Position: PhD Candidate
Current Institution: Northwestern University
Abstract: Improving Labor Transparency in Social Computing Systems
Sociotechnical systems are built maintained and made financially successful by human labor. Digital workers from all around the world contribute and generate data manage content and collaborate to uphold important and prevalent systems such as Wikipedia and sub-fields of computing research such as crowdsourcing and artificial intelligence. Scholars studying data governance data feminism and the data economy brought forth the invisibility of such human labor and its broader societal impact: the lack of labor transparency made it possible for for-profit companies that operate social computing systems to reap the financial benefits of invisible labor contributing to their outsized influence over the public diminishing user privacy and industry-wide decline in labor share (the proportion of business income allocated to wages). While the importance of users’ invisible labor to social computing systems is qualitatively known we have limited quantitative evidence about the large-scale economic impact of this labor. The lack of transparency and assessment of invisible labor’s value impedes users’ ability to discern their contribution to many successful technology companies and hinders research and policymaking efforts from exploring equitable and democratic governance of social computing systems that are powered by the public’s collective labor. My dissertation aims to pave the way for the public scholars and policymakers to understanding invisible labor at a large scale and inform design and policy interventions to improve labor transparency in social computing systems. Building on digital labor literature computational social science and labor studies I make two distinct contributions: 1) quantitative methods to assess invisible labor’s economic value and 2) a definition of labor transparency from the public’s perspective.
Hanlin Li is a fifth-year Ph.D. student in Technology and Social Behavior at Northwestern University a joint program between Computer Science and Communication Studies. She is part of the People Space and Algorithms Research Group. Her research focuses on the utility and value of user-generated data for technology companies and the public. She is currently developing quantitative methods to measure the monetary value of user-generated data. She is also interested in designing and building systems to help the public leverage their data power in collective action. She holds a master’s degree in Human-Computer Interaction and a bachelor’s degree in Computer Science.