Mapping the Potential and Pitfalls of "Data Dividends" as a Means of Sharing the Profits of Artificial Intelligence - Vincent, Nicholas; Li, Yichun; Zha, Renee; Hecht, Brent (arXiv, 2019)
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.