Situating Datasets: Making Public Eviction Data Actionable for Housing Justice

要旨

Activists, governments, and academics regularly advocate for more open data. But how is data made open, and for whom is it made useful and usable? In this paper, we investigate and describe the work of making eviction data open to tenant organizers. We do this through an ethnographic description of ongoing work with a local housing activist organization. This work combines observation, direct participation in data work, and creating media artifacts, specifically digital maps. Our interpretation is grounded in D’Ignazio and Klein’s Data Feminism, emphasizing standpoint theory. Through our analysis and discussion, we highlight how shifting positionalities from data intermediaries to data accomplices affects the design of data sets and maps. We provide HCI scholars with three design implications when situating data for grassroots organizers: becoming a domain beginner, striving for data actionability, and evaluating our design artifacts by the social relations they sustain rather than just their technical efficacy.

著者
Anh-Ton Tran
Georgia Institute of Technology, Atlanta, Georgia, United States
Grace Guo
Georgia Institute of Technology, Atlanta, Georgia, United States
Jordan Taylor
Carnegie Mellon University, Pittsburgh, Pennsylvania, United States
Katsuki Andrew. Chan
Georgia Institute of Technology, Atlanta, Georgia, United States
Elora Lee. Raymond
Georgia Institute of Technology, Atlanta, Georgia, United States
Carl DiSalvo
Georgia Institute of Technology, Atlanta, Georgia, United States
論文URL

https://doi.org/10.1145/3613904.3642452

動画

会議: CHI 2024

The ACM CHI Conference on Human Factors in Computing Systems (https://chi2024.acm.org/)

セッション: Politics of Datasets

316C
5 件の発表
2024-05-14 18:00:00
2024-05-14 19:20:00