AI and Society Workshop

Sarah Fox is an assistant professor at Carnegie Mellon University in the Human-Computer Interaction Institute, where she directs the Tech Solidarity Lab. Her research examines how AI, automation and algorithmic management systems are designed, deployed and governed in everyday work settings, and how these processes redistribute power between institutions, technologies, and workers. Drawing on ethnographic fieldwork and design research, she foregrounds workers’ situated expertise as a critical site of governance, tracing how everyday practices of adaptation, contestation and repair expose the limits of technological systems and inform mechanisms of accountability and enforcement. Her work has earned multiple best paper and honorable mention awards at ACM CSCW, CHI and DIS, has been published in journals including Design Issues, New Media & Society and Feminist Studies, and is supported by the National Science Foundation—including a CAREER award—the U.S. Department of Transportation, and the Russell Sage Foundation.

Artificial intelligence is frequently introduced into workplaces with the promise that it will make work easier, safer, or more efficient. In practice, many automated systems only function because workers intervene when those systems fall short. This talk examines how organizations deploy workplace technologies in ways that depend on frontline intervention while restricting workers’ influence over how those systems are designed, evaluated and governed.

Drawing on ethnographic and design research in waste management, hospitality and public transportation, the talk shows how AI systems impose standardized logics on labor that is inherently contingent, embodied, and relational. When those logics break down—or conflict with the demands of safety, service or bodily limits—workers absorb the consequences: adapting robotic systems, navigating algorithmic schedules, and exercising discretion under tightly constrained operating conditions. This work is essential to keeping organizations functioning, yet it is rarely recognized as expertise or incorporated into decisions about procurement, deployment or oversight.

The talk then traces what changes when worker expertise is taken seriously as a basis for governance. Across cases, Fox examines labor-aligned design work, collective bargaining and policy interventions that shift decision-making power—ranging from co-design efforts that surface worker knowledge, to procurement constraints, oversight requirements and regulatory obligations that make that knowledge institutionally binding. A consistent pattern emerges: worker participation becomes consequential only when it alters who has the authority to set limits, define acceptable risk, and halt or redirect deployment. Taken together, these cases show that AI governance depends on whether working people have meaningful authority in the creation, implementation, and governance of the technologies that organize their labor.

Joy Ming has over a decade of experience in academia and industry working with community partners on socially impactful technology across multiple geographies (e.g., South/Southeast Asia, Sub Saharan Africa) and domains (e.g., disability justice, global health, civic engagement) as an NSF Graduate Fellow, software engineer at Google, and Fulbright researcher. She has made contributions to discourse in human-computer interaction, information and communication technologies for development, science and technology studies, and labor relations. Additionally, she creates real-world, community impact through technological artifacts, public writing, and policy campaigns. 

Maria Y. Rodriguez, MSW, PhD Rodriguez joined the University at Buffalo in 2020. Her research is at the intersection of applied demography, computational social science, and social policy. Dr. Rodriguez’ work explores systems of care across technology and human services. From offline child welfare systems to online social media platforms, her work examines the systems we build to care for marginalized groups, particularly how we make decisions about whom those groups are. Based on a central tenet of ethical social work practice, the aim of Dr. Rodriguez’ work is to support the reorientation of systems towards working best for outlier cases. In her work, Dr. Rodriguez explores if and how the values and ideals  that define systems  can come from the  lived experience of the system involved.

Brian E. Perron, PhD, is a professor at the University of Michigan School of Social Work. Perron’s recent work focuses on helping community-based organizations use data to improve service delivery and other business processes. This includes developing user-friendly and sustainable data management systems; creating interactive data visualizations to facilitate interpretation of data, especially for non-technical users; and building organizational capacity to promote data-driven decision making. Perron helped establish and works actively with the Child & Adolescent Data Lab, where he examines services for vulnerable youth and families in the child welfare system, with the ultimate goal of improving service outcomes. His research (NCBI, Google Scholar, ResearchGate) has been supported by the National Institutes of Health, Department of Veterans Affairs and the state of Michigan. Perron is also interested in the role, application and ethical use of artificial intelligence in social work and how tools like machine learning and natural language processing can be leveraged to improve our knowledge.

Bio: Alexandra Mateescu is an ethnographer and researcher in the Labor Futures program at the Data & Society Research Institute, where her work has spanned between academic research, policy, education, and advocacy focused on understanding how new technologies often deepen and entrench existing inequities and social precarities, particularly in low-wage and feminized industries. Her past projects have explored care labor within the gig platform economy, the human labor behind automation within service industries and agricultural labor, worker data rights and resistance against data commodification, the intersections of state surveillance, disability, and criminalization within the US welfare systems, and emergent worker impacts of AI technologies. She holds a Masters degree in Social Sciences (Anthropology) and Bachelors from the University of Chicago, and is currently a 2025-2026 Fellow at the Siegel Family Endowment. 

Challenging the cycle of efficiency, austerity, and devaluation in workplace AI adoption

This talk will explore the mutually-reinforcing narratives and structural trends that fuel the boom of workplace AI adoption, as corporate spending on AI reaches peak levels in a race to build an “AI-first” economy. Just as the rise of the gig platform economy and techniques of algorithmic management were not really about empowering workers to become their own bosses, the current wave of AI investment is neither a matter of straightforward workplace augmentation nor of one-to-one task automation. First, I will discuss how tech companies’ and employers’ pursuit of AI-driven efficiency metrics can often cut off workers’ abilities to define the value added–or lack thereof–of AI systems within their workplaces, distorting both professional norms and how efficiency itself is defined. In many sectors, these narratives of efficiency can serve to legitimize the institutional leaders’ framing of AI as a solution to widening service and labor gaps in the wake of large-scale institutional and government disinvestments—particularly in areas like healthcare, education, legal services, and the public sectors. Through infrastructural capture, technology firms can further consolidate authority over professional expertise and occupational scope, contributing to the further devaluation of skilled labor. This talk will conclude by examining how these cycles can exacerbate existing racial, gender, and class inequalities within industries, where AI integrations often serve to reinforce the precarity of societally-devalued labor while limiting policy debates towards calls to reskill workers or perfunctory inclusion of workers in technology design. This work is part of an ongoing project and draws on literature review and expert interviews with labor and civil society leaders, policy experts, and academic researchers connected to workers and constituencies across multiple sectors, including healthcare, logistics, hospitality, telecommunications, tech, the public sector, education, and creative industries. 

Rice specializes in social network science and theory, as well as community-based research. His primary focus is on youth experiencing homelessness and how issues of social network influence may affect risk-taking behaviors and resilience. For several years he has worked with colleague Milind Tambe to merge social work science and AI, seeking novel solutions to major social problems such as homelessness and HIV.

Rice is the author of more than 150 peer-reviewed articles in such publications as the American Journal of Public Health, AIDS and Behavior, Journal of Adolescent Health, Pediatrics, and Child Development, and the Journal of the Society for Social Work Research. He is the recipient of grants from the National Institute of Mental Health, the California HIV/AIDS Research Program, the Army Research Office and other agencies. In 2012, he received the John B. Reid Early Career Award through the Society for Prevention Research. In 2021, he was inducted as a Fellow of the Society for Social Work Research.