Here is a list of resources about integrating data science and ethnography. Even though it is an up and coming field without a consistent list of publications, several fascinating and insightful resources do exist.
If there are any resources about integrating data science and ethnography that you have found useful, feel free to share them as well.
General Overviews:
- Curran, John. “Big Data or ‘Big Ethnographic Data’? Positioning Big Data within the Ethnographic Space.” EPIC (2013). (Found here: https://www.epicpeople.org/big-data-or-big-ethnographic-data-positioning-big-data-within-the-ethnographic-space/)
- Patel, Neal. “For a Ruthless Criticism of Everything Existing: Rebellion Against the Quantitative-Qualitative Divide.” EPIC (2013): 43-60.
- Nick Seaver. “Bastard Algebra.” Boellstorff, Tom and Bill Maurer. Data, Now Bigger and Better. Chicago: Prickly Paradigm Press, 2015. 27-46.
- Slobin, Adrian and Todd Cherkasky. “Ethnography in the Age of Analytics.” EPIC (2010).
- Nafus, Dawn and Tye Rattenbury. Data Science and Ethnography: What’s Our Common Ground, and Why Does It Matter? 7 3 2018. <https://www.epicpeople.org/data-science-and-ethnography/>.
- Nick Seaver. “The nice thing about context is that everyone has it.” Media, Culture & Society (2015).
Books:
- Nafus, Dawn and Hannah Knox. Ethnography for a Data-Saturated World. Manchester: Manchester Univeristy Press, 2018.
- Boellstorff, Tom and Bill Maurer. Data, Now Bigger and Better! Chicago: Prickly Paradigm Press, 2015.
- Mackenzie, Adrian. Machine Learners: Archaeology of a Data Practice. Cambridge: The MIT Press, 2017.
Examples and Case Studies:
- “Autonomous Drive: Teaching Cars Human Behaviour” by Melissa Cefkin on the Youtube Channel DrivingTheNation: https://www.youtube.com/watch?v=6koKuDegHAM
- Eslami, Motahhare, et al. “First I “like” it, then I hide it: Folk Theories of Social Feeds.” Curation and Algorithms (2016).
- Giaccardi, Elisa, Chris Speed and Neil Rubens. “Things Making Things: An Ethnography of the Impossible.” (2014).
- Elish, M. “The Stakes of Uncertainty: Developing and Integrating Machine Learning in Clinical Care.” EPIC (2018).
- Madsen, Matte My, Anders Blok and Morten Axel Pedersen. “Transversal collaboration: an ethnography in/of computational social science.” Nafus, Dawn. Ethnography for a Data-saturated World. Manchester: Manchester Univeristy Press, 2018.
- Thomas, Suzanne, Dawn Nafus and Jamie Sherman. “Algorithms as fetish: Faith and possibility in algorithmic work.” Big Data & Society (2018): 1-11.
Articles and Blog Posts:
- “An Engineering Anthropologist: Why tech companies need to hire software developers with ethnographic skills” by Astrid Countee: http://ethnographymatters.net/blog/2016/06/22/an-engineering-anthropologist-why-tech-companies-need-to-hire-software-developers-with-ethnographic-skills/
- “Cross-disciplinary Insights Teams: Integrating Data Scientists and User Researchers at Spotify” by Sara Belt and Peter Gilks: https://www.epicpeople.org/cross-disciplinary-insights-teams-integrating-data-scientists-and-user-researchers-at-spotify/
- “Data is a stakeholder” by Schaun Wheeler: https://towardsdatascience.com/data-is-a-stakeholder-31bfdb650af0
- “Why Big Data Needs Thick Data” by Tricia Wang: https://medium.com/ethnography-matters/why-big-data-needs-thick-data-b4b3e75e3d7
My Own Articles on This Website:
- https://ethno-data.com/integrating-ethnography-and-data-science/
- https://ethno-data.com/why-business-anthropologists-should-reconsider-machine-learning/
- https://ethno-data.com/projects-integrating-data-science-and-ethnography/
- https://ethno-data.com/anthropology-by-data-science/
- https://ethno-data.com/integrating-ethnography-and-data-science/
Podcasts and Lectures:
- “Computational Anthropology: Quali-quantitative Analyses of Attention Economies during the Covid-19 Lockdown” by Morten Axel Pedersen: https://www.material.city/recordings/mortenaxelpedersen
- “Human-Driven Machine Learning with Saleema Amershi”: https://datastori.es/115-human-driven-machine-learning-with-saleema-amershi/#t=29:00.204
- “Welcome to Dataworld, by Alexander Taylor”: https://player.fm/series/camthropod/episode-13-welcome-to-dataworld-by-alex-taylor
- “Machine Learning for Artists with Gene Kogan”: https://datastori.es/114-machine-learning-for-artists-with-gene-kogan/#t=34:28.738
Ethical Considerations:
- “Caroline Sinders on Ethical Product Design for Machine Learning”: https://design.blog/2017/03/23/caroline-sinders-on-ethical-product-design-for-machine-learning/
- “The Trouble with Bias” by Kate Crawford: https://www.youtube.com/watch?v=fMym_BKWQzk
- “Justice for ‘Data Janitors’” by Lilly Irani: http://www.publicbooks.org/justice-for-data-janitors/
- Elish, Madeleine. “Moral Crumple Zones: Cautionary Tales in Human-Robot Interaction.” Engaging Science, Technology, and Society (2019).
- boyd, danah and Kate Crawford. “Critical Questions for Big Data: Provocations for a Cultural, Technological, and Scholarly Phenomenon.” Information, Communication, & Society (2012): 662-679.