О чём стоит помнить, размышляя об искусственном интеллекте
Научная статья
Для цитирования
Тамбовцев В. Л. О чём стоит помнить, размышляя об искусственном интеллекте // Управление наукой: теория и практика. 2024. Том 6. № 2. С. 26-34. DOI: https://doi.org/10.19181/smtp.2024.6.2.2 EDN: FFDRFQ
Аннотация
В статье обсуждаются основания схожести общественного мнения об искусственном интеллекте в разных странах. В то же время оно отличается от суждений по этой тематике, которые высказывают эксперты. Эти сходства и различия объясняются близостью народных теорий, которые возникают у граждан в силу ограниченного опыта взаимодействия с искусственным интеллектом. Оценки рисков, которые дают эксперты, не полностью учитывают результаты и выводы когнитивных наук, имеющие непосредственное отношение к искусственному интеллекту. Приводится ряд результатов, полученных в когнитивных науках, и выделяются некоторые из них, которые полезно учитывать при оценке искусственного интеллекта.
Ключевые слова:
искусственный интеллект, народные теории, когнитивные науки
Литература
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2. Müller V. C., Bostrom N. Future progress in artificial intelligence: A poll among experts. AI Matters. 2014;1(1):9–11. DOI 10.1145/2639475.2639478.
3. Morikawa M. Who are afraid of losing their jobs to artificial intelligence and robots? Evidence from a survey. RIETI Discussion Paper Series. 17-E-069. 2017. May. Available at: https://rieti.go.jp/jp/publications/dp/17e069.pdf (accessed: 26.04.2024).
4. Merenkov A. V., Campa R., Dronishinets N. P. Public opinion on artificial intelligence development. KnE Social Sciences. 2020;5(2):565–574. DOI 10.18502/kss.v5i2.8401.
5. Kelley P. G., Yang Y., Heldreth C., Moessner C., Sedley A., Kramm A., Newman D. T., Woodruf A. Exciting, useful, worrying, futuristic: Public perception of artificial intelligence in 8 countries. In: AIES’21 : Proceedings of the 2021 AAAI/ACM conference on AI, ethics, and society. May 19–21, 2021, Virtual Event USA. New York : Association for Computing Machinery; 2021. P. 627–637. DOI 10.1145/3461702.3462605.
6. European Commission, European Research Council Executive Agency. Foresight: Use and impact of artificial intelligence in the scientific process. Luxembourg : Publications Office of the European Union; 2023. 17 p. DOI 10.2828/10694.
7. Gillespie N., Lockey S., Curtis C., Pool J., Akbari A. Trust in artificial intelligence: A global study. Brisbane ; New York : The University of Queensland ; KPMG Australia; 2023. 82 p. DOI 10.14264/00d3c94.
8. Sun M., Hu W., Wu Y. Public perceptions and attitudes towards the application of artificial intelligence in journalism: From a China-based survey. Journalism Practice. 2024;18(3):548–570. DOI 10.1080/17512786.2022.2055621.
9. Haesevoets T., Verschuere B., Van Severen R., Roets A. How do citizens perceive the use of Artificial Intelligence in public sector decisions? Government Information Quarterly. 2024;41(1):101906. DOI 10.1016/j.giq.2023.101906.
10. Brauner P., Hick A., Philipsen R., Ziefle M. What does the public think about artificial intelligence? – A criticality map to understand bias in the public perception of AI. Frontiers in Computer Science. 2023;5:1113903. DOI 10.3389/fcomp.2023.1113903.
11. Müller V. C. Risks of general artificial intelligence. Journal of Experimental & Theoretical Artificial Intelligence. 2024;26(3):297–301. DOI 10.1080/0952813X.2014.895110.
12. McLean S., Read G. J. M., Thompson J., Baber C., Stanton N. A., Salmon P. M. The risks associated with Artificial General Intelligence: A systematic review. Journal of Experimental & Theoretical Artificial Intelligence. 2023;35(5):649–663. DOI 10.1080/0952813X.2021.1964003.
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14. Alon-Barkat S., Busuioc M. Human–AI interactions in public sector decision making: “Automation bias” and “selective adherence” to algorithmic advice. Journal of Public Administration Research and Theory. 2023;33(1):153–169. DOI 10.1093/jopart/muac007.
