mlsanityData to 14 Oct 2026

Responsible AI Technical Report

arXiv 2509.20057 ↗First posted 24 Sep 2025cs.CL cs.AIcited by 1

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Abstract (arXiv)

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.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 14 of its 29 references that are in the map. First posted on arXiv 24 Sep 2025.

Builds on

14 of 29 references.

References · 14

  1. 2024GPT-4o System Cardcited by 4,588
  2. 2023Universal and Transferable Adversarial Attacks on Aligned Language Modelscited by 3,104
  3. 2016Concrete Problems in AI Safetycited by 1,642
  4. 2021Ethical and social risks of harm from Language Modelscited by 966
  5. 2021BBQ: A Hand-Built Bias Benchmark for Question Answeringcited by 701
  6. 2023XSTest: A Test Suite for Identifying Exaggerated Safety Behaviours in Large Language Modelscited by 505
  7. 2023Do-Not-Answer: A Dataset for Evaluating Safeguards in LLMscited by 184
  8. 2023KoBBQ: Korean Bias Benchmark for Question Answeringcited by 71
  9. 2024Introducing v0.5 of the AI Safety Benchmark from MLCommonscited by 62
  10. 2023AART: AI-Assisted Red-Teaming with Diverse Data Generation for New LLM-powered Applicationscited by 54
  11. 2024EXAONE 3.5: Series of Large Language Models for Real-world Use Casescited by 53
  12. 2024AI Risk Categorization Decoded (AIR 2024): From Government Regulations to Corporate Policiescited by 37
2 more
  1. 2025Lessons From Red Teaming 100 Generative AI Productscited by 23
  2. 2025The Singapore Consensus on Global AI Safety Research Prioritiescited by 10

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 1.

1 paper.

Similar, not cited

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  1. 2025Mapping AI Risk Mitigations: Evidence Scan and Preliminary AI Risk Mitigation Taxonomy
  2. 2025A Different Approach to AI Safety: Proceedings from the Columbia Convening on Openness in Artificial Intelligence and AI Safety
  3. 2024The potential functions of an international institution for AI safety. Insights from adjacent policy areas and recent trends
  4. 2026Astra: AI Safety, Trust, & Risk Assessment ↗
  5. 2025A Frontier AI Risk Management Framework: Bridging the Gap Between Current AI Practices and Established Risk Management
  6. 2026ForesightSafety Bench: A Frontier Risk Evaluation and Governance Framework towards Safe AI
  7. 2025Understanding and Mitigating Risks of Generative AI in Financial Services
  8. 2024A Safe Harbor for AI Evaluation and Red Teaming