A unified ontological and explainable framework for decoding AI risks from news data.

Journal: Scientific reports
Published Date: Jul 15, 2025

Abstract

Artificial intelligence (AI) is rapidly permeating various aspects of human life, raising growing concerns about its associated risks. However, existing research on AI risks often remains fragmented-either limited to specific domains or focused solely on ethical guideline development-lacking a comprehensive framework that bridges macro-level typologies and micro-level instances. To address this gap, we propose an ontological risk model that unifies AI risk representation across multiple scales. Based on this model, we construct an enriched AI risk event database by systematically extracting and structuring raw news data. We then apply a suite of visual analytics methods to extract and summarize key characteristics of AI risk events. Finally, by integrating explainable machine learning techniques, we identify potential driving factors underlying different risk attributes. This study provides a novel, quantitative framework for understanding AI risks, offering both structural insights through ontological modeling and mechanistic interpretations by explainable machine learning.

Authors

  • Chuan Chen
    Information Security Center, State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing 100876, China.
  • Peng Luo
    Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing 100091, PR China.
  • Huilin Zhao
    Department of Radiology, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
  • Mengyi Wei
    Chair of Cartography and Visual Analytics, Technical University of Munich, Munich, Germany.
  • Puzhen Zhang
    Chair of Cartography and Visual Analytics, Technical University of Munich, Munich, Germany.
  • Zihan Liu
    Dept. of Construction Management, School of Civil and Hydraulic Engineering, Huazhong University of Science and Technology, Wuhan, Hubei, 430074, China. Electronic address: liuzihan1996@hust.edu.cn.
  • Liqiu Meng
    Chair of Cartography and Visual Analytics, Technical University of Munich, Munich, Germany.

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