Heng Xu and Nan Zhang will speak at the Interdisciplinary Symposium on Human and Societal Aspects in Computing organized by the Max Planck Institute for Security and Privacy on October 25-26, 2022, in Bochum, Germany.

The title of Heng’s talk is “The Future of Privacy Research: Lessons from Artificial Intelligence and Machine Learning”. The abstract of the talk follows.

There is a growing concern that what privacy scholars study in research – theoretically, empirically, and technically – do not resonate well with consumers or businesses in practice. The aim of my talk is to review the obstacles facing behavioral researchers today and to offer alternative paths for proceeding forward. I will focus most of the talk on the important role of context in privacy research, and how a recent breakthrough in Machine Learning can help transition a decades-old behavioral theory into a practical method for assessing context effects. At the end of my talk, I will discuss other barriers facing privacy research. Specifically, by drawing an analogy to the development of Artificial Intelligence over the past 70 years, I contend that many of these barriers may trace their root to a set of misplaced ontological and epistemological priorities that were taken for granted in the field. To explore alternative paths, I will conclude by discussing research strategies that could lower the barriers by generating scholarly knowledge from the same continuum as what people draw from when engaging in everyday activities related to their information privacy.

The title of Nan’s talk is “Theorizing about the Complexity of Privacy Phenomena: A Configurational Approach”. The abstract of the talk follows.

Privacy scholars study phenomena that are marked by complexity: Different individuals may ascribe distinct meanings to the very term “privacy”. They also tend to engage in privacy-seeking behavior (or choose not to) for idiosyncratic and, at times, contradictory reasons. The complexity of privacy phenomena is reflected in many longstanding and influential debates in the field, from what privacy means to the nature of the privacy paradox to whether privacy self-management can be effective in today’s digital world. Theorizing about a complex privacy phenomenon is challenging for at least two reasons. First, the prevalence of idiosyncrasy confronts the limits of variable-centric theorizing, an influential approach for developing the extant privacy theories. Second, casual asymmetry is a common occurrence in privacy, meaning that the factors that consistently predict the presence of an outcome need not be the mirror opposite of factors that predict its absence. In this talk, I will attempt to address these challenges by engaging with configurational theorizing, which explores how and why multiple variables (or concepts) combine in distinct configurations (i.e., value combinations) to explain a phenomenon of interest. I will show that, as a “people-centric”, rather than variable-centric, approach, configurational theorizing allows antecedents and outcomes to diverge across individuals with different configurations. Further, it naturally captures causal asymmetry because it allows us to separately contemplate (and distinguish between) the conditions that are sufficient and/or necessary for an outcome. Through an empirical demonstration, I will highlight how configurational theorizing may unveil unique insights by sensitizing scholars to how different people engage in qualitatively distinct mechanisms when making sense of privacy. I will conclude by discussing the limits of configurational theorizing and providing actionable recommendations for future research.