31. Data De-Invisibilization: Visualizing the Dark Matter of Networks
Matthew Battles Battles, The Arnold Arboretum of Harvard University; Catherine D'Ignazio, MIT; Kim Albrecht, metaLAB at Harvard/Berkman Center, FU Berlin; Nicole Martin, Indigenous Women Rising
Posted: February 28, 2022 Accepted Languages: English/Inglés/Inglês, Spanish/Español/Espanhol
This panel invites projects at the intersection of data visualization and power. This might mean projects predicated on a politic of visibility, such as feminicide data activists using maps and graphics as “affect amplifiers” for gender violence (Suárez Val 2022), or work that challenges visual metaphors as hegemonic (Haraway 1989). Data visualization helps construct the neoliberal consensus through which data are conceived, operationalized, and consumed. Many of us inherit Enlightenment sensibilities that privilege vision and ignore relation. How do we acknowledge effacements, silences, and invisibilities across network and data imaginaries, and render data in terms of reunion, recuperation, and reconfiguration?
Under such conditions, it’s crucial to acknowledge that “all data are local” (Loukissas 2019), that gender binaries contort data in ways that lead to injustice and invisibility (D’Ignazio and Klein 2020), and that Indigenous, queer, feminist and Black communities are developing counter-hegemonic uses of data grounded in care and relation (Carroll, Rodriguez-Lonebear, and Martinez 2019).
Invited investigators are asked to contribute either 1) Empirically, for example, with a case study of counter-hegemonic visualizations; or 2) Speculatively and creatively, with projects that expand possibilities for addressing visualization and power.
Projects in either category may engage technological metaphors in relation to living communities; make use of critical design practices to submit normative forms of knowledge to scrutiny; strive to render hidden systems and relations visible, while honoring the silences and invisibilities that structure networked social life; and recognize systems in their raw states, tracing the transformations taking place as data transit materialities.
