Hanlin Li, Nicholas Vincent &ndash; Northwestern University, USA
The current data governance paradigm in which technology companies solely decide how user data is collected and used has introduced many issues to the tech sector. Prominent examples include information asymmetry about user data’s value, monopolistic practices enabled by data’s network effects, and power imbalance with respect to data aggregation and analysis. This work explicates how viewing users’ data-generating activities through a labor lens can help to mitigate these issues and provides corresponding design and research directions.
2.1 Information asymmetry about user data's value¶ The lack of transparency about user data's value helps make it possible for operators of for-profit computing systems to monetize user data and reap the bulk of its financial benefits. Currently, there exists a substantial gap between what data-driven technology companies know about user data's value and what users themselves do. For example, while social media platforms are well aware of the amount of financial benefits of user engagement, users do not have a window into how their collective attention and knowledge powers such businesses. This information asymmetry is further exacerbated by the fact that the vast majority of data that users produce during their interaction with modern technologies is rarely visible to themselves and is used downstream without their awareness and consent. For instance, the rise of AI technologies is possible largely due to the abundance of data unwittingly generated by the public for purposes other than enabling AI models. Prominent examples include Flickr photos [12], Wikipedia articles [14], and the Common Crawl dataset consisting of publicly available webpages [11]. In many of such cases, users produce data without being aware of its value and potential, giving technology companies the opportunity to extract an enormous amount of revenue from such data.