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Development and Validation of Ethical Awareness Scale for AI Technology

인공지능기술 윤리성 인식 척도개발 연구

  • Kim, Doeyon (Graduate School of Education, Environmental Education, Seoul National University) ;
  • Ko, Younghwa (Graduate School of Convergence Technology Management Engineering, Yonsei University)
  • 김도연 (서울대학교 사범대학 협동과정 환경교육전공) ;
  • 고영화 (연세대학교 일반대학원 융합기술경영공학과)
  • Received : 2021.11.16
  • Accepted : 2022.01.20
  • Published : 2022.01.28

Abstract

The purpose of this study is to develop and validate a scale to measure the ethical awareness of users who accept artificial intelligence technology or service. To this end, the constructs and properties of AI ethics were identified through literature analysis on AI ethics. Reliability and validity were assessed through a preliminary survey(N=273), after conducting an open-type survey to men and women(N=133) in 10s to 70s nationwide, extracting the first questions, and reviewing them by experts. The results of an online survey conducted on men and women(N=500) were refined by confirmatory factor analysis. Finally, an AI technology ethics scale was developed. The AI technology ethics awareness scale was developed with 16 questions in total of 4 factors (transparency, safety, fairness, accountability) so that general awareness of ethics related to AI technology can be measured by detailed factors. In addition, through follow-up research, it will be possible to reveal the relationship with measurement variables in various fields by using the ethical awareness scale of artificial intelligence technology.

본 연구의 목적은 인공지능 기술 또는 서비스를 수용하는 사용자의 윤리성 인식을 측정하기 위한 척도 개발 및 타당화에 있다. 이를 위해 인공지능 윤리성 관련 문헌 분석을 통해 구성개념 및 속성을 확인하였다. 전국의 10대-70대 남녀 133명(개방형 설문:1차 문항), 273명(예비조사:2차 문항), 500명(본조사:최종 문항)을 대상으로 실시한 온라인 설문조사 결과를 확인적 요인분석에 의해 정제하여 최종적으로 인공지능기술 윤리성 척도를 개발하였다. 인공지능기술 윤리성 인식 척도는 총 4개 요인(투명성, 안전성, 공정성, 책임성) 16개 문항으로 개발하여 일반적인 인공지능기술 관련 윤리성 인식을 세부 요인별로 측정할 수 있도록 하였다. 개발된 척도를 활용하여 다양한 분야의 측정 변인들과의 관련성을 밝힐 수 있을 것이며, 인공지능기술 발전의 초기 단계에서 윤리성 인식을 높이기 위한 기초 데이터를 제공하는 데 중요한 역할을 할 것으로 기대한다.

Keywords

References

  1. L. Rothenberger, B. Fabian, E. Arunov. (2019). Relevance of Ethical Guidelines for Artificial Intelligence - A Survey and Evaluation, In Proceedings of the 27 th European Conference on Information Systems(ECIS),1-11.
  2. Junngi Economy. (2019). How to ensure transparency and accountability in AI decisions, 2019. 7. 25. Retrieved from http://www.junggi.co.kr/article/articleView.html?no=23729&totalSearchField=&totalSearchText=%C0%CC%C3%A2%C8%A3&totalSearchDate1=2019-01-01&totalSearchDate2=2019-12-31&totalSearchDGubun=4&prevPagename=searchMain.html&page=12
  3. The Arm. (2020). AI Today, AI Tomorrow; The Arm 2020 Global AI Survey.
  4. Y.H. Ko, C.S. Leam. (2021). The Influence of AI Technology Acceptance and Ethical Awareness towards Intention to Use. Journal of Digital Convergence, 19(3), 217-225. https://doi.org/10.14400/JDC.2021.19.3.217
  5. National Information Society Agency. (2019). Guidelines for Artificial Intelligence Ethics-Cases of Japan and EU, NIA.
  6. N. Cointe, G. Bonnet, O. Boissier. (2016). Ethical Judgment of Agents' Behaviors in Multi-agent Systems, AAMAS, 1106-1114.
  7. K. Siau, W. Wang. (2020). Artificial Intelligence(AI) Ethics: Ethics of AI and Ethical AI, Journal of Database Management, 31(2), 74-87. https://doi.org/10.4018/jdm.2020040105
  8. A. Jobin, M. Ienca, E. Vayena. (2019). The Global Landscape of AI Ethics Guidelines, Nature Machine Intelligence, 1(September), 389-399. https://doi.org/10.1038/s42256-019-0088-2
  9. R.B. Kline. (1998). Principles and practice of structural equation modeling. New York: Guilford.
  10. R.B. Kline. (2005). Principles and practice of structural equation modeling (2nd ed.). New York: Guilford.
  11. C. Fornell, D. F. Larcker. (1981). Evaluating Structural Equation Models with Unobservable Variables and Measurement Error. Journal of Marketing Research, 18(1), 39-50. https://doi.org/10.2307/3151312
  12. J. Anderson, D. W. Gerbing. (1988). Structural Equation Modeling in Practice: A Review and Recommended Two-Step Approach. Psychological Bulletin, 103(3), 411-423. https://doi.org/10.1037/0033-2909.103.3.411
  13. T. Hagendorff. (2020). The ethics of AI ethics: An evaluation of guidelines. Minds and Machines, 30(1), 99-120. https://doi.org/10.1007/s11023-020-09517-8
  14. J. Whittlestone, R. Nyrup, A. Alexandrova, S. Cave (2019). The role and limits of principles in AI ethics: towards a focus on tensions. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society, 195-200.
  15. J. Morley, L. Floridi, L. Kinsey, A. Elhalal. (2021). From what to how: an initial review of publicly available AI ethics tools, methods and research to translate principles into practices. In Ethics, Governance, and Policies in Artificial Intelligence, pp. 153-183. Springer, Cham.
  16. D. Schiff, J. Biddle, J. Borenstein, K. Laas. (2020, February). What's next for ai ethics, policy, and governance? a global overview. In Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, 153-158.
  17. M. Coeckelbergh. (2020). AI Ethics. The MIT Press.
  18. H. Kim. (2019). Artificial Intelligence and Ethics, Communication Books.
  19. H.S. Ko, H.B. Jung, D. H. Park. (2019). Artificial Intelligence and Discrimination. Justice, (171), 199-277.
  20. S.P. Cho. (2017). The Study on threats of information security and their solutions in the fourth industrial revolution. Korean Security Journal, (51), 11-35.
  21. E.K. Lee. (2016). Exploring Educational Possibilities for Posthumans. Theology and World, (86), 83-111.
  22. R. Hanna, E. Kazim. (2021). Philosophical foundations for digital ethics and AI Ethics: a dignitarian approach. AI and Ethics, 1(4), 405-423. https://doi.org/10.1007/s43681-021-00040-9
  23. C. H., Choi, Y.W. Yoo. (2017). The Study on the Comparative Analysis of EFA and CFA. Journal of Digital Convergence, 15(10),103-111. https://doi.org/10.14400/JDC.2017.15.10.103