Annabel Rothschild

The U.S. Food and Drug Administration (FDA)’s Food Traceability Rule, promulgated as part of the Food Safety Modernization Act (FSMA), requires agri-food supply chain stakeholders (stakeholders) —including farmers, fishers, retail workers, and others—to maintain detailed tracking records beginning in January 2026. Through this Rule, the FDA envisions a “New Era of Tech-Enabled Traceability,” in which standardized, harmonized tracking data serve as a foundational public health infrastructure, enabling more rapid identification and removal of potentially contaminated food and ultimately reducing the risk of foodborne illness. Despite this promising vision, we observe that the Rule reconfigures agri-food stakeholders into data laborers by mandating stringent data-collection, formatting, and reporting requirements. In this paper, we examine the tensions and burdens that arise from such reconfiguration. Leveraging Data Feminism as an orientation to attend to how data-driven policy implementation disproportionately burdens smaller, under-resourced stakeholders who lack the infrastructural and financial capacity to comply, we analyze 1,198 public comments submitted to Regulations.gov in response to the proposed Rule. Our qualitative document analysis reveals three key tensions: (1) the individual labor, financial, and educational burdens stakeholders experience as they are reconfigured into data workers; (2) moments where data tracking becomes infeasible due to infrastructural limitations, cultural contexts, and situated production practices; and (3) instances where the Rule’s intended flexibility instead introduces confusion and burden due to its ambiguity. This work contributes to CSCW by providing an empirical account of how data-driven policy reconfigures stakeholders as data producers. We demonstrate that those reconfigured as data workers under data-driven governance must be recognized as meaningful policy partners rather than merely as actors subjected to compliance obligations. We further suggest that CSCW can play a critical role in uncovering their yet invisible burdens, supporting their capacity to voice concerns through participatory democratic mechanisms, and aiding their ability to “make-do” under imposed data works.

As AI and data systems often falter when encountering queer identities and knowledge, reinforcing existing oppressions, queer people have resisted such systems and their normalizing tendencies. This pictorial explores tactics of queering AI through a collaborative zine-making project (i.e. zineography) that challenges generative AI and data systems. We share how we workshopped and materialized queering tactics in zine spreads; analyzed these spreads according to materials, content, and tone; and visualized our analysis as thematic collages. We contribute: (1) tangible characteristics of queering AI and data systems (i.e. materials, tones, and aesthetics); and (2) design opportunities for using zineographies as a radical method for building and collectively sharing knowledge about a marginalized community, including recommendations for enacting queer zineographies. By materializing queering tactics through zine-making, we invite embodied, action-oriented critiques that question dominant techno-solutionist movements and trace queer possibilities outside of their normalizing narratives.

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.

The massive datasets used to train AI models frequently contain offensive and harmful entries that are only caught during the system’s later performance. The data workers who curate such datasets are experts in datasets’ contents, but silenced by horrible labor conditions and lack of respect by their employers. In my dissertation, I study how to build 1) workplaces, 2) workflows, and 3) tools to elicit and embrace data workers’ observations in dataset development. My goals are creating datasets safe to use to train AI systems and developing a more pro-social data labor paradigm.

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.

This one day workshop will explore queering as a design technique for troubling data and AI systems, ranging from quotidian personal data to recent Generative AI tools. By surfacing numerous instances of queering data or AI, we will come together to develop an archive of techniques for queering or artful subversion. From this archive, participants will select a technique and develop a speculative prototype or artifact via critical making. In doing so, we resist techno-determinism and conventional narratives of AI harms and benefits by tracing queer possibilities outside these categories.

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.