15. Zhao Y., Yin D., Wang L., Yu Y. The rise of artificial intelligence, the fall of human wellbeing? International Journal of Social Welfare. 2024;33(1):75–105. DOI 10.1111/ijsw.12586.
16. Keil F. C. Folkscience: Coarse interpretations of a complex reality. Trends in Cognitive Sciences. 2003;7(8):368–373. DOI 10.1016/s1364-6613(03)00158-x.
17. Schapiro A., Turk-Browne N. Statistical learning. In: Toga A. W., ed. Brain mapping: An encyclopedic reference. Vol. 3. London : Elsevier/Academic Press; 2015. P. 501–506. DOI 10.1016/B978-0-12-397025-1.00276-1.
18. Nickerson R. S. Confirmation bias: A ubiquitous phenomenon in many guises. Review of General Psychology. 1998;2(2):175–220. DOI 10.1037/1089-2680.2.2.175.
19. Vitriol J. A., Marsh J. K. The illusion of explanatory depth and endorsement of conspiracy beliefs. European Journal of Social Psychology. 2018;48(7):955–969. DOI 10.1002/ejsp.2504.
20. Rozenblit L., Keil F. The misunderstood limits of folk science: An illusion of explanatory depth. Cognitive Science. Vol. 2002;26(5):521–562. DOI 10.1207/s15516709cog2605_1.
21. Miller T. Explanation in artificial intelligence: Insights from the social sciences. Artificial Intelligence. 2019;267:1–38. DOI 10.1016/j.artint.2018.07.007.
22. De Graaf M. M. A., Malle B. F. How people explain action (and autonomous intelligent systems should too). In: Artificial intelligence for human–robot interaction : Papers from the AAAI Fall Symposium, 2017. Palo Alto, CA : The AAAI Press; 2017. P. 19–26.
23. Doshi-Velez F., Kim B. Towards a rigorous science of interpretable machine learning. arXiv. 2017. March 2. Available at: https://arxiv.org/abs/1702.08608 (accessed: 26.04.2024). DOI 10.48550/arXiv.1702.08608.
24. Vapnik V. The nature of statistical learning theory. New York : Springer; 1995. xv, 193 p. ISBN 978-0-387-94559-0.
25. Ordin M., Polyanskaya L., Soto D. Neural bases of learning and recognition of statistical regularities. Annals of the New York Academy of Sciences. 2020;1467(1):60–76. DOI 10.1111/nyas.14299.
26. Alnuaimi A. F. A. H., Albaldawi T. H. K. Concepts of statistical learning and classification in machine learning: An overview. BIO Web of Conferences. 2024;97:00129. DOI 10.1051/bioconf/20249700129.
27. Roli A., Jaeger J., Kauffman S. A. How organisms come to know the world: Fundamental limits on artificial general intelligence. Frontiers in Ecology and Evolution. 2022;9:806283. DOI 10.3389/fevo.2021.806283.
28. Curtis V., Aunger R., Rabie T. Evidence that disgust evolved to protect from risk of disease. Proceedings of the Royal Society B: Biological Sciences. 2004;271(Suppl. 4):S131–S133. DOI 10.1098/rsbl.2003.0144.
29. Rozin P., Haidt J. The domains of disgust and their origins: Contrasting biological and cultural evolutionary accounts. Trends in Cognitive Sciences. 2013; 17(8):367–368. DOI 10.1016/j.tics.2013.06.001.
30. Libet B., Gleason C. A., Wright E. W., Pearl D. K. Time of conscious intention to act in relation to onset of cerebral activity (readiness-potential): The unconscious initiation of a freely voluntary act. Brain. 1983;106(3):623–642. DOI 10.1093/brain/106.3.623.
31. Braun M. N., Wessler J., Friese M. A meta-analysis of Libet-style experiments. Neuroscience & Biobehavioral Reviews. 2021;128:182–198. DOI 10.1016/j.neubiorev.2021.06.018.
Статья
Поступила: 17.04.2024
Опубликована: 25.06.2024
Форматы цитирования
Другие форматы цитирования:
APA
Тамбовцев, В. Л. (2024). О чём стоит помнить, размышляя об искусственном интеллекте. Управление наукой: теория и практика, 6(2), 26-34. https://doi.org/10.19181/smtp.2024.6.2.2
Раздел
Дискуссия
