The Need for Sensemaking in Networked Privacy and Algorithmic Responsibility

This paper proposes that two significant and emerging problems facing our connected, data-drivensociety may be more effectively solved by being framed as sensemaking challenges. The first is inempowering individuals to take control of their privacy, in device-rich information environmentswhere personal information is fed transparently to complex networks of information brokers. Althoughsensemaking is often framed as an analytical activity undertaken by experts, due to the fact thatnon-specialist end-users are now being forced to make expert-like decisions in complex informationenvironments, we argue that it is both appropriate and important to consider sensemaking challengesin this context. The second is in supporting human-in-the-loop algorithmic decision-making, in whichimportant decisions bringing direct consequences for individuals, or indirect consequences for groups,are made with the support of data-driven algorithmic systems. In both privacy and algorithmic decision-making, framing the problems as sensemaking challenges acknowledges complex and illdefinedproblem structures, and affords the opportunity to view these activities as both building uprelevant expertise schemas over time, and being driven potentially by recognition-primed decisionmaking.