In this paper, we describe how critical data designers have created projects that 'push back' against the eclipse of environmental problems by dominant orders: the pioneering pollution database Scorecard, released by the US NGO Environmental Defense Fund in 1997; the US Environmental Protection Agency's EnviroAtlas that brings together numerous data sets and provides tools for valuing ecosystem services; and the Houston Clean Air Network's maps of real-time ozone levels in Houston. Drawing on ethnographic observations and interviews, we analyse how critical data designers turn scientific data and findings into claims and visualisations that are meaningful in contemporary political terms. The skills of critical data designers cross scales and domains; they must identify problems calling for public consideration, and then locate, access, link, and create visualisations of data relevant to the problem. We conclude by describing hazards ahead in work to leverage Big Data to understand and address environmental problems. Critical data designers need to understand what counts as a societal problem in a particular context, what doesn't, what is seen as connected and not, what is seen as ethically charged, and what is exonerated and discounted. Such recognition is produced through interpretive, 'close reading' of the historical moment in which they operate.

All datasets emerge from and are enmeshed in power-laden semiotic systems. While emerging data ethics curriculum is supporting data science students in identifying data biases and their consequences, critical attention to the cultural histories and vested interests animating data semantics is needed to elucidate the assumptions and political commitments on which data rest, along with the externalities they produce. In this article, I introduce three modes of reading that can be engaged when studying datasets—a denotative reading (extrapolating the literal meaning of values in a dataset), a connotative reading (tracing the socio-political provenance of data semantics), and a deconstructive reading (seeking what gets Othered through data semantics and structure). I then outline how I have taught students to engage these methods when analyzing three datasets in Data and Society—a course designed to cultivate student competency in politically aware data analysis and interpretation. I show how combined, the reading strategies prompt students to grapple with the double binds of perceiving contemporary problems through systems of representation that are always situated, incomplete, and inflected with diverse politics. While I introduce these methods in the context of teaching, I argue that the methods are integral to any data practice in the conclusion.

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Diverse disciplinary communities approach design with diverse design logics design directives informed by critical theoretical commitments that are to be translated into material form. Recounting the design of a digital humanities platform, this paper shows how design logics of existing digital infrastructure can at times be out of sync with those of a design community seeking to leverage it. I argue that, in such situations, a designer should do more than simply “make do” with available infrastructure; the designer should instead design deviously – leveraging infrastructure in ways that undercut its logics. This suggests that reflective design involves reflecting, not only on design practice, but also on the logics of the infrastructure available to designers.