Short video recommendations cause more problems for users with ADHD
Quantifying the Engagement Trap: Impact of Short-form Video Recommender Systems on Users with ADHD
Human-Computer InteractionArtificial Intelligence
Summary
Short-form video apps try to keep people watching by showing videos based on their interests. This study looked at how these recommendations affect people with ADHD compared to those without. People with ADHD felt more lost track of time, upset after watching, and emotionally distressed. The authors suggest changes to make these systems fairer and kinder to people with different brain types. They want designs that support everyone better, not just increase watch time.
short-form videorecommender systemsADHDengagementtime blindnesspost-usage regretemotional distressneurodiversityhuman-centered designalgorithmic harm
Authors
Vedad Misirlic, Gregor Mayr, Elisabeth Lex
Abstract
Short-form video platforms use recommender systems to maximize engagement through highly efficient personalized recommendations. However, the impact of these recommendations on users with ADHD compared to users without ADHD remains underexplored. Through this study, we introduce and operationalize the Engagement Trap, illustrating how recommender systems, while successfully optimizing for engagement, disproportionately disadvantage users with ADHD. This stratified study of 302 participants, recruited via the online platform Prolific, compares experiences between participants with and without ADHD. Our results show that while recommendations are perceived as relevant across groups, participants with ADHD report significantly higher levels of time blindness, post-usage regret, and emotional distress when consuming recommendations. Moreover, we collect feedback for several proof-of- concept, theoretical design interventions for neuro-inclusive design principles. These findings provide quantitative evidence of systemic differences in engagement-optimized recommender systems and highlight the unbalanced negative effects and interactions these systems create for participants with ADHD. We argue for neurodiversity-aware, human-centered design approaches that mitigate such algorithmic harms and support more equitable experiences.