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LinkedIn is central to salaried job search and professional networking. In a career development program for adults seeking upward socioeconomic mobility through middle-wage computing work, we aimed to use LinkedIn to find and develop new social ties. However, we could not use the platform for this purpose. Through a participatory research approach, we formed a research team with diverse positionalities to understand why LinkedIn was difficult to use and how it could be better for our program. We analyzed recorded walk-throughs and confirmed our findings with two years of ethnographic field notes and written reflections. Our findings demonstrate that LinkedIn's embedded algorithms and interface design prioritize users with large networks who can afford a LinkedIn Premium subscription. We argue that such platform-embedded power differentials lead to platform-delivered microaggressions. Non-Premium users and users with small networks must endure microaggressions to participate in the salaried labor market. We argue the politics of LinkedIn as a platform are such that its embedded power differentials are beyond our control and unlikely to change. Therefore, we recommend sociotechnical coping and mitigation strategies for career development programs in lieu of design implications for LinkedIn or similar platforms. We contribute a detailed example of how a technology reinforces pre-existing privilege without users' knowledge. 

Data work is often completed by crowdworkers, who are routinely dehumanized, disempowered, and sidelined. We turn to citizen science to reimagine data work, highlighting collaborative relationships between citizen science project managers and volunteers. Though citizen science and traditional crowd work entail similar forms of data work, such as classifying or transcribing large data sets, citizen science relies on volunteer contributions rather than paid data work. We detail the work citizen science project managers did to shape volunteer experiences: aligning science goals, minimizing barriers to participation, engaging communities, communicating with volunteers, providing training and education, rewarding contributions, and reflecting on volunteer work. These management strategies created opportunities for meaningful work by cultivating intrinsic motivation and fostering collaborative work relationships but ultimately limited participation to specific data-related tasks. We recommend management tactics and task design strategies for creating meaningful work for invisible collar workers, an understudied class of labor in CSCW. 

Computing education is often confined to the context of formal education or after-school programs; however, there is a growing industry built around adult education, including workshops, coding intensives, online learning, and apprenticeship programs. Amidst these efforts, little research has explored the workplace as a site for novice adult learners to develop computing skills. In this experience report, we present an integrated training curriculum for adults at DataWorks, an organization that trains and employs novice adults from groups historically underrepresented in computing who seek to advance their career through on-the-job learning. ''Data Fellows'' are hired to complete client projects by providing data services for local organizations, nonprofits, and businesses. Training is integrated into employees' weekly responsibilities at DataWorks, and the curriculum consists of four modules: Microsoft Excel, Critical Data Literacy, Python Fundamentals, and Career Development. In this report, we reflect holistically on the evolution of the curriculum over three years. We distill our reflection into insights to inform other integrated training programs that aim to equip novice adults with computing skills in the workplace. 

In this paper, we describe and reflect upon the development of critical consciousness and workplace democracy within an experimental workplace called DataWorks. Through DataWorks, we hire adults from communities historically minoritized in computing education and data careers, and train them in entry-level data skills developed through work on client projects. In this process, workers gain a range of skills. Some of these skills are technical, such as programming for data analysis; some are managerial, such as scoping and bidding projects; others are social, perhaps even political, such as the ability to say No to projects. In what follows, we describe a workshop series developed to build the workers' critical literacy and consciousness about their data work, specifically regarding the use of data in machine learning systems. After that, we describe a data project the workers questioned and resisted because they determined the work to be harmful. In that process, they demonstrated and enacted a critical consciousness towards data and machine learning. Reflecting on this enactment of data-focused critical consciousness, we identify themes that characterize a democratic workplace, describe the work of designing for organizational action and institutional relations, and discuss how worker and researcher positionality affects this work. In doing so, we argue for enabling workers to resist and refuse harmful data work and challenge the standard power structures of academic research and data work. 

