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<p>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.</p>

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The unknown-known trap: navigating invisible expertise boundaries in machine learning

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年6月22日

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  • Giulio Toscani

摘要

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.