Understanding Users' Perception Towards Automated Personality Detection with Group-specific Behavioral Data

Abstract

Thanks to advanced sensing and logging technology, automatic personality assessment (APA) with users' behavioral data in the workplace is on the rise. While previous work has focused on building APA systems with high accuracy, little research has attempted to understand users' perception towards APA systems. To fill this gap, we take a mixed-methods approach: we (1) designed a survey (n=89) to understand users'social workplace behavior both online and offline and their privacy concerns; (2) built a research probe that detects personality from online and offline data streams with up to 81.3% accuracy, and deployed it for three weeks in Korea (n=32); and (3) conducted post-interviews (n=9). We identify privacy issues in sharing data and system-induced change in natural behavior as important design factors for APA systems. Our findings suggest that designers should consider the complex relationship between users' perception and system accuracy for a more user-centered APA design.