Data science is characterized by engaging heterogeneous data to tackle real world questions and problems. But data science has no data of its own and must seek it within real world domains. We call this search for data "prospecting" and argue that the dynamics of prospecting are pervasive in, even characteristic of, data science. Prospecting aims to render the data, knowledge, expertise, and practices of worldly domains available and tractable to data science method and epistemology. Prospecting precedes data synthesis, analysis, or visualization, and is constituted by the upstream work of discovering disordered or inaccessible data resources, thereafter to be ordered and rendered available for computation. Through this work, data science positions itself in the middle of all things-capable of engaging this, that, or any domain-and thus prospecting is a key driver of data science's ongoing formation as a universal(izing) science.

Artificial intelligence (AI), including machine learning (ML), is widely viewed as having substantial transformative potential across society, and novel implementations of these technologies promise new modes of living, working, and community engagement. Data and the algorithms that operate upon it thus operate under an expansive ethical valence, bearing consequence to both the development of these potentially transformative technologies and our understanding of how best to manage and support its impact. This paper reports upon an interview‐driven study of stakeholders engaged with technology development, policy, and law relating to AI. Among our participating stakeholders, unexpected outcomes and flawed implementations of AI, especially those leading to negative social consequences, are often attributed to ill‐structured, incomplete, or biased data, and the algorithms and interpretations that might produce negative social consequence are seen as neutrally representing the data, or otherwise blameless in that consequence. We propose a more complex infrastructural view of the tools, data, and operation of AI systems as necessary to the production of social good, and explore how representations of the successes and failures of these systems, even among experts, tend to valorize algorithmic analysis and locate fault at the quality of the data rather than the implementation of systems.

While data science is vociferous about it value, it is oddly silent about its values.Algorithmically-driven analysis of very large data sets, often falling under broader categories of machine learning (ML) and artificial intelligence (AI) has already become an integral part of our social fabric. We continually monitor ourselves, each other, and the environment in an increasingly data-intensive manner, and policy decisions are more and more often being made on the basis of the results of opaque, occulted (Slota, Slaughter and Bowker, 2020) or otherwise black-boxed analysis of very large, heterogeneous data sets. Our relationship with data is changing, both as individuals and as a society. Self-tracking and quantification provides a data-oriented mode of understanding our own behaviors, bodies, and lives. (Berson, 2015) As a society, we

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As community-oriented programs move from intervention to infrastructure, questions of just and equitable access to that infrastructure both arise and become more consequential to those served. However, extant tools are general in scope, often undertested, and inconsistently linked with positive outcomes for served communities and service providers. We explore the dynamics and implications of a key tool within this infrastructure intended to enable portable collaboration across organizations serving those who are experiencing homelessness: the VI-SPDAT (Vulnerability Index -Service Prioritization Decision Assistance Tool). This tool, while providing a means of coordinated assessment, must itself be negotiated according to the values, data concerns, and goals of the agencies and service providers who make use of it. This paper reports findings from 29 interviews with individuals working in nonprofits, charities, and government agencies that provide services or resources to people experiencing homelessness within the City of Austin's Continuum of Care. The life-and-death stakes of the VI-SPDAT, which is designed to prioritize access to services based in part on a prediction of potential for premature mortality, drive home the need for equitable and just infrastructure.