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 use of data, the primary goal of Data Feminism is to identify 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 advances 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 based on the seven principles outlined in the book *Data Feminism* by Catherine D'Ignazio and Lauren F. Klein.
The Course Examiner
The Course Structure
The course contains seven modules, each dedicated to one of the outlined objectives. Within each module, students will participate in two sessions: one lecture and one discussion. In the lecture session, the instructor will introduce the module's topic and provide an overview of the assigned reading materials. Students will then have one week to review the readings and submit a detailed critique of the selected papers. The discussion session will be dedicated to a deeper review and exploration of the module's topic and associated readings.
Intended Learning Outcome (ILO)
After the course, the student should be able to:
ILO1: understand the theoretical 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 be familiar with machine learning models and have experience with Python or a similar programming language.
Assessment
Grading in this course will be based on three distinct tasks: completion of module reading assignments, group discussion, 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.
Reading Assignments 1 (deadline: Sep. 25)
Reading Assignments 2 (deadline: Oct. 9)
Reading Assignments 3 (deadline: Oct. 23)
Reading Assignments 4 (deadline: Nov. 13)
Reading Assignments 5 (deadline: Nov. 27)
Reading Assignments 6 (deadline: Dec. 11)
Task 2 (group discussion): each student/group will act as the moderator for a pre-selected module. In this role, they will be responsible for presenting the assigned papers and contributing to the collective understanding of the module’s content. All other students are expected to attend the presentation sessions and actively participate in the subsequent group discussions.
Task 3 (final project): for the final project, students will apply the principles of data feminism to a topic within their own research area or professional field. Each student will propose their own topic and choose either (1) to develop a small practical project, such as an application, prototype, data analysis, visualization, audit, or redesign, or (2) to write a short analytical essay. Regardless of the format, the project should engage meaningfully with concepts from the course, such as power, intersectionality, context, classification, missing perspectives, and invisible labour. Students will first submit a short project proposal for approval and, at the end of the course, present their work and reflect on how data feminism influenced their understanding of the problem, their methodological or design choices, and the limitations of their work.
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
Module 1: Critiquing Power in Data Science
Lecture Session: Sep. 11, 13:00-15:00
Discussion Session: Sep. 18, 13:00-15:00
Required Reading
- Data Feminism, C. D'Ignazio and L. F. Klein (intro, ch. 1-2)
- Black Feminist Thought, P. H. Collins (ch. 12)
- Demarginalizing the Intersection of Race and Sex, K. Crenshaw
- Feminism is For Everybody, b. hooks (ch. 1)
- Combahee River Collective Statement [link]
Optional Reading
- Feminist Theory: From Margin to Center, b. hooks (ch. 1)
- Feminism for the 99%: A Manifesto, N. Fraser (thesis 1-10)
- Feminist Theory: From Margin to Center, b. hooks (ch. 1)
- Algorithms of Oppression, Safiya U. Noble (ch. 1)
- Race after Technology, R. Benjamin (intro)
- Automating Inequality, V. Eubanks (ch. 4)
- Restorative Justice and Reparations, M. U. Walker
- Intersectionality, P. H. Collins
- Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification, J. Buolamwini et al., 2018
- Towards Algorithmic Luddism: Class Politics in Data Capitalism, V. Charitsis et al.
