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This paper offers a reflexive analysis of an interdisciplinary and cross-race collaboration to advance equity in engineering called LATTICE (Launching Academics on the Tenure-Track: an Intentional Community in Engineering). We engage two bodies of scholarship—matters of care in feminist science and technology studies (STS) and critical race theory on counterspaces—to theorize on the data infrastructure and narrative practices that we developed when applying critical methodologies to collective action in technoscience. We discuss how our care practices conflicted with traditional ethnographic practices and thus, inspired us to innovate on methods. These methods—member-checking and polyvocal memo-ing—make transgressing the boundaries of LATTICE counterspaces for public dissemination possible by invoking caring as praxis. We conclude that using these methods to discuss the contradictions and challenges in STS collaborations is an opportunity for advancing mutual intelligibility among interdisciplinary scholars and a politics of knowledge production grounded in values of care and friendship that may contribute to equity and justice in technoscience. 

Search engines, by ranking a few links ahead of million others based on opaque rules, open themselves up to criticism of bias. Previous research has focused on measuring political bias of search engine algorithms to detect possible search engine manipulation effects on voters or unbalanced ideological representation in search results. Insofar that these concerns are related to the principle of fairness, this notion of fairness can be seen as explicitly oriented toward election candidates or political processes and only implicitly oriented toward the public at large. Thus, we ask the following research question: how should an auditing framework that is explicitly centered on the principle of ensuring and maximizing fairness for the public (i.e., voters) operate? To answer this question, we qualitatively explore four datasets about elections and politics in the United States: 1) a survey of eligible U.S. voters about their information needs ahead of the 2018 U.S. elections, 2) a dataset of biased political phrases used in a large-scale Google audit ahead of the 2018 U.S. election, 3) Google’s “related searches” phrases for two groups of political candidates in the 2018 U.S. election (one group is composed entirely of women), and 4) autocomplete suggestions and result pages for a set of searches on the day of a statewide election in the U.S. state of Virginia in 2019. We find that voters have much broader information needs than the search engine audit literature has accounted for in the past, and that relying on political science theories of voter modeling provides a good starting point for informing the design of voter-centered audits.