Research Group Albrecht Schmidt

Future domestic robots will become integral parts of our homes. They will have various sensors that continuously collect data and varying locomotion and interaction capabilities, enabling them to access all rooms and physically manipulate the environment. This raises many privacy concerns. We investigate how such concerns can be mitigated, using all possibilities enabled by the robot's novel locomotion and interaction abilities. First, we found that privacy concerns increase with advanced locomotion and interaction capabilities through an online survey (N=90). Second, we conducted three focus groups (N=22) to construct 86 patterns to communicate the states of microphones, cameras, and the internet connectivity of domestic robots. Lastly, we conducted a large-scale online survey (N=1720) to understand which patterns perform best regarding trust, privacy, understandability, notification qualities, and user preference. Our final set of communication patterns will guide developers and researchers to ensure a privacy-preserving future with domestic robots.

Private homes are increasingly becoming smart spaces. While smart homes promise comfort, they expose most intimate spaces to security and privacy risks. Unfortunately, most users today are not equipped with the right tools to assess the vulnerabilities or privacy practices of smart devices. Further, users might lose track of the devices installed in their homes or are unaware of devices placed by a partner or host. We developed SaferHome, an interactive digital-physical privacy framework, to provide smart home users with security and privacy assessments and a sense of device location. SaferHome includes a digital list view and physical and digital dashboards that map real floor plans. We evaluated SaferHome with eight households in the wild. We find that users adopted various strategies to integrate the dashboards into their understanding and interpretation of smart home privacy. We present implications for the design of future smart home privacy frameworks that are impacted by technical affinity, device types, device ownership, and tangibility of assessments.

While systems that use Artificial Intelligence (AI) are increasingly becoming part of everyday technology use, we do not fully understand how AI changes design processes. A structured understanding of how designers work with AI is needed to improve the design process and educate future designers. To that end, we conducted interviews with designers who participated in projects which used AI. While past work focused on AI systems created by experienced designers, we focus on the perspectives of a diverse sample of interaction designers. Our results show that the design process of an interactive system is affected when AI is integrated and that design teams adapt their processes to accommodate AI. Based on our data, we contribute four approaches adopted by interaction designers working with AI: a priori, post-hoc, model-centric, and competence-centric. Our work contributes a pragmatic account of how design processes for AI systems are enacted.

Users avoid engaging with privacy policies because they are lengthy and complex, making it challenging to retrieve relevant information. In response, research proposed contextual privacy policies (CPPs) that embed relevant privacy information directly into their affiliated contexts. To date, CPPs are limited to concept showcases. This work evolves CPPs into a production tool that automatically extracts and displays concise policy information. We first evaluated the technical functionality on the US’s 500 most visited websites with 59 participants. Based on our results, we further revised the tool to deploy it in the wild with 11 participants over ten days. We found that our tool is effective at embedding CPP information on websites. Moreover, we found that the tool’s usage led to more reflective privacy behavior, making CPPs powerful in helping users understand the consequences of their online activities. We contribute design implications around CPP presentation to inform future systems design.