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.

Research on work and occupations in the information field have largely focused on white‐collar jobs. Little is known about the information and technology experiences and behaviors of workers in blue‐collar jobs. This study examines the user experiences of current welding tools and welding training and asks how integration of information feedback through smart technology in welding tools can help welders do their jobs safer, easier, and faster. We conducted 14 in‐depth interviews with members of Longhorn Racing, a student organization that designs, builds, and tests race cars. Participants experienced frustrations with the dangerous and technical setup and the limited vision and information feedback from welding tools. Many argued that the integration of smart technology into welding tools could improve their experience. These innovations could lead to faster training and reduced attrition in the welding industry. Further, this research points to the urgent need for more research on blue‐collar workers in the information field.

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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.

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