FID3216 Data Feminism 7.5 credits

The course contains seven modules. 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.

This 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 an intersectional feminism lens. The objectives are mainly drawn based on the seven principles outlined in the book ”Data Feminism”: (1) examine power, (2) challenge power, (3) elevate emotion and embodiment, (4) rethink binaries and hierarchies, (5) embrace pluralism, (6) consider the context, and (7) make labor visible. The first two modules center on acknowledging the profound significance of identifying systems of power while also recognizing the diverse methods for challenging them. In the third module, the course focuses on appreciating multiple forms of knowledge, including those originating from marginalized communities. Module four engages with the reevaluation of binary and hierarchical structures. Module five delves into pluralism, emphasizing the incorporation of local, indigenous, and experiential data in shaping knowledge paradigms. In the last two modules, the course delves into the contextualization of data and the often overlooked labor involved in data science. The course includes seven modules, each dedicated to fulfilling the outlined objectives. The instructor collaborates with students within each module, covering relevant book chapters and cutting-edge research papers. Students should read the provided material, write reports, and present their findings to the class. By the conclusion of each module, students will have gained insights into the respective topic and will be able to analyze and evaluate biases inherent in data science practices critically.

After the course, the student should be able to:

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

Participants should be enrolled as doctoral students.

The students should be familiar with Python programming and have completed courses on data science or deep learning.

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• Data Feminism, Catherine D'Ignazio and Lauren F. Klein, MIT Press, 2020
• Fairness and Machine Learning: Limitations and Opportunities, Solon Barocas, Moritz Hardt, and Arvind Narayanan, MIT Press, 2023
• The Ethical Algorithm: The Science of Socially Aware Algorithm Design, Michael Kearns and Aaron Roth, Oxford University Press, 2019
• Set of state-of-the-art articles

If the course is discontinued, students may request to be examined during the following two academic years.

• EXA1 - Examination, 7.5 credits, grading scale: P, F

Based on recommendation from KTH’s coordinator for disabilities, the examiner will decide how to adapt an examination for students with documented disability.

The examiner may apply another examination format when re-examining individual students.

Assessment of this course will be based on four distinct tasks:

• Task 1 (reading assignments): Each student/group is required to submit a comprehensive review for a set of assigned papers corresponding to each module.
• Task 2 (presentation): Each student/group should present a set of the assigned papers.
• 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 and deliver an oral presentation.

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Amir Hossein Payberah

• All members of a group are responsible for the group's work.
• In any assessment, every student shall honestly disclose any help received and sources used.
• In an oral assessment, every student shall be able to present and answer questions about the entire assignment and solution.

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