18.08.2023; Житомир, Україна: III Міжнародна наукова конференція «Комплексний підхід до модернізації науки: методи, моделі та мультидисциплінарність»
Роботи, що індексуються в Google Scholar

PSYCHOLOGICAL IMPACT OF ARTIFICIAL INTELLIGENCE ON THE SCIENCE

  • Klaudia Lewita,
  • Adrianna Krzemińska,
  • Polina Kozlovska,
  • Tymoteusz Miller
PDF

Опубліковано 24.08.2023

Як цитувати

Lewