Responsible AI Technical Report

• URL: http://arxiv.org/abs/2509.20057v2
• Date: Mon, 29 Sep 2025 05:30:21 GMT
• Title: Responsible AI Technical Report
• Authors: KT, :, Soonmin Bae, Wanjin Park, Jeongyeop Kim, Yunjin Park, Jungwon Yoon, Junhyung Moon, Myunggyo Oh, Wonhyuk Lee, Dongyoung Jung, Minwook Ju, Eunmi Kim, Sujin Kim, Youngchol Kim, Somin Lee, Wonyoung Lee, Minsung Noh, Hyoungjun Park, Eunyoung Shin,
• Abstract summary: KT developed a Responsible AI (RAI) assessment methodology and risk mitigation technologies to ensure the safety and reliability of AI services.<n>We present a reliable assessment methodology that verifies model safety and robustness based on KT's AI risk taxonomy tailored to the domestic environment.<n>We also provide practical tools for managing and mitigating identified AI risks.
• Score: 2.855225489126354
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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.

Related papers

• Frontier AI Risk Management Framework in Practice: A Risk Analysis Technical Report v1.5 [61.787178868669265]
  This technical report presents an updated and granular assessment of five critical dimensions: cyber offense, persuasion and manipulation, strategic deception, uncontrolled AI R&D, and self-replication.<n>This work reflects our current understanding of AI frontier risks and urges collective action to mitigate these challenges.
  arXiv  Detail & Related papers  (2026-02-16T04:30:06Z)
• Never Compromise to Vulnerabilities: A Comprehensive Survey on AI Governance [211.5823259429128]
  We propose a comprehensive framework integrating technical and societal dimensions, structured around three interconnected pillars: Intrinsic Security, Derivative Security, and Social Ethics.<n>We identify three core challenges: (1) the generalization gap, where defenses fail against evolving threats; (2) inadequate evaluation protocols that overlook real-world risks; and (3) fragmented regulations leading to inconsistent oversight.<n>Our framework offers actionable guidance for researchers, engineers, and policymakers to develop AI systems that are not only robust and secure but also ethically aligned and publicly trustworthy.
  arXiv  Detail & Related papers  (2025-08-12T09:42:56Z)
• Integrating Generative AI in Cybersecurity Education: Case Study Insights on Pedagogical Strategies, Critical Thinking, and Responsible AI Use [0.0]
  This study presents a structured framework for integrating GenAI tools into cybersecurity education.<n>The implementation strategy followed a two-stage approach, embedding GenAI within tutorial exercises and assessment tasks.<n>Findings indicate that AI-assisted learning significantly enhanced students' ability to evaluate security policies, refine risk assessments, and bridge theoretical knowledge with practical application.
  arXiv  Detail & Related papers  (2025-02-21T10:14:07Z)
• Position: Mind the Gap-the Growing Disconnect Between Established Vulnerability Disclosure and AI Security [56.219994752894294]
  We argue that adapting existing processes for AI security reporting is doomed to fail due to fundamental shortcomings for the distinctive characteristics of AI systems.<n>Based on our proposal to address these shortcomings, we discuss an approach to AI security reporting and how the new AI paradigm, AI agents, will further reinforce the need for specialized AI security incident reporting advancements.
  arXiv  Detail & Related papers  (2024-12-19T13:50:26Z)
• Trustworthy, Responsible, and Safe AI: A Comprehensive Architectural Framework for AI Safety with Challenges and Mitigations [15.946242944119385]
  AI Safety is an emerging area of critical importance to the safe adoption and deployment of AI systems.<n>Our goal is to promote advancement in AI safety research, and ultimately enhance people's trust in digital transformation.
  arXiv  Detail & Related papers  (2024-08-23T09:33:48Z)
• EARBench: Towards Evaluating Physical Risk Awareness for Task Planning of Foundation Model-based Embodied AI Agents [53.717918131568936]
  Embodied artificial intelligence (EAI) integrates advanced AI models into physical entities for real-world interaction.<n>Foundation models as the "brain" of EAI agents for high-level task planning have shown promising results.<n>However, the deployment of these agents in physical environments presents significant safety challenges.<n>This study introduces EARBench, a novel framework for automated physical risk assessment in EAI scenarios.
  arXiv  Detail & Related papers  (2024-08-08T13:19:37Z)
• Responsible AI Question Bank: A Comprehensive Tool for AI Risk Assessment [18.966590454042272]
  The study introduces our Responsible AI (RAI) Question Bank, a comprehensive framework and tool designed to support diverse AI initiatives.<n>By integrating AI ethics principles such as fairness, transparency, and accountability into a structured question format, the RAI Question Bank aids in identifying potential risks.
  arXiv  Detail & Related papers  (2024-08-02T22:40:20Z)
• Towards Guaranteed Safe AI: A Framework for Ensuring Robust and Reliable AI Systems [88.80306881112313]
  We will introduce and define a family of approaches to AI safety, which we will refer to as guaranteed safe (GS) AI. The core feature of these approaches is that they aim to produce AI systems which are equipped with high-assurance quantitative safety guarantees. We outline a number of approaches for creating each of these three core components, describe the main technical challenges, and suggest a number of potential solutions to them.
  arXiv  Detail & Related papers  (2024-05-10T17:38:32Z)
• AI Hazard Management: A framework for the systematic management of root causes for AI risks [0.0]
  This paper introduces the AI Hazard Management (AIHM) framework. It provides a structured process to systematically identify, assess, and treat AI hazards. It builds upon an AI hazard list from a comprehensive state-of-the-art analysis.
  arXiv  Detail & Related papers  (2023-10-25T15:55:50Z)
• Responsible Design Patterns for Machine Learning Pipelines [10.184056098238765]
  AI ethics involves applying ethical principles to the entire life cycle of AI systems. This is essential to mitigate potential risks and harms associated with AI, such as biases. To achieve this goal, responsible design patterns (RDPs) are critical for Machine Learning (ML) pipelines.
  arXiv  Detail & Related papers  (2023-05-31T15:47:12Z)
• Quantitative AI Risk Assessments: Opportunities and Challenges [7.35411010153049]
  Best way to reduce risks is to implement comprehensive AI lifecycle governance.<n>Risks can be quantified using metrics from the technical community.<n>This paper explores these issues, focusing on the opportunities, challenges, and potential impacts of such an approach.
  arXiv  Detail & Related papers  (2022-09-13T21:47:25Z)
• Toward Trustworthy AI Development: Mechanisms for Supporting Verifiable Claims [59.64274607533249]
  AI developers need to make verifiable claims to which they can be held accountable. This report suggests various steps that different stakeholders can take to improve the verifiability of claims made about AI systems. We analyze ten mechanisms for this purpose--spanning institutions, software, and hardware--and make recommendations aimed at implementing, exploring, or improving those mechanisms.
  arXiv  Detail & Related papers  (2020-04-15T17:15:35Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.