We present a Google Sheets add-on, Datum Fieldnotes, to enhance and formalize civic and non-profit (CNP) data workers’ rich dataset annotation and contextualization practices. Unlike the massive, poorly-contextualized datasets typically used to train AI system—which result in unexpected and harmful downstream system behavior— CNP datasets are usually crafted with an intimate understanding of the subject matter by the data workers creating and contributing to them, whether professionals, volunteers, or CNP clients. As part of a larger project, in this paper we report on the development of the Datum Fieldnotes tool and the results of user testing sessions with twelve CNP data workers. Our experience demonstrates how tools to facilitate data contextualization practices can be developed and supported. Following the user testing, we share a key revision to the tool: namely, the creation of a suite of training materials to not only ease tool onboarding for CNP data workers, but also to give them to the tools to interrogate and analyze the resultant paradata themselves, as a record of their own expertise and data labor contributed. This second contribution is critical to the development of CNP data work tools that are actually usable by practitioners in the long term. 

While much computing education research focuses on formal K-12 and undergraduate CS education, a growing body of work is exploring alternative pathways to computing careers [7, 16], alternative outcomes for computing education [15], and adult learning in workplace communities [9, 13]. Within this context, we are studying novice-friendly computational work as a pathway to computing careers. Novice-friendly computational work is a phrase we use to describe computing activities that have a low barrier to entry, are used in authentic contexts outside formal CS spaces, and are legitimate computational activities, e.g., data work [13], web design [5], and Salesforce CRM [9]. Learning through authentic work practices is a promising pathway to computing careers because it poses lower financial and findability barriers than coding bootcamps [14] and online courses [4]. However, gatekeeping culture in computing deems novice-friendly tools like Excel, HTML/CSS, and JSON distinct from “real” programming [12]. Further, novice workers may not be considered legitimate peripheral members of computing communities of practice despite engaging in legitimate computational work [6, 11]. 

Artistic computing learning environments have been of central importance in the exploration of how to support equity and inclusion in computing. Explorations within e-textiles, music, and interactive media, for example, have created diverse opportunities for learning how to program while creating culturally relevant artifacts. However, there is a gap in our understanding of the design processes of learners in these constructionist environments, including how the computational artifacts and their components impact the learning processes and the ways they build meaning and agency with computing. We advocate for research to attend more closely to the materiality of the computational materials to understand how they impact the social and cultural dimensions in which students are learning. In this paper, we present an analysis of 6 high school learners’ experiences within a co-designed arts and computing curriculum. Our analyses highlight how the materiality of the components impacted the ways in which learners developed personal and epistemological connections to computing based on how it enabled them to connect to their interests, represent their ideas, engage with their community, and overcome or navigate around challenges to get to their final designs. We demonstrate how centralizing the materiality in the design of computational construction kits can inform how we support agency and engagement with computing. 

This work contributes to just and pro-social treatment of digital pieceworkers ("crowd collaborators") by reforming the handling of crowd-sourced labor in academic venues. With the rise in automation, crowd collaborators' treatment requires special consideration, as the system often dehumanizes crowd collaborators as components of the “crowd” [41]. Building off efforts to (proxy-)unionize crowd workers and facilitate employment protections on digital piecework platforms, we focus on employers: academic requesters sourcing machine learning (ML) training data. We propose a cover sheet to accompany submission of work that engages crowd collaborators for sourcing (or labeling) ML training data. The guidelines are based on existing calls from worker organizations (e.g., Dynamo [28]); professional data workers in an alternative digital piecework organization; and lived experience as requesters and workers on digital piecework platforms. We seek feedback on the cover sheet from the ACM community 

In this paper, we describe and analyze a workshop developed for a work training program called DataWorks. In this workshop, data workers chose a topic of their interest, sourced and processed data on that topic, and used that data to create presentations. Drawing from discourses of data literacy; epistemic agency and lived experience; and critical race theory, we analyze the workshops’ activities and outcomes. Through this analysis, three themes emerge: the tensions between epistemic agency and the context of work, encountering the ordinariness of racism through data work, and understanding the personal as communal and intersectional. Finally, critical race theory also prompts us to consider the very notions of data literacy that undergird our workshop activities. From this analysis, we ofer a series of suggestions for approaching designing data literacy activities, taking into account critical race theory.