Public-facing data-driven technologies such as social media platforms and search engines rely on data producers, such as users and crowd workers, to be feasible and financially sustainable. Recently, it became clear that the goals of these data-driven technologies do not always align with those of the public, causing public backlashes against such companies. Despite playing a crucial role in supporting data-driven technologies, data producers often do not have control over the downstream applications of the digital traces from their activities. My dissertation focuses on empowering data producers to leverage their data contribution to shape data practices and data-driven technologies. I do so by characterizing data production and identifying opportunities to recognize and support the unpaid labor data producers provide. In the first chapter, I examined how data producers protest prominent tech companies motivated by concerns about data monetization. In the second chapter, I construct a taxonomy of data labor to guide researchers, designers, and the general public in helping data producers gain control over the outcome of their data through collective means. The third chapter dives into a specific type of data labor--content moderation on Reddit--and characterizes the invisibility of data producers' contribution to technology. The fourth chapter then focuses on quantifying the amount of data labor going in upholding Reddit communities. In the fifth chapter, I move onto the aspect of collective action in the data labor taxonomy and identify opportunities for collective means among data producers to gain leverage against technology companies. Finally, the sixth and final chapter extends the data labor taxonomy identifying broader directions and principles of data labor through a cross-disciplinary literature review of data governance, data markets, and worker-centered design work. Together, this dissertation characterizes data labor and charts out the path towards a data future in which data producers' interests and values shape the design and development of data-driven technologies.

Creator

• Li, Hanlin

DOI

• https://doi.org/10.21985/n2-n1h9-6q88

Subject

• Computer science
• Communication

Language

• en

Alternate Identifier

• http://dissertations.umi.com/northwestern:16375
• etdadmin_upload_947289

Date created

• 2022-01-01

Resource type

• Dissertation

Rights statement

• In Copyright