The rapid development of machine-learning algorithms, which underpin contemporary artificial intelligence systems, has created new opportunities for the automation of work processes and management functions. While algorithmic management has been observed primarily within the platform-mediated gig economy, its transformative reach and consequences are also spreading to more standard work settings. Exploring algorithmic management as a sociotechnical concept, which reflects both technological infrastructures and organizational choices, we discuss how algorithmic management may influence existing power and social structures within organizations. We identify three key issues. First, we explore how algorithmic management shapes pre-existing power dynamics between workers and managers. Second, we discuss how algorithmic management demands new roles and competencies while also fostering oppositional attitudes toward algorithms. Third, we explain how algorithmic management impacts knowledge and information exchange within an organization, unpacking the concept of opacity on both a technical and organizational level. We conclude by situating this piece in broader discussions on the future of work, accountability, and identifying future research steps.

This paper analyzes interviews with individuals discussing their experiences of searching for and watching DIY videos on YouTube. By exploring the entanglement of individuals’ search practices and the algorithmic underpinnings of the platform, this paper examines how experiences on Web 2.0 platforms can work to narrow, rather than widen, information worlds. Contributing to ongoing conversations in critical algorithm studies, this paper illustrates how even mundane practices like watching home repair videos on YouTube can play a role in identity-making and the shaping of modern subjectivities.

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Information use intrigues information behavior researchers, though many have struggled with how to conceptualize and study this phenomenon. Some work suggests that information may have social uses, hinting that information use is more complicated than previous frameworks suggest. Therefore, we use a micro-sociological, symbolic interactionist approach to examine the use of one type of information-biomedical information-in the everyday life interactions of chronic illness patients and their families. Based on a grounded theory analysis of 60 semi-structured interviews (30 individual patient interviews and 30 family group interviews) and observations within the family group interviews, we identify 4 categories of information use: (a) knowing my body; (b) mapping the social terrain; (c) asserting autonomy; and (d) puffing myself up. Extending previous research, the findings demonstrate use of biomedical information in interactions that construct a valued self for the patient: a person who holds authority, and who is unique and cared for. In so doing, we contribute novel insights regarding the use of information to manage social emotions such as shame, and to construct embodied knowledge that is mobilized in action to address disease-related challenges. We thus offer an expanded conceptualization of information use that provides new directions for research and practice.

This paper considers sensemaking as it relates to everyday software engineering (SE) work practices and draws on a multi-year ethnographic study of SE projects at a large, global technology company building digital services infused with artificial intelligence (AI) and machine learning (ML) capabilities. Our findings highlight the breadth of sensemaking practices in AI/ML projects, noting developers' efforts to make sense of AI/ML environments (e.g., algorithms/methods and libraries), of AI/ML model ecosystems (e.g., pre-trained models and "upstream" models), and of business-AI relations (e.g., how the AI/ML service relates to the domain context and business problem at hand). This paper builds on recent scholarship drawing attention to the integral role of sensemaking in everyday SE practices by empirically investigating how and in what ways AI/ML projects present software teams with emergent sensemaking requirements and opportunities.

Human-algorithm interaction is a growing phenomenon of interest as the use of machine learning (ML) capabilities in everyday technologies becomes more commonplace. In the workplace, such developments raise questions about how people not only make sense of algorithmic actions, but also figure out ways to collaborate with tools and systems that integrate algorithmic outputs. We draw on a field study of IT infrastructure design and report on the experiences of highly-skilled IT architects with the natural language processing (NLP) capabilities in an intelligent system under development to support their solution design work. While architects were supportive of the potential of NLP to enhance their solutioning work, they faced challenges in integrating such capabilities into their existing collaborative work practices. We discuss how these findings add nuance and complexity to discourse around the future of work.

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