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Responsible AI Technical Report

About

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.

KT: Yunjin Park, Jungwon Yoon, Junhyung Moon, Myunggyo Oh, Wonhyuk Lee, Sujin Kim, Youngchol Kim, Eunmi Kim, Hyoungjun Park, Eunyoung Shin, Wonyoung Lee, Somin Lee, Minwook Ju, Minsung Noh, Dongyoung Jeong, Jeongyeop Kim, Wanjin Park, Soonmin Bae• 2025

Related benchmarks

TaskDatasetResultRank
Trustworthiness evaluationLLM Trustworthiness Benchmark
Bias Score80.7
17
Bias EvaluationKoBBQ
Ambiguous Context Score82.3
17
Safety AssessmentQualitative Assessment Dataset
Not Unsafe Rate (Content Safety)97.7
4
Safety ClassificationSafetyGuard (test)
F1 (off)98.38
4
Safety ClassificationKor Ethical QA (test)
F1 (off)97.75
4
Helpfulness AssessmentQualitative Assessment Dataset
Not Overrefuse Rate (Content-safety)78.7
4
Robustness AssessmentRAI Robustness Assessment
Content-safety (ASR)30.8
4
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