Folk Theories of Cyber-Social Systems and their Implications for Privacy
As people interact with complex cyber-social systems such as Facebook’s ranked news feed and Uber’s hiring algorithms, they build up folk theories of how these systems work. These theories, however, can often be wrong. For example, many people believed the Facebook news feed to be an unfiltered window of their friends’ behavior, leading to widespread surprise and news coverage when a Facebook experiment on emotional contagion highlighted that Facebook manipulates the content of users’ feeds. We propose to investigate the folk theories that people hold about complex cyber-social systems, and determine whether users’ privacy behaviors on these systems are direct reflections of their folk theories. We then propose targeted design interventions to nudge users’ folk theories. This research highlights how systems and algorithms impact society not only through their direct outputs, but also through the (potentially problematic) understandings that people form of them.
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