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In response to growing concerns of bias, discrimination, and unfairness perpetuated by algorithmic systems, the datasets used to train and evaluate machine learning models have come under increased scrutiny. Many of these examinations have focused on the contents of machine learning datasets, finding glaring underrepresentation of minoritized groups. In contrast, relatively little work has been done to examine the norms, values, and assumptions embedded in these datasets. In this work, we conceptualize machine learning datasets as a type of informational infrastructure, and motivate a genealogy as method in examining the histories and modes of constitution at play in their creation. We present a critical history of ImageNet as an exemplar, utilizing critical discourse analysis of major texts around ImageNet’s creation and impact. We find that assumptions around ImageNet and other large computer vision datasets more generally rely on three themes: the aggregation and accumulation of more data, the computational construction of meaning, and making certain types of data labor invisible. By tracing the discourses that surround this influential benchmark, we contribute to the ongoing development of the standards and norms around data development in machine learning and artificial intelligence research.

This research shows how face masks took on discursive political significance during the early stages of the coronavirus disease 2019 pandemic in the United States. The authors argue that political divisions over masks cannot be understood by looking to partisan differences in mask-wearing behaviors alone. Instead, they show how the mask became a political symbol enrolled into patterns of affective polarization. This study relies on qualitative and computational analyses of opinion articles (n = 7,970) and supplemental analyses of Twitter data, the transcripts of major news networks, and longitudinal survey data. First, the authors show that antimask discourse was consistently marginal and that backlash against mask refusal came to prominence and did not decline even as masking behaviors normalized and partly depolarized. Second, they show that backlash against mask refusal, rather than mask refusal itself, was the primary way masks were discussed in relation to national electoral, governmental, and partisan themes.

How does stakeholder participation in natural resource management change when conservation rules are grounded in near real‐time data? Recent technological advances have increased the feasibility of the ‘dynamic management’ of natural resources, which promises to align the spatiotemporal scales of management with ecological variability and resource use. Drawing on Kelty's (2020) concept of ‘contributory autonomy’, this article offers a critical comparison of how participation is conceived of in the more established context of static conservation areas and planning versus the emergent field of dynamic management. A systematic review of the dynamic ocean management literature reveals a varied, but shallow engagement with the topic of stakeholder participation in that context. Whereas static management regimes are governed by relatively intuitive and contestable maps, dynamic management is governed by models and data flows. Overall, the decision‐making stakeholder of participatory mapping processes under static management is displaced by the stakeholder conceived as an ‘end‐user’ of a dynamic management product and consultant in its design. Yet, these shifts also open up potential points of contestation, which may pattern the future theory and practice of participation in dynamic management: counterdata, countermodelling and data chokepoints. Beyond the empirical focus on oceans, this article contributes to broader conversations about the political stakes of environmental data, and algorithmic and artificial intelligence‐driven natural resource conservation by considering how possibilities for participation are foreclosed, enabled and reconstituted by new spatiotemporal and technological conditions. Short The authors critically compare how participation is conceived in static and dynamic ocean management. Static management regimes are governed by relatively intuitive and contestable maps; dynamic management is governed by models and data flows.

[...]the objective of Thomas Nail’s paper is to unveil the formal structures and “core operations” of Deleuze and Guattari’s theoretical account of assemblages developed in A Thousand Plateaus and What is Philosophy? [...]they show that the concept of ambiguity in Merleau-Ponty is the point of articulation that qualifies both the commonality of sensation and its immanent exteriority as a pre-political experience. In their presentation, this situated political ambiguity is possible because it is framed in advance as a form of resistance grounded in the lived experience of the participants but also because it is designed, as such, by the opposition forces assembled in the square. [...]the pre-political emerges from the pre-personal assemblage of screams and noises which spark a commonality of sensation of all the participants assembled in the square; this commonality of sensation, in turn, generates an experience of ambiguity that ultimately shapes the formation of the conditions of the political event as such. [...]Daniel Barber’s contribution also sees political promise in the idea of the assemblage, but only if the assemblage is conceived of in what might be called its negative form.

[...]our concept of politics is understood here as the domain of concerted actions of a plurality of actors who articulate a lived experiential relation of power that enable a collective subject that gives form to itself via definite strategic objectives, which are particular in their nature. According to Merleau-Ponty, one perceives its bodily presence through a personal corporeal schema that appears as “the summary of our corporeal experience,” but also as a conscious realization of my posture in the intersensorial world, a ‘form’ as defined by Gestaltpsychology” (PP 129).10 The bodily schema forms the unity of the body for the following two reasons. [...]this phenomenological order of things organizes the immediate perception of meaning and value in such a way that they are necessarily dependent upon a notion of the whole irreducible to its parts and is positively understood in terms of perceptual field and figure against a background. 11. According to Merleau-Ponty, these theories fail because isolated and elementary sensible qualities/punctual impressions are: a) not de facto “objects of experience,” and b) they are not relational.

This paper introduces measurement trees, a novel class of metrics designed to combine various constructs into an interpretable multi-level representation of a measurand. Unlike conventional metrics that yield single values, vectors, surfaces, or categories, measurement trees produce a hierarchical directed graph in which each node summarizes its children through user-defined aggregation methods. In response to recent calls to expand the scope of AI system evaluation, measurement trees enhance metric transparency and facilitate the integration of heterogeneous evidence, including, e.g., agentic, business, energy-efficiency, sociotechnical, or security signals. We present definitions and examples, demonstrate practical utility through a large-scale measurement exercise, and provide accompanying open-source Python code. By operationalizing a transparent approach to measurement of complex constructs, this work offers a principled foundation for broader and more interpretable AI evaluation.