Prominent technologies such as social media platforms and search engines are profitable in large part because the public supplies time, data, and knowledge without compensation. By monetizing users’ activities, technology companies accumulate an immense amount of power to shape the technology landscape without the public’s input. This power imbalance is problematic when the public interest and well-being are at odds with corporate interests, as seen in issues related to misinformation, user privacy, and algorithm governance.
In this talk, I will present my empirical research on data labor, a framework that explicates the relationship between data and power, in mitigating the problematic power imbalance between the public and technology companies. I define data labor as user activities that generate or improve data for for-profit technology companies, for example, moderating online content and producing ratings.
My talk will introduce a method that quantitatively measures the monetary value of data labor, using volunteer content moderation as a case study. I will show that this instance of “labor subsidy” that members of the public unwittingly supply to for-profit technology companies is in the order of millions of dollars annually. I will then provide an overview of how the transparency of data labor’s value informs pragmatic pathways for researchers, designers, and policymakers to redistribute power in tech to members of the public. Together, my work informs an agenda for more democratic governance of data and technology.
In 2023, I am going to be a postdoc in the Center for Long-Term Cybersecurity at UC Berkeley and then an assistant professor in the School of Information at UT Austin. Very excited about these positions!
I am a Ph.D. candidate in Technology and Social Behavior at Northwestern University and a visiting scholar at the Center for Long-term Cybersecurity at UC Berkeley. My research in human-computer interaction and social computing is on reimagining data governance with the goal of empowering the public in its relationship with technology companies. I do so by examining the utility and value of user-generated data for businesses and the public. I also design and build systems to help the public leverage their data power in collective action. I am working on my dissertation entitled “Rethinking Data Governance: A Labor-Oriented Approach.” I am in the People, Space, and Algorithms (PSA) Research Group and co-advised by Dr. Brent Hecht at Northwestern University and Dr. Stevie Chancellor at the University of Minnesota Twin Cities.
Prior to my doctoral study, I gained design experience in multiple industrial domains, including biotech, education, and cargo shipping. I earned my Master’s degree in Human-Computer Interaction from Indiana University-Purdue University Indianapolis, where I was advised by Dr. Erin Brady (chair), Dr. Lynn Dombrowski, and Dr. Andrew Miller.