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Session 1: Data Feminism (Chapters 1 - 5)
Main text: Data Feminism (open review version) (Intro & chapter 1 - 5 only), Catherine D'Ignazio & Lauren Klein.
A fully open-access version of this text will soon be available. In th meantime, attendees of the event can be sent a link to a digital version of th text upon request (please email one of the course leaders). Please do not share this digital copy of the book outside of the group.
If you cannot obtain a copy of Data Feminism, or wish to continue your reading outside of the core material, below is a selection of supplementary reading material.
Journal article:
- "Good" isn't good enough, Ben Green, NeurIPS Joint Workshop on AI for Social Good, 2019 (15 minute read)
- Feminist Data Visualization, Catherine D'Ignazio & Lauren Klein, published in the proceedings from the Workshop on Visualization for the Digital Humanities at IEEE VIS Conference, 2016 (15 minute read)
Quick read:
- Catherine D'Ignazio: 'Data is never a raw, truthful input – and it is never neutral', Zoe Corbyn for The Guardian 2020 (5 minute read)
- Putting Data Back Into Context, Catherine D'Ignazio, DataJournalism.com (10 minute read)
- “Having Trouble Explaining Oppression? This Comic Can Do It for You Robot Hugs, 2017 (3 minute read)
- The Matrix of Domination and the Four Domains of Power, Black Feminisms, 2019 (5 minute read)
Short watch:
- Challenging Power in Data Science, The Alan Turing Institute, 2020, (1hr 3 mins watch, but shorter on 1.5x playback 🙀)
- Gender Shades Joy Buolamwini, Timnit Gebru et. al at the MIT Media Lab, 2018, (5 minute watch), (see also AI, Ain't I a Woman? as well as Joy's TED talk on TED Radio Hour from npr)
Visual story:
- U.S Gun Deaths, Periscopic
- Mimi Onuoha’s Library of Missing Datasets (github repo)
- Hidden Figures: The American Dream and the Untold Story of the Black Women Who Helped Win the Space Race, Margot Lee Shetterly, 2016
- When proof is not enough, Mimi Onuoah, 2020
- Fairness and Accountability Design Needs for Algorithmic Support in High-Stakes Public Sector Decision-Making Veale et al., 2018
There is so much, too much to put here - we are thinking of creating a more comprehensive resource if anyone is interested, but a good start is all the references in Data Feminism!
General questions might be:
- What did you think about what you read?
- What was your favourite idea/passage in the piece?
- What did you like least?
- Was there anything that shocked or surprised you?
- Did this change your perception of data science? Does this change public perception of data science?
- What do you think the author was trying to achieve with the book/article?
- What were the most important points/topics covered?
- Was there anything you disagreed with, or that struck you as controversial?
- Pick out a quote from the material/book you found particularly interesting and be prepared to explain why
- More ideas here
A few ideas of specific points of discussion, based on the material:
- What does data science mean to you? Does the author's description of data science differ (see below) from your understanding?
“Many people think of data as numbers alone, but data can also consist of words or stories, colors or sounds, or any type of information that is systematically collected, organized, and analyzed (sic). The science in data science simply implies a commitment to systematic methods of observation and experiment.” “...a capacious definition of data science, one that seeks to include rather than exclude and does not erect barriers based on formal credentials, professional affiliation, size of data, complexity of technical methods, or other external markers of expertise.”
- Is data is the new oil a useful concept?
- Do we feel this book provides “concrete steps to action for data scientists seeking to learn how feminism can help them work toward justice”?
- As civil servants can we move from data ethics to data justice? and if so how? How can we challenge power as data scientists?
- What have been our missed opportunities to work with vulnerable/marginalised groups at work?
- What does data feminism look like during a pandemic? (e.g. the need for gender/race/ethnicity disaggregated data, effect of occupational differences (to health and economic stability), caregiving, Data for black lives crowd-sourcing getting data on race and ethnicity in the US
| Concepts That Secure Power Because they locate the source of the problem in individuals or technical systems | Concepts That Challenge Power Because they acknowledge structural power differentials and work toward dismantling them |
|---|---|
| Ethics | Justice |
| Bias | Oppression |
| Fairness | Equity |
| Accountability | Co-liberation |
| Transparency | Reflexivity |
| Understanding algorithms | Understanding history, culture and context |
We welcome all feedback on all aspects of the event- please do so as soon as you are able.
We are aware that we could improve the accessibility of this material, and welcome all suggestions and help to do so.