한국인공지능학회지 (Korean Journal of Artificial Intelligence)

한국인공지능학회 (Korea Artificial Intelligence Association)
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Exploring AI Principles in Global Top 500 Enterprises: A Delphi Technique of LDA Topic Modeling Results

  • Hyun BAEK (Department of AI and Public Policy, Seoul National University Of Science And Technology)
  • 투고 : 2023.04.05
  • 심사 : 2023.05.25
  • 발행 : 2023.06.30
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초록

Artificial Intelligence (AI) technology has already penetrated deeply into our daily lives, and we live with the convenience of it anytime, anywhere, and sometimes even without us noticing it. However, because AI is imitative intelligence based on human Intelligence, it inevitably has both good and evil sides of humans, which is why ethical principles are essential. The starting point of this study is the AI principles for companies or organizations to develop products. Since the late 2010s, studies on ethics and principles of AI have been actively published. This study focused on AI principles declared by global companies currently developing various products through AI technology. So, we surveyed the AI principles of the Global 500 companies by market capitalization at a given specific time and collected the AI principles explicitly declared by 46 of them. AI analysis technology primarily analyzed this text data, especially LDA (Latent Dirichlet Allocation) topic modeling, which belongs to Machine Learning (ML) analysis technology. Then, we conducted a Delphi technique to reach a meaningful consensus by presenting the primary analysis results. We expect to provide meaningful guidelines in AI-related government policy establishment, corporate ethics declarations, and academic research, where debates on AI ethics and principles often occur recently based on the results of our study.

