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| Hauptverfasser: | , , , , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| Schlagworte: | |
| Online-Zugang: | https://arxiv.org/abs/2509.20057 |
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| _version_ | 1866910060399034368 |
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| author | KT : Park, Yunjin Yoon, Jungwon Moon, Junhyung Oh, Myunggyo Lee, Wonhyuk Kim, Sujin Kim, Youngchol Kim, Eunmi Park, Hyoungjun Shin, Eunyoung Lee, Wonyoung Lee, Somin Ju, Minwook Noh, Minsung Jeong, Dongyoung Kim, Jeongyeop Park, Wanjin Bae, Soonmin |
| author_facet | KT : Park, Yunjin Yoon, Jungwon Moon, Junhyung Oh, Myunggyo Lee, Wonhyuk Kim, Sujin Kim, Youngchol Kim, Eunmi Park, Hyoungjun Shin, Eunyoung Lee, Wonyoung Lee, Somin Ju, Minwook Noh, Minsung Jeong, Dongyoung Kim, Jeongyeop Park, Wanjin Bae, Soonmin |
| contents | KT developed a Responsible AI (RAI) assessment methodology and risk mitigation technologies to ensure the safety and reliability of AI services. By analyzing the Basic Act on AI implementation and global AI governance trends, we established a unique approach for regulatory compliance and systematically identify and manage all potential risk factors from AI development to operation. We present a reliable assessment methodology that systematically verifies model safety and robustness based on KT's AI risk taxonomy tailored to the domestic environment. We also provide practical tools for managing and mitigating identified AI risks. With the release of this report, we also release proprietary Guardrail : SafetyGuard that blocks harmful responses from AI models in real-time, supporting the enhancement of safety in the domestic AI development ecosystem. We also believe these research outcomes provide valuable insights for organizations seeking to develop Responsible AI. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_20057 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Responsible AI Technical Report KT : Park, Yunjin Yoon, Jungwon Moon, Junhyung Oh, Myunggyo Lee, Wonhyuk Kim, Sujin Kim, Youngchol Kim, Eunmi Park, Hyoungjun Shin, Eunyoung Lee, Wonyoung Lee, Somin Ju, Minwook Noh, Minsung Jeong, Dongyoung Kim, Jeongyeop Park, Wanjin Bae, Soonmin Computation and Language Artificial Intelligence KT developed a Responsible AI (RAI) assessment methodology and risk mitigation technologies to ensure the safety and reliability of AI services. By analyzing the Basic Act on AI implementation and global AI governance trends, we established a unique approach for regulatory compliance and systematically identify and manage all potential risk factors from AI development to operation. We present a reliable assessment methodology that systematically verifies model safety and robustness based on KT's AI risk taxonomy tailored to the domestic environment. We also provide practical tools for managing and mitigating identified AI risks. With the release of this report, we also release proprietary Guardrail : SafetyGuard that blocks harmful responses from AI models in real-time, supporting the enhancement of safety in the domestic AI development ecosystem. We also believe these research outcomes provide valuable insights for organizations seeking to develop Responsible AI. |
| title | Responsible AI Technical Report |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2509.20057 |