Session 3: Data Feminism (Chapters 6+)
Main text: Data Feminism (open review version) (Chapter 6 onwards), Catherine D'Ignazio & Lauren Klein.
A fully open-access version of this text will soon be available. In the meantime, attendees of the event can be sent a link to a digital version of the 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.
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)
Journal articles:
- Ford, Heather & Wajcman, Judy. (2017). ‘Anyone can edit’, not everyone does: Wikipedia's infrastructure and the gender gap. Social Studies of Science. 47. 10.1177/0306312717692172. (25 minute read)
- Critical Race & Digital Studies Syllabus, Center For Critical Race & Digital Studies
- Kate Crawford and Vladan Joler, “Anatomy of an AI System: The Amazon Echo As An Anatomical Map of Human Labor, Data and Planetary Resources,” AI Now Institute and Share Lab, (September 7, 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:
- Do you agree with the authors' 'expanded' 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."
- Do you think we do enough as civil servants, to think critically about the data we use, and how we use it?
- Are data science practitioners in the civil service adequately informed and engaged in critical reflection about the nature of their practice?
- Can an 'engaged' and thoughtful data science practitioner in government actually make change?
- Should we think critically about 'public good' as civil servants?
- Is working through a lens of intersectional feminism in direct conflict with public sector 'neutrality'?
- Is there 'hidden labour' involved in data science practice in the civil service?
- Is the concept of 'technochauvinism', the belief that the technological solution to a problem is the right one, useful?
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.