Catherine Wieczorek

Inha Cha, Catherine Wieczorek, Richmond Y. Wong · gatech

Although organizations increasingly position AI adoption as a pathway to competitiveness and innovation, organizations' perspectives on productivity and efficiency often clash with workers' perspectives on AI's economic and social value. Through design workshops with 15 UX designers, we examine how AI adoption unfolds across individual, team, and organizational scales. At the individual level, designers weighed efficiency, skill development, and professional worth. At the team level, they negotiated collaboration, responsibility, and rigor. At the organizational level, adoption was shaped by compliance requirements and organizational norms. Across these scales, discourses of efficiency carried social and ethical dimensions of responsibility, trust, and autonomy. We view adoption as a site where roles, relationships, and power are reconfigured. We argue that AI adoption should be understood as a process of negotiating values, and call for future work examining how AI systems redistribute responsibility among team members, while understanding how such shifts could strengthen worker agency.

Soonho Kwon, Catherine Wieczorek, Heidi Biggs et al.

The U.S. Food and Drug Administration (FDA)'s Food Traceability Rule 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.