Open Access

Labels inform smart home users about the privacy of devices before purchase and during use. Yet, current privacy labels fail to fully reflect the impact of advanced device configuration options like sensor state control. Based on the successful implementation of related privacy and security labels, we designed extended static and interactive labels that reflect sensor states and device connectivity. We first did expert interviews (N = 10) that informed the final label design. Second, we ran an online survey (N = 160) to assess the interpretation and usability of the novel interactive privacy label. Lastly, we conducted a second survey (N = 120) to investigate how well our interactive labels educate users about sensor configuration. We found that most participants successfully used the interactive label and retrieved sensor information more efficiently and correctly. We discuss our findings in the context of a potential shift in label use toward control and use-case-based interaction.

AI is anticipated to enhance human decision-making in high-stakes domains like aviation, but adoption is often hindered by challenges such as inappropriate reliance and poor alignment with users' decision-making. Recent research suggests that a core underlying issue is the recommendation-centric design of many AI systems, i.e., they give end-to-end recommendations and ignore the rest of the decision-making process. Alternative support paradigms are rare, and it remains unclear how the few that do exist compare to recommendation-centric support. In this work, we aimed to empirically compare recommendation-centric support to an alternative paradigm, continuous support, in the context of diversions in aviation. We conducted a mixed-methods study with 32 professional pilots in a realistic setting. To ensure the quality of our study scenarios, we conducted a focus group with four additional pilots prior to the study. We found that continuous support can support pilots' decision-making in a forward direction, allowing them to think more beyond the limits of the system and make faster decisions when combined with recommendations, though the forward support can be disrupted. Participants' statements further suggest a shift in design goal away from providing recommendations, to supporting quick information gathering. Our results show ways to design more helpful and effective AI decision support that goes beyond end-to-end recommendations.

Hubs are at the core of most smart homes. Modern cross-ecosystem protocols and standards enable smart home hubs to achieve interoperability across devices, offering the unique opportunity to integrate universally available smart home privacy awareness and control features. To date, such privacy features mainly focus on individual products or prototypical research artifacts. We developed a cross-ecosystem hub featuring a tangible dashboard and a digital web application to deepen our understanding of how smart home users interact with functional privacy features. The ecosystem allows users to control the connectivity states of their devices and raises awareness by visualizing device positions, states, and data flows. We deployed the ecosystem in six households for one week and found that it increased participants’ perceived control, awareness, and understanding of smart home privacy. We further found distinct differences between tangible and digital mechanisms. Our findings highlight the value of cross-ecosystem hubs for effective privacy management.