- Mapping the Margins: Intersectionality, Identity Politics, and Violence against Women of Color, K. Crenshaw
Module 2: Ghost Work
Lecture Session: Sep. 25, 13:00-15:00
Discussion Session: Oct. 2, 13:00-15:00
Required Reading
- Data Feminism, C. D'Ignazio and L. F. Klein (ch. 7)
- Methodological Considerations for Centering Workers' Epistemic Authority in AI Research, M. Miceli et al., 2025
- The Data-Production Dispositif, M. Miceli et al., 2022
- The Exploited Labor Behind Artificial Intelligence, A. Williams et al. [link]
- Big Tech Sets Unfair Terms and Conditions for AI Data Workers Globally, M. Silva [link]
- A Typology of Artificial Intelligence Data Work, J. Muldoon et al., 2024
Optional Reading
- Towards Ethical Outsourcing of Data Work in Academia, T. Yang et al., 2026
- Where does AI Come From? A Global Case Study Across Europe, Africa, and Latin America, P. Tubaro et al., 2025
- Friction and Promise in Data Labor, M. Ruckenstein et al., 2026
- Ghost Work, M. L. Gray and S. Suri (ch. 1)
- Atlas of AI, K. Crawford (ch. 2)
- Wages Against Housework, S. Federici [link]
- Making Feminist Points, S. Ahmed [link]
- Justice for Data Janitors, L. Irani [link]
- Digital Labour Markets in the Platform Economy, F. Schmidt, 2017
- Digital Labour Platforms and the Future of Work, J. Berg et al., 2018
- Ethical Norms and Issues in Crowdsourcing Practices: A Habermasian Analysis, D. Schlagwein et al., 2019
- Difference and Dependence Among Digital Workers: The Case of Amazon Mechanical Turk, L. Irani, 2015
- Platformization of Inequality: Gender and Race in Digital Labor Platforms, I. Munoz et al., 2024
- The Cultural Work of Microwork, L. Irani, 2015
- Whose Truth? Power, Labor, and the Production of Ground-Truth Data, M. Miceli, 2023
- Turkopticon: Interrupting Worker Invisibility in Amazon Mechanical Turk, L. Irani et al., 2013
- We are Dynamo: Overcoming Stalling and Friction in Collective Action for Crowd Workers, N. Salehi et al., 2015
Module 3: Data Colonialism
Lecture Session: Oct. 9, 13:00-15:00
Discussion Session: Oct. 16, 13:00-15:00
Required Reading
- Data Feminism, C. D'Ignazio and L. F. Klein (ch. 5-6)
- Datasheets for Datasets, T. Gebru et al., 2021
- Documenting Data Production Processes: A Participatory Approach for Data Work, M. Miceli et al., 2022
- Lessons From the Margins: Contextualizing, Reimagining, and Hacking Generative AI in the Global South, A. D. Hernández et al., 2025
- Situated Knowledges: The Science Question in Feminism and the Privilege of Partial Perspective, D. Haraway, 2013
- Data Colonialism: Rethinking Big Data's Relation to the Contemporary Subject, N. Couldry and U.A. Mejias, 2019
- Against Cleaning, Katie Rawson and Trevor Muñoz, Debates in the Digital Humanities, 2019
Optional Reading
- The TESCREAL Bundle, T. Gebru and É. Torres., 2024
- Ghost Stories for Darwin, B. Subramaniam (intro)
- Data Grab, U.A. Mejias and N. Couldry (ch. 1, ch. 6)
- All Data Are Local: Thinking Critically in a Data-Driven Society, Y. Loukissas (intro)
- Indigenous Statistics: A Quantitative Research Methodology, M. Walter et al. (intro)
- The Dataset Nutrition Label, S. Holland et al., 2020
- The Dataset Nutrition Label (2nd Gen), K. S. Chmielinski et al., 2022
- Data Biographies: Getting to Know Your Data, H. Krause [link]
- Data User Guides, B. Gradeck [link]
- The Anti-Eviction Mapping Project: Counter Mapping and Oral History Toward Bay Area Housing Justice, M. Maharawal et al., 2018
- The Subjects and Stages of AI Dataset Development: A Framework for Dataset Accountability, M. Khan et al., 2023
Module 4: Bias and Fairness in Data
Lecture Session: Oct. 23, 13:00-15:00
Discussion Session: Nov. 6, 13:00-15:00
Required Reading
- A Framework for Understanding Sources of Harm Throughout the Machine Learning Life Cycle, H. Suresh et al., 2021
- Assessing and Remedying Coverage for a Given Dataset, A. Asudeh et al., 2019.
- Representation Bias in Data: A Survey on Identification and Resolution Techniques, N. Shahbazi et al., 2023
- Bias Mitigation for Machine Learning Classifiers: A Comprehensive Survey, M. Hort et al., 2024
- Machine bias, J. Angwin et al., ProPublica, 2016 [link]
- Gender Bias and Stereotypes in Large Language Models, H. Kotek et al., 2023
- Cultural Bias and Cultural Alignment of Large Language Models, Y. Tao et al., 2024
Optional Reading
- Fairness in the AI Lifecycle [link]
- The Accuracy, Fairness, and Limits of Predicting Recidivism, J. Dressel et al., 2018
- Tackling Documentation Debt: A Survey on Algorithmic Fairness Datasets, A. Fabris et al., 2022
- Word Embeddings Quantify 100 Years of Gender and Ethnic Stereotypes, N. Garg et al., 2018
- No Classification Without Representation, S. Shankar et al., 2017
- AI Fairness 360: An Extensible Toolkit for Detecting, Understanding, and Mitigating Unwanted Algorithmic Bias, 2018
- Fairlearn: A Toolkit for Assessing and Improving Fairness in AI, S. Bird et al., 2020.
