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This tutorial provides practical training in designing and conduct- ing online user experiments with recommender systems, and in statistically analyzing the results of such experiments. It covers the development of a research question and hypotheses, the selection of study participants, the manipulation of system aspects and mea- surement of behaviors, perceptions and user experiences, and the evaluation of subjective measurement scales and study hypotheses. Interested parties can find the slides, example datset, and other resources at https://www.usabart.nl/QRMS/. 

Batteryless wearables use energy harvested from the environment, eliminating the burden of charging or replacing batteries. This makes them convenient and environmentally friendly. However, these benefits come at a price. Batteryless wearables operate intermittently (based on energy availability), which adds complexity to their design and introduces usability limitations not present in their battery-powered counterparts. In this paper, we conduct a scenario-based study with 400 wearable users to explore how users perceive the inherent trade-offs of batteryless wearable devices. Our results reveal users’ concerns, expectations, and preferences when transitioning from battery-powered to batteryless wearable use. We discuss how the findings of this study can inform the design of usable batteryless wearables. 

This tutorial engages researchers in a series of collaborative activities towards Enhanced Privacy and Integrity Considerations (EPIC) for human subjects research in the artificial intelligence (AI) field. The tutorial aims to identify common challenges to study integrity, convey best practices for protecting participants at the point of study design, and discuss how to best design tools to support robust, privacy-enhancing human subjects research in AI. In particular, the tutorial provides hands-on training on how to determine sample size and collect participant demographics in a way that prioritizes data integrity, participant privacy, and sample representativeness. Tutorial participants discuss and troubleshoot the unique challenges to and opportunities for designing robust and ethical human-centered AI research. 

While the algorithms used by music streaming services to provide recommendations have often been studied in offline, isolated settings, little research has been conducted studying the nature of their recommendations within the full context of the system itself. This work seeks to compare the level of diversity of the real-world recommendations provided by five of the most popular music streaming services, given the same lists of low-, medium- and high-diversity input items. We contextualized our results by examining the reviews for each of the five services on the Google Play Store, focusing on users’ perception of their recommender systems and the diversity of their output. We found that YouTube Music offered the most diverse recommendations, but the perception of the recommenders was similar across the five services. Consumers had multiple perspectives on the recommendations provided by their music service—ranging from not wanting any recommendations to applauding the algorithm for helping them find new music. 

We conducted a user study with 380 Android users, profiling them according to two key privacy behaviors: the number of apps installed, and the Dangerous permissions granted to those apps. We identified four unique privacy profiles: 1) Privacy Balancers (49.74% of participants), 2) Permission Limiters (28.68%), 3) App Limiters (14.74%), and 4) the Privacy Unconcerned (6.84%). App and Permission Limiters were significantly more concerned about perceived surveillance than Privacy Balancers and the Privacy Unconcerned. App Limiters had the lowest number of apps installed on their devices with the lowest intention of using apps and sharing information with them, compared to Permission Limiters who had the highest number of apps installed and reported higher intention to share information with apps. The four profiles reflect the differing privacy management strategies, perceptions, and intentions of Android users that go beyond the binary decision to share or withhold information via mobile apps. 

This position paper describes our research project to improve middle school students’ use of security “best-practices” in their day-to-day online activities, while enhancing their fundamental understanding of the underlying security principles and math concepts that drive AI and cybersecurity technologies. The project involves the design and implementation of a time- and teacher-friendly learning module that can be readily integrated into existing middle school math curricula. We plan to deploy this module at a high-needs, rural-identifying middle school in South Carolina that serves underrepresented students 

Lack of diversity and high dropout rates among underrepresented students plague the CS discipline. We developed, administered, and validated survey scales measuring social factors that impact the retention and graduation of under-represented CS undergrads at two institutions. Results revealed significant differences between students who identify as men vs. women in terms of computing identity and confidence, and between black and non-black students in terms of familiarity with future opportunities.