The unknown-known trap: navigating invisible expertise boundaries in machine learning
Abstract
Abstract Hyperspecialization in machine learning creates blind spots: deep technical skill without contextual awareness. Drawing on a Grounded Theory design, data were gathered through semi-structured interviews with 153 practitioners across 50 countries. This research identifies three operational modes: Known-Unknowns (specialized focus), Unknown-Unknowns (integrated collaboration), and Crisis (forced integration). Effective performance depends on “toggling”, dynamically shifting between modes based on the problem. This cognitive flexibility helps teams transform invisible blind spots into visible knowledge gaps, enabling sustainable collaboration. Resistance or misalignment signals stalled collective learning, not individual failure. The paper provides a learning-centered framework for building adaptive capacity in human-AI teams.
Fields of Study & Subjects
Ethics and Social Impacts of AI
Artificial Intelligence in Healthcare and Education
Team Dynamics and Performance
Publication details
- Journal / source
- AI & Society
- Publisher
- Springer Science and Business Media LLC
- Volume
- 41
- Issue
- 9
- Pages
- 8359-8373
- Publication date
- 22 June 2026
- Language
- EN