Johanna Gunawan

Personalized privacy assistants (PPAs) are emerging AI assistants that hold the promise to manage privacy on users’ behalf. Prior to the design decisions and deployments of a PPA in different regions, it is important to understand prospective user acceptance and factors contributing to users’ behavioral intention to adopt the assistant. In this paper, we report a cross-regional survey study with participants from China, Germany and California analyzing a UTAUT2-informed acceptance model for PPA acceptance. The results suggest that cross-regional comparability of PPA acceptance cannot be assumed. While partial measurement invariance supports meaningful structural comparisons between California and Germany, compositional invariance was not established for several constructs when comparing the Chinese sample with Californian and German samples. Together, we raise three provocations based on these findings: 1) Cross-regional acceptance comparisons might often be invalid by design; 2) it is time to reconsider acceptance models for AI assistants for privacy management, and 3) some thoughts on the future of PPAs.

FemTech is an emerging industry offering products, software, and services to support women’s health and well-being. Within FemTech, mobile health applications (mHealth apps) are popular for managing menstruation, fertility, pregnancy, and menopause. Yet, these apps expose users to deceptive and misleading practices, which can be characterized as Dark Patterns in user experience (or dark UX). Dark UX in commercial FemTech mHealth apps is underexplored, leaving a critical gap in understanding how deceptive patterns manifest in intimate health contexts, the harms they cause, and how to address them. We crowd-source and thematically analyze user accounts of dark UX through user reviews from sixteen systematically selected FemTech mHealth apps. User-reported accounts of dark UX in FemTech mHealth apps reveal several problematic design patterns, which emphasize risks for minors and the need for more transparent design of FemTech mHealth apps. Based on our results, we outline recommendations for enhancing ethical UX design and furthering regulatory action in FemTech.

Deceptive, manipulative, and coercive practices are deeply embedded in our digital experiences, impacting our ability to make informed choices and undermining our agency and autonomy. These design practices—collectively known as “dark patterns” or “deceptive patterns”—are increasingly under legal scrutiny and sanctions, largely due to the efforts of human-computer interaction scholars that have conducted pioneering research relating to dark patterns types, definitions, and harms. In this workshop, we continue building this scholarly community with a focus on organizing for action. Our aims include: (i) building capacity around specific research questions relating to methodologies for detection; (ii) characterization of harms; and (iii) creating effective countermeasures. Through the outcomes of the workshop, we will connect our scholarship to the legal, design, and regulatory communities to inform further legislative and legal action.

Technology ethics is increasingly at the forefront of human-computer interaction scholarship, with increasing visibility not only to end users of technology, but also regulators, technology practitioners, and platforms. The notion of “dark patterns” has emerged as one common framing of technology manipulation, describing instances where psychological or perceptual tricks are used to decrease user agency and autonomy. In this panel, we have assembled a group of highly diverse early-career scholars that have built a transdisciplinary approach to scholarship on dark patterns, engaging with a range of socio-technical approaches and perspectives. Panelists will discuss their methodological approaches, key research questions to be considered in this emerging area of scholarship, and necessary connections between and among disciplinary perspectives to engage with the diverse constituencies that frame the creation, use, and impacts of dark patterns.