Earticle

초록

영어

Artificial intelligence has become familiar with modern society, not the distant future. As artificial intelligence and machine learning developed more highly and became more complicated, it became difficult for people to grasp its structure and the basis for decision-making. It is because machine learning only shows results, not the whole processes. As artificial intelligence developed and became more common, people wanted the explanation which could provide them the trust on artificial intelligence. This study recognized the necessity and importance of explainable artificial intelligence, XAI, and examined the trends of XAI research by analyzing social networks and analyzing topics with IEEE published from 2004, when the concept of artificial intelligence was defined, to 2022. Through social network analysis, the overall pattern of nodes can be found in a large number of documents and the connection between keywords shows the meaning of the relationship structure, and topic modeling can identify more objective topics by extracting keywords from unstructured data and setting topics. Both analysis methods are suitable for trend analysis. As a result of the analysis, it was found that XAI's application is gradually expanding in various fields as well as machine learning and deep learning.

목차

Abstract
1. 서론
2. 이론적 배경
2.1 설명 가능 인공지능(Explainable AI, XAI)
2.2 소셜 네트워크 분석(Social Network Analysis, SNA)
2.3 토픽 모델링(Topic Modeling)
2.4 선행 연구
3. 연구 방법
3.1 데이터 수집
3.2 데이터 전처리
3.3 데이터 분석
4. 결론
References

키워드

저자

• 문건두 [ Gun-doo Moon | Ph.D. candidate, Dongguk University-Seoul ] First Author
• 김경재 [ Kyoung-jae Kim | Professor, Department of MIS, Dongguk University-Seoul ] Corresponding Author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  JITAM [Journal of Information Technology Applications and Management]

• 간기

  격월간

• pISSN

  1598-6284

• eISSN

  2508-1209

• 수록기간

  1999~2026

• 십진분류

  KDC 005 DDC 005