Hanlin Li, Nicholas Vincent, Stevie Chancellor, Brent Hecht &ndash; University of California, Berkeley, USA; University of California, Davis, USA; University of Minnesota, Minneapolis, USA; Northwestern University, Evanston, USA
The Dimensions of Data Labor: A Road Map for Researchers, Activists, and Policymakers to Empower Data Producers
Many recent technological advances (e.g. ChatGPT and search engines) are possible only because of massive amounts of user-generated data produced through user interactions with computing systems or scraped from the web (e.g. behavior logs, user-generated content, and artwork). However, data producers have little say in what data is captured, how it is used, or who it benefits. Organizations with the ability to access and process this data, e.g. OpenAI and Google, possess immense power in shaping the technology landscape. By synthesizing related literature that reconceptualizes the production of data for computing as ``data labor'', we outline opportunities for researchers, policymakers, and activists to empower data producers in their relationship with tech companies, e.g advocating for transparency about data reuse, creating feedback channels between data producers and companies, and potentially developing mechanisms to share data's revenue more broadly. In doing so, we characterize data labor with six important dimensions - legibility, end-use awareness, collaboration requirement, openness, replaceability, and livelihood overlap - based on the parallels between data labor and various other types of labor in the computing literature.
For example, publicly available texts and artwork enabled the creation of generative AI models like ChatGPT and Dall- E because model developers were able to scrape and process data from billions of web pages¹. [¹https://commoncrawl.org/2022/10/sep-oct-2022-crawl-archive-now-available/]