democracy
Cathy O'Neil
Crown
2017
https://crownpublishing.com/search/?keyword=weapons-of-math-destruction
ISBN: 9780553418835

REQUIRED READING NOTES:

Your personalized Course Material list, including digital and physical textbooks, are available through the SFU Bookstore website by simply entering your Computing ID at: shop.sfu.ca/course-materials/my-personalized-course-materials.

Graduate Studies Notes:

Important dates and deadlines for graduate students are found here: http://www.sfu.ca/dean-gradstudies/current/important_dates/guidelines.html. The deadline to drop a course with a 100% refund is the end of week 2. The deadline to drop with no notation on your transcript is the end of week 3.

Registrar Notes:

ACADEMIC INTEGRITY: YOUR WORK, YOUR SUCCESS

SFU’s Academic Integrity website http://www.sfu.ca/students/academicintegrity.html is filled with information on what is meant by academic dishonesty, where you can find resources to help with your studies and the consequences of cheating. Check out the site for more information and videos that help explain the issues in plain English.

Each student is responsible for his or her conduct as it affects the university community. Academic dishonesty, in whatever form, is ultimately destructive of the values of the university. Furthermore, it is unfair and discouraging to the majority of students who pursue their studies honestly. Scholarly integrity is required of all members of the university. http://www.sfu.ca/policies/gazette/student/s10-01.html

RELIGIOUS ACCOMMODATION

Students with a faith background who may need accommodations during the term are encouraged to assess their needs as soon as possible and review the Multifaith religious accommodations website. The page outlines ways they begin working toward an accommodation and ensure solutions can be reached in a timely fashion.