- Artificial Intelligence and Inclusion: Formerly Gang-Involved Youth as Domain Experts for Analyzing Unstructured Twitter Data, W. R. Frey et al., 2020
Module 5: Bias and Fairness in Models
Lecture Session: Nov. 13, 13:00-15:00
Discussion Session: Nov. 20, 14:00-16:00
Required Reading
- Data Feminism, C. D'Ignazio and L. F. Klein (ch. 4)
- Fairness and Machine Learning, S. Barocas et al. (ch. 3)
- The Ethical Algorithm, Michael Kearns, A. Roth (ch. 2)
- Fairness in Machine Learning: A Survey, S. Caton et al., 2024
- Fairness Definitions Explained, S. Verma et al., 2018
- A Short-Term Intervention for Long-Term Fairness in the Labor Market, L. Hu et al., 2018
- On the Apparent Conflict Between Individual and Group Fairness, R. Binns, 2020
- Fairness in Machine Learning: Lessons from Political Philosophy, R. Binns, 2018
Optional Reading
- Atlas of AI, K. Crawford (ch. 4)
- Bias and Unfairness in Machine Learning Models, T. P. Pagano et al., 2023
- Sorting Things Out, G. C. Bowker and S. Leigh Star (intro)
- Practical Fairness, Aileen Nielsen (ch. 5-6)
- Fairness Through Awareness, C. Dwork et al., 2012
- Bias Mitigation for Machine Learning Classifiers: A Comprehensive Survey, M. Hort et al., 2024
- Fairness Metrics: A Comparative Analysis, P. Garg et al., 2020
- A Survey on Bias and Fairness in Machine Learning, N. Mehrabi et al., 2021
- A Unified Approach to Quantifying Algorithmic Unfairness: Measuring Individual and Group Unfairness Via Inequality Indices, T. Speicher et al., 2018
Module 6: Data Feminism in Action
Lecture Session: Nov. 27, 13:00-15:00
Discussion Session: Dec. 4, 13:00-15:00
Required Reading
- Doing the Feminist Work in AI: Reflections from an AI Project in Latin America, M. C. Felice et al., 2025
- Towards Intersectional Feminist and Participatory ML: A Case Study in Supporting Feminicide Counterdata Collection, H. Suresh et al., 2022
- Reimagining Data Work: Participatory Annotation Workshops as Feminist Practice, Y. Gao et al., 2026
- Sustainable AI Meets Feminist African Ethics, M. Mensah et al., 2025
- Data Against Feminicide: The Process and Impact of Co-designing Digital Research Tools, H. V. Suárezm, 2024
- Lessons from the Margins: Contextualizing, Reimagining, and Hacking Generative AI in the Global South, A. Hernández et al., 2025
Optional Reading
- Participatory AI Considerations for Advancing Racial Health Equity, A. G. Parker et al., 2025
- Counting Feminicide: Data Feminism in Action, C. D'Ignazio, 2024
- A Data Feminist Approach to Urban Data Practice: Tenant Power Through Eviction Data, M. E. Hatch et al., 2025
- Data Solidarity in Feminist Technology Activism and Innovation, A. Richterich, 2025
- Are "Intersectionally Fair" AI Algorithms Really Fair to Women of Color? A Philosophical Analysis, Y. Kong, 2022
- (Un)Fairness in AI: An Intersectional Feminist Analysis, Y. Kong [link]
Module 7: Emotion and Embodiment
Lecture Session: Dec. 11, 13:00-15:00
Discussion Session: Dec. 18, 13:00-15:00
Required Reading
- Data Feminism, C. D'Ignazio and L. F. Klein (ch. 3)
- Visualization Rhetoric: Framing Effects in Narrative Visualization, J. Hullman et al., 2011
- Entanglements for Visualization: Changing Research Outcomes through Feminist Theory, D. Akbaba et al., 2024
- Data Hunches: Incorporating Personal Knowledge into Visualizations, H. Lin et al., 2022
- Feminist Data Visualization, C. D'Ignazio and L. F. Klein, 2016
- Feminist HCI: Taking Stock and Outlining an Agenda for Design, S. Bardzell, 2010
Optional Reading
- Design Justice, S. Costanza-Chock
- Dear Data, S. Posavec and G. Lupi
- The Work That Visualisation Conventions Do, H. Kennedy et al., 2016
- Iceberg Sensemaking: A Process Model for Critical Data Analysis, C. Berret et al., 2024
- Emotional Data Visualization: Periscopic's "U.S. Gun Deaths" and the Challenge of Uncertainty [link]
- Discursive Patinas: Anchoring Discussions in Data Visualizations, T. Kauer et al., 2024
- The Power of Absence: Thinking with Archival Theory in Algorithmic Design, J. Sherman et al., 2024
- Disclosure as a Critical-Feminist Design Practice for Web-based Data Stories, H. Schwan et al., 2022