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참고문헌

  1. Amin, M. H., Mohamed, E. K. A., & Elragal, A. (2020). Corporate disclosure via social media: a data science approach. Online Information Review, 44(1), 278-298. https://doi.org/10.1108/OIR-03-2019-0084 
  2. Baker-Brunnbauer, J. (2021). Management perspective of ethics in artificial intelligence. AI and Ethics, 1(2), 173-181. https://doi.org/10.1007/s43681-020-00022-3 
  3. Benites-Lazaro, L. L., Giatti, L., & Giarolla, A. (2018). Sustainability and governance of sugarcane ethanol companies in Brazil: Topic modeling analysis of CSR reporting. Journal of Cleaner Production, 197(Part 1), 583-591.  https://doi.org/10.1016/j.jclepro.2018.06.212
  4. Birkstedt, T., & Mantymaki, M. (2021). From Ethical AI Principles to Governed AI. https://digitalstrategy.ec.europa.eu/en/library/proposal-regulation-layingdown-harmonised-rules-artificial-intelligence-
  5. Blei, D., Ng, A., & 1. Jordan, M. (2001). Latent Dirichlet Allocation. In T. Dietterich, S. Becker, & Z. Ghahramani (Eds.), Advances in Neural Information Processing Systems (Vol. 14). MIT Press. https://proceedings.neurips.cc/paper/2001/file/296472c9542ad4d4788d543508116cbc-Paper.pdf 
  6. Blei, D. M., Ng, A. Y., & Edu, J. B. (2003). Latent Dirichlet Allocation Michael I. Jordan. In Journal of Machine Learning Research (Vol. 3). 
  7. Chen, Y., Zhang, H., Liu, R., Ye, Z., & Lin, J. (2019). Experimental explorations on short text topic mining between LDA and NMF based Schemes. Knowledge-Based Systems, 163, 1-13. https://doi.org/10.1016/J.KNOSYS.2018.08.011 
  8. Divakaran, A., Sridhar, A., & Srinivasan, R. (2022). Broadening AI Ethics Narratives: An Indic Art View. http://arxiv.org/abs/2204.03789 
  9. Eitel-Porter, R. (2021). Beyond the promise: implementing ethical AI. AI and Ethics, 1(1), 73-80. https://doi.org/10.1007/s43681-020-00011-6 
  10. Floridi, L., & Cowls, J. (2019). A Unified Framework of Five Principles for AI in Society. Harvard Data Science Review. https://doi.org/10.1162/99608f92.8cd550d1 
  11. Greene, D., Hoffmann, A. L., & Stark, L. (2019). Better, Nicer, Clearer, Fairer: A Critical Assessment of the Movement for Ethical Artificial Intelligence and Machine Learning. https://hdl.handle.net/10125/59651 
  12. Griffiths, T., Jordan, M., Tenenbaum, J., & Blei, D. (2003). Hierarchical Topic Models and the Nested Chinese Restaurant Process. In S. Thrun, L. Saul, & B. Scholkopf (Eds.), Advances in Neural Information Processing Systems (Vol. 16). MIT Press. https://proceedings.neurips.cc/paper/2003/file/7b41bfa508580 6dfa24b8c9de0ce567f-Paper.pdf 
  13. Grosz, B. J., & Stone, P. (2018). A Century Long Commitment to Assessing Artificial Intelligence and its Impact on Society. https://arxiv.org/abs/1808.07899 
  14. Hagendorff, T. (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 
  15. Hsu, C.-C., & Sandford, B. A. (2007). The Delphi Technique: Making Sense of Consensus. Practical Assessment, Research, and Evaluation, 12, 10. https://doi.org/10.7275/pdz9-th90 
  16. Ibanez, J. C., & Olmeda, M. V. (2022). Operationalising AI ethics: how are companies bridging the gap between practice and principles? An exploratory study. AI and Society, 37(4), 1663-1687. https://doi.org/10.1007/s00146-021-01267-0 
  17. Jobin, A., Ienca, M., & Vayena, E. (2019). Artificial Intelligence: the global landscape of ethics guidelines. 
  18. Kazim, E., & Koshiyama, A. S. (2021). A high-level overview of AI ethics. Patterns, 2(9), 100314. https://doi.org/10.1016/J.PATTER.2021.100314 
  19. Kelley, S. (2022). Employee Perceptions of the Effective Adoption of AI Principles. Journal of Business Ethics, 178(4), 871-893. https://doi.org/10.1007/s10551-022-05051-y 
  20. Korfiatis, N., Stamolampros, P., Kourouthanassis, P., & Sagiadinos, V. (2019). Measuring service quality from unstructured data: A topic modeling application on airline passengers' online reviews. Expert Systems with Applications, 116, 472-486.  https://doi.org/10.1016/j.eswa.2018.09.037
  21. Layman, L., Nikora, A. P., Meek, J., & Menzies, T. (2016). Topic Modeling of NASA Space System Problem Reports: Research in Practice. 2016 IEEE/ACM 13th Working Conference on Mining Software Repositories (MSR), 303-314. 
  22. Mashayekhi, B., Vanaki, A. S., & Sivandian, M. (2021). A Review of Internal Audit Research Using Topic Modeling. ھای بررسی 358.-296 ,(2)28 ,حسابداری و حسابرسی 
  23. Megahed, F. M., Chen, Y.-J., Ferris, J. A., Knoth, S., & JonesFarmer, L. A. (2023). How Generative AI models such as ChatGPT can be (Mis)Used in SPC Practice, Education, and Research? An Exploratory Study. http://arxiv.org/abs/2302.10916 
  24. Ning, X., Yim, D., & Khuntia, J. (2021). Online Sustainability Reporting and Firm Performance: Lessons Learned from Text Mining. Sustainability, 13(1069), 1069. 
  25. Park, S., & Kim, B. (2022). The impact of everyday AI-based smart speaker use on the well-being of older adults living alone. Technology in Society, 71, 102133. https://doi.org/10.1016/J.TECHSOC.2022.102133 
  26. Rothenberger, L., Fabian, B., & Arunov, E. (2019). RELEVANCE OF ETHICAL GUIDELINES FOR ARTIFICIAL INTELLIGENCE-A SURVEY AND EVALUATION. Progress Papers. https://aisel.aisnet.org/ecis2019_rip/26 
  27. Weaver, W. T. (1971). The Delphi Forecasting Method. The Phi Delta Kappan, 52(5), 267-271. http://www.jstor.org/stable/20372868