디지털융복합연구 (Journal of Digital Convergence)

  • 제20권2호
  • /
  • Pages.269-275
  • /
  • 2022
  • /
  • 2713-6434(pISSN)
  • /
  • 2713-6442(eISSN)
한국디지털정책학회 (The Society of Digital Policy and Management)
권호 표지
DOI QR코드

DOI QR Code

인공지능 윤리 인식에 대한 데이터 분석 및 시각화 연구 -대화형 인공지능 서비스 '이루다'를 중심으로-

A Data Analysis and Visualization of AI Ethics -Focusing on the interactive AI service 'Lee Luda'-

  • 이수련 (서울여자대학교 정보보호학과) ;
  • 최은정 (서울여자대학교 정보보호학과)
  • Lee, Su-Ryeon (Department of Information Security, Seoul Women's University) ;
  • Choi, Eun-Jung (Department of Information Security, Seoul Women's University)
  • 투고 : 2021.12.31
  • 심사 : 2022.02.20
  • 발행 : 2022.02.28
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

사람을 대상으로 하는 인공지능 서비스가 증가하면서 인공지능에서도 윤리적 토대 위에서 이루어져야 한다는 사회적 요구가 증가하고 있다. 이러한 흐름에 따라 정부와 기업에서는 인공지능 윤리와 관련된 정책, 규범 등을 마련하고 있다. 합리적인 정책, 규범을 마련하기 위해서는 대중들이 가지고 있는 인식을 파악하는 것이 첫 번째 단계이다. 본 논문에서는 인공지능과 윤리에 대한 대중들의 인식을 파악하기 위해 소셜데이터와 뉴스 댓글을 수집하고 관심도 분석, 감성 분석, 담론 분석 수행 후 시각화하였다. 분석 결과, "인공지능 윤리"에 대한 관심도와 '인공지능" 호감도는 반비례하는 상관관계를 보여주었다. 담론분석 결과로, 가장 큰 이슈가 "개인정보 유출"이었고 학습 데이터의 오염 및 편향 문제와 컴퓨터로 만들어진 인공지능에게 법인격을 부여해야 하는지에 대한 담론도 보여주었다. 본 연구가 인공지능 윤리 규범, 정책을 마련할 때 대중들의 인식을 파악할 수 있는 자료로 활용될 수 있을 것이다.

As artificial intelligence services targeting humans increase, social demands are increasing that artificial intelligence should also be made on an ethical basis. Following this trend, the government and businesses are preparing policies and norms related to artificial intelligence ethics. In order to establish reasonable policies and norms, the first step is to understand the public's perceptions. In this paper, social data and news comments were collected and analyzed to understand the public's perception related to artificial intelligence and ethics. Interest analysis, emotional analysis, and discourse analysis were performed and visualized on the collected datasets. As a result of the analysis, interest in "artificial intelligence ethics" and "artificial intelligence" favorability showed an inversely proportional correlation. As a result of discourse analysis, the biggest issue was "personal information leakage," and it also showed a discourse on contamination and deflection of learning data and whether computer-made artificial intelligence should be given a legal personality. This study can be used as data to grasp the public's perception when preparing artificial intelligence ethical norms and policies.

키워드

과제정보

This work was supported by a research grant from Seoul Women's University(2021-0235)

참고문헌

  1. K. H. Kim. (2021). Current Status and Implications of Artificial Intelligence (AI) Introduction by Major Industries. Jincheon : KISDI
  2. Personal Information Protection Commission, (2021). Case of corrective action for violation of the Personal Information Protection Act. no 2021-007-072
  3. K. S. Kwang. (2021. 03). The problem left by the chatbot "Iruda" in our society: Mounting a human rights manual on artificial intelligence. Culture and science, 105, 183-198.
  4. Ministry of Science and ICT. (2021.05.13.). The government has come up with a reliable AI realization strategy centered on people. https://www.korea.kr/news/policyNewsView.do?newsId=148887381
  5. NAVER. (2021.02). AI ethical rules. https://www.navercorp.com/value/aiCodeEthics
  6. W. Y. Lee. (2011). Seoul National University Law. The Law Research Institute Seoul National University, 52(4), 125-168 UCI : G704-002133.2011.52.4.003
  7. Y, H. Kim. (2020. Oct). Understanding and application of social network analysis technology. KIPA Research Forum, 34, 58-68
  8. J. H. Lee, J. M. Moon & Y. S. Jang. (2017). Analysis of 2018 PyeongChang Olympic keywords using social network big data analysis. Korean Journal of Sport Management, 22(6), 73-89 DOI : 10.31308/KSSM.22.6.5
  9. W. G. Kang, E. S. Ko, H. R. Lee & J. N. Kim. (2018). A Study of the Consumer Major Perception of Packaging Using Big Data Analysis -Focusing on Text Mining and Semantic Network Analysis-. Journal of the Korea Convergence Society. 9(4). 15-22 DOI : 10.15207/JKCS.2018.9.4.015
  10. J. Devlin, M. W. Chand, K. Lee, K. Toutanova. (2018). Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805.
  11. J. S. Lee. (2013). A Study on Visualizing Method and Expression of Information Design for Big Data. Journal of Basic Design & Art, 14(3), 259-269 UCI : G704-001069.2013.14.3.026
  12. G. S. Choe, Y. G. Ham & S. H. Kim. (2013) Bigdata Visualization. KSCI Review, 21(1), 33-43
  13. Friendly, M. (2008). A brief history of data visualization. In Handbook of data visualization (pp. 15-56). Springer, Berlin, Heidelberg. DOI : 10.1007/978-3-540-33037-0_2
  14. Veale. M. & Borgesius. F. Z. (2021). Demystifying the Draft EU Artificial Intelligence Act-Analysing the good, the bad, and the unclear elements of the proposed approach. Computer Law Review International, 22(4), 97-112. DOI : 10.9785/cri-2021-220402
  15. Borgatti, S.P., Everett, M.G. and Freeman, L.C. (2002). Ucinet for Windows: Software for Social Network Analysis. Harvard, MA: Analytic Technologies.
  16. J. H. Park. (2021). An Analysis of Perception on Law and Policy regarding the 4th Industrial Revolution. Korean Journal of Law & Society, 66, 175-204 DOI : 10.33446/KJLS.56.3
  17. Y. H Ko & C. S Leem. (2021). The Influence of AI Technology Acceptance and Ethical Awareness towards Intention to Use. Journal of Digital Convergence, 19(3), 217-225 DOI : 10.14400/JDC.2021.19.3.217
  18. G. S. Kim & Y. J. Shin. (2021). A Cross-Sectional Study of Artificial Intelligence Ethics Awareness - Preparation for Climate Change Education Using Artificial Intelligence -. Jounral of Energy and Climate Change Education (JECCE), 11(1), 27-36 DOI : 10.22368/ksecce.2021.11.1.27
  19. D. G. Kim & S. Y. Shin. (2021). Comparing the Results of Big-Data with Questionnaire Survey. Journal of the Korea Institute of Information and Communication Engineering, 20(11), 2027-2032 DOI : 10.6109/jkiice.2016.20.11.2027