A unified ontological and explainable framework for decoding AI risks from news data
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—lackin...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-07-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-10675-x |
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| _version_ | 1849235433588785152 |
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| author | Chuan Chen Peng Luo Huilin Zhao Mengyi Wei Puzhen Zhang Zihan Liu Liqiu Meng |
| author_facet | Chuan Chen Peng Luo Huilin Zhao Mengyi Wei Puzhen Zhang Zihan Liu Liqiu Meng |
| author_sort | Chuan Chen |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-0d943fc9a14f48c9a6d037f73cad5a42 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-0d943fc9a14f48c9a6d037f73cad5a422025-08-20T04:02:46ZengNature PortfolioScientific Reports2045-23222025-07-0115111910.1038/s41598-025-10675-xA unified ontological and explainable framework for decoding AI risks from news dataChuan Chen0Peng Luo1Huilin Zhao2Mengyi Wei3Puzhen Zhang4Zihan Liu5Liqiu Meng6Chair of Cartography and Visual Analytics, Technical University of MunichChair of Cartography and Visual Analytics, Technical University of MunichDepartment of Land Surveying and Geo-Informatics, Faculty of Construction and Environment, The Hong Kong Polytechnic UniversityChair of Cartography and Visual Analytics, Technical University of MunichChair of Cartography and Visual Analytics, Technical University of MunichChair of Cartography and Visual Analytics, Technical University of MunichChair of Cartography and Visual Analytics, Technical University of MunichAbstract 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.https://doi.org/10.1038/s41598-025-10675-xAI ethicsAI riskOntological modelNews dataExplainable machine learning |
| spellingShingle | Chuan Chen Peng Luo Huilin Zhao Mengyi Wei Puzhen Zhang Zihan Liu Liqiu Meng A unified ontological and explainable framework for decoding AI risks from news data Scientific Reports AI ethics AI risk Ontological model News data Explainable machine learning |
| title | A unified ontological and explainable framework for decoding AI risks from news data |
| title_full | A unified ontological and explainable framework for decoding AI risks from news data |
| title_fullStr | A unified ontological and explainable framework for decoding AI risks from news data |
| title_full_unstemmed | A unified ontological and explainable framework for decoding AI risks from news data |
| title_short | A unified ontological and explainable framework for decoding AI risks from news data |
| title_sort | unified ontological and explainable framework for decoding ai risks from news data |
| topic | AI ethics AI risk Ontological model News data Explainable machine learning |
| url | https://doi.org/10.1038/s41598-025-10675-x |
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