Search for: All records

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.