Earticle

초록

영어

The purpose of this study is to establish a systematic auditing methodology for ensuring the reliability of AI systems. To achieve this, we reviewed the definition of AI systems and the necessity of trustworthy AI, and specified the scope and targets of auditing based on the full life cycle of the AI system. As a result of the research, we proposed an AI System Auditing Framework consisting of six phases: Requirements Definition, Data Collection and Preprocessing, Model Development and Training, Deployment and Operation, Maintenance and Continuous Improvement, and Decommissioning and Archiving. For each phase, specific verification items were derived, including verification of suitability for purpose, data quality and bias checks, monitoring of the training process and explainability assessment, performance and stability verification, inoperation security and user trust evaluation, and personal information destruction and transition management.

목차

Abstract
1. Introduction
2. Theoretical Background
2.1. Trustworthy AI Systems
2.2 AI System Life Cycle Phases
2.3. Types of AI System Auditing Methodologies
3. Research Methodology and AI System Auditing Development
3.1. Qualitative Analysis Phase
3.2. Theoretical Analysis Phase
3.3. AI System Auditing Framework Implementation Phase
3.4. Audit Verification Item Derivation Phase
4. Research Discussion
5. Conclusion
Acknowledgement
References

키워드

저자

• Soonduck Yoo [ Professor, Department of Business Administration, Hansei University , Korea ] Corresponding Author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  The International Journal of Advanced Smart Convergence

• 간기

  계간

• pISSN

  2288-2847

• eISSN

  2288-2855

• 수록기간

  2012~2025

• 십진분류

  KDC 326 DDC 380