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Suggested further readings

• Cifor, M., Garcia, P., Cowan, T. L., Rault, J., Sutherland, T., Chan, A., Rode, J., Hoffmann, A. L., Salehi, N., & Nakamura, L. (2019). Feminist Data Manifest-No. https://www.manifestno.com.
• Heeks, R., & Shekhar, S. (2019). Datafication, development and marginalised urban communities: An applied data justice framework. Information, Communication & Society, 22(7).https://www.tandfonline.com/doi/full/10.1080/1369118X.2019.1599039
• Pierre, J., Crooks, R., Currie, M., Paris, B., & Pasquetto, I. (2021). Getting Ourselves Together: Epistemic Burden and Data-centered Participatory Design. Proceedings of the annual Conference on Human Factors in Computing (CHI).
• Benjamin, R. (2019). Race After Technology: Abolitionist Tools for the New Jim Code. John Wiley & Sons.
• Bui, M., & Noble, S. U. (2020). We’re Missing a Moral Framework of Justice in Artificial Intelligence: On the Limits, Failings, and Ethics of Fairness. In M. D. Dubber, F. Pasquale, & S. Das (Eds.), The Oxford Handbook of Ethics of AI (pp. 161–179). Oxford University Press. https://doi.org/10.1093/oxfordhb/9780190067397.013.9
• D’Ignazio, C., & Klein, L. F. (2020). Data Feminism. MIT Press.
• Dourish, P., & Gómez Cruz, E. (2018). Datafication and data fiction: Narrating data and narrating with data. Big Data & Society, 5(2), 2053951718784083. https://doi.org/10.1177/2053951718784083
• Gangadharan, S. P., & Niklas, J. (2019). Decentering technology in discourse on discrimination. Information, Communication & Society, 22(7), 882–899. https://doi.org/10.1080/1369118X.2019.1593484
• Milan, S., & Treré, E. (2019). Big Data from the South(s): Beyond Data Universalism. Television & New Media, 20(4), 319–335. https://doi.org/10.1177/1527476419837739

Event information

Our exhibition Bending Lines: Maps and Data from Distortion to Deception examines how visual representations of the world can shape what people believe. But sometimes biases and distortions are built into the data that is used to produce a map. Far from offering a perfectly objective, all-encompassing view of the world, data sets of all kinds are deeply shaped by human choices.

In this conversation series, we talk with experts about why we should be careful about geographic information in modern data. How is data collected, and how does it get fixed into categories and numbers? Who gets to own data sets, and who gets to make decisions using them? What sorts of public responsibilities should shape the social lives of data?

These talks are free, designed for general public audiences with time for questions. Talks will be broadcast over the LMEC’s YouTube Live and Facebook Live channels.

Matt Bui is an assistant professor and faculty fellow at the NYU Alliance for Public Interest Technology who examines the intersections of digital, data, and racial justice in everyday life.

Bending Lines was made possible in part by a grant from the Institute of Museum and Library Services.