1 research outputs found
Mapping the Potential and Pitfalls of "Data Dividends" as a Means of Sharing the Profits of Artificial Intelligence
Identifying strategies to more broadly distribute the economic winnings of AI
technologies is a growing priority in HCI and other fields. One idea gaining
prominence centers on "data dividends", or sharing the profits of AI
technologies with the people who generated the data on which these technologies
rely. Despite the rapidly growing discussion around data dividends - including
backing by prominent politicians - there exists little guidance about how data
dividends might be designed and little information about if they will work. In
this paper, we begin the process of developing a concrete design space for data
dividends. We additionally simulate the effects of a variety of important
design decisions using well-known datasets and algorithms. We find that
seemingly innocuous decisions can create counterproductive effects, e.g.
severely concentrated dividends and demographic disparities. Overall, the
outcomes we observe -- both desirable and undesirable -- highlight the need for
dividend implementers to make design decisions cautiously.Comment: This is a working draft. It has not been peer-reviewed and is
intended for internal discussion in the computing communit