Data Slots: trade-offs between privacy concerns and benefits of data-driven solutions

Cities are increasingly adopting data-driven solutions derived from diverse digital media interactions—from geolocated social media posts and self-tracking apps to CCTV surveillance and transit cards. We define a data-driven solution as an application, service, or device that leverages big data. However, concerns persist about the potential trade-off between the benefits of these solutions and individual privacy. To assess people’s opinions on these trade-offs, we designed a physical and digital card game, Data Slots, which we played with participants around the world. In this game, cards embody data possibilities, enabling players to trade cards, develop their data-driven ideas for solutions, assess other players’ proposals regarding benefits and privacy concerns, and strategically invest in their preferred solution. Here we describe the results based on more than two thousand times Data Slots have been played, in 79 countries. We show that perceived privacy concerns as well as benefits are not intrinsic values of specific solutions or datasets, but rather they are combinatorial, situational, transactional, and contextual. By understanding the complex interdependencies that shape public attitudes, policymakers, developers, and stakeholders can refine their approaches to prioritize privacy while harnessing the advantages of data-driven technologies.