Co-liberative Computing


Data Feminism is a paradigm that reimagines the concept of data and its applications while acknowledging the inherent power imbalances within data science. It recognizes that power is unequally distributed globally, with data itself serving as a form of power. Given the often unjust utilization of data, the primary goal of Data Feminism is to realize and reshape these imbalances. Data Feminism goes beyond a narrow focus on gender; rather, it adopts an intersectional approach by acknowledging various factors such as race, class, sexuality, ability, age, and religion that intersect to shape individuals' experiences and opportunities.

The Data Feminism course aims to bridge ethical and social justice themes with advancements in data science, exploring how individuals working with data can actively challenge and transform power differentials through a Data Feminism lens. This course is positioned at the intersection of data science and intersectional feminism. The objectives are mainly drawn based on the seven principles outlined in the book "Data Feminism" by Catherine D'Ignazio and Lauren F. Klein.

The Course Examiner

Amir H. Payberah

The Course Structure

The course contains seven modules, each dedicated to one of the outlined objectives. Within each module, students will engage in two sessions: one lecture and one discussion. In the lecture session of each module, the instructor will provide a comprehensive introduction to the module's context, offering an overview of the designated reading material. Subsequently, students will have one week to thoroughly review the assigned reading materials and submit a detailed critique of the selected papers. The discussion session of each module will be dedicated to a thorough review and in-depth exploration of the module's topic and associated papers.

Intended Learning Outcome (ILO)

After the course, the student should be able to:

  • ILO1: understand the theoritical and technical issues related to data justice.
  • ILO2: apply acquired knowledge to employ data and data science as tools to confront injustices magnified by data and associated techniques.
  • ILO3: analyze and evaluate data science practices by recognizing their biases and taking actions to address them.

Prerequisites

The students should have completed courses in machine learning and deep learning and be familiar with Python programming.

Assessment

Grading in this course will be based on four distinct tasks: completion of module reading assignments, presentation, active participation during module sessions, and the final project. The assignments can be undertaken in groups of two students.

  • Task 1 (reading assignments): each student/group is required to submit a comprehensive review for a set of assigned papers corresponding to each module. The reviews should encompass three key components: (1) a summary of the papers, (2) a thorough discussion highlighting the strengths of each paper, and (3) a critical examination of the limitations inherent in the papers.
  • Task 2 (presentation): each student/group will act as the moderator for a pre-selected module. In this capacity, they are responsible for presenting the set of the assigned papers, contributing to the collective understanding of the module's content.
  • Task 3 (group discussion): students are expected to attend the group presentation sessions and actively engage in the subsequent group discussions.
  • Task 4 (final project): the final project requires each student/group to reproduce a paper relevant to the course topics. A set of papers will be provided to the students, but they also have the option to propose alternative papers for consideration.

Grading

The course will be assessed on a Pass/Fail basis, and successful completion is contingent on meeting specific criteria. These criteria encompass completing at least 75% of the reading assignments, delivering a presentation during the discussion session, attending a minimum of 75% of the student presentation sessions, and successfully implementing the chosen paper, incorporating basic experiments.

Credits

It is a 7.5 ECTS credits course that spans 224 hours over 14 weeks, including the time allocated for the final project.

Schedule

The course will be offered every year during P1 and P2, consisting of one lecture per week. The first session of the course is scheduled to start in September 2024.