Productively Wrong: How Auditing Open Profiles Enhances Interest Awareness and Reflection in Movie Recommenders

Open User Models (OUM) leverage natural language to help users scrutinize their interests, moving beyond opaque behavioral logs to establish a ground truth of user preference. Building on this foundation, we introduce an LLM-generated open profile for a live movie recommender system that presents editable, personalized interest summaries. Unlike static profiles, this design invites users to directly inspect, audit, and reflect on the system’s inferences. In an eight-week field deployment with 1,775 active users, we found that participants actively engaged in preference construction, specifically pruning inaccurate dislikes and refining nuanced tastes. Our results show that this profile auditing process encourages high-efficiency maintenance behavior, leading to significantly increased reflection activity and engagement. We advocate for a Productively Wrong design strategy, where exposing mild AI inaccuracies serves as a catalyst to stimulate user intervention, fostering transparent systems where users actively curate their digital identity.