Speaker

Hanlin Li

Abstract

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 wellbeing are at odds with corporate profits, as seen in issues related to misinformation, user privacy, and algorithm governance.

In this talk, I will present my 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. In studying data labor, I take mixed-methods approaches, combining insights from large-scale data modeling and qualitative investigations. 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.

Bio

Hanlin Li is a Ph.D. candidate in Technology and Social Behavior at Northwestern University. Her research sits at the intersection of data governance, HCI, and labor studies. She examines the societal and economic impact of data generated by the public and identifies opportunities to mitigate issues related to data monetization, power inequities, and transparency in the tech sector. Li publishes in premier venues such as ACM CHI, ACM CSCW, and ACM FAccT. Her work has been featured in national and international media, including the MIT Technology Review and Bloomberg. She was recently selected for the EECS Rising Stars Cohort at MIT and has been part of the California Data Dividends Working Group, a committee making policy recommendations aimed at building an equitable data economy in California.

Talk url | Passcode: 766398 [Video-off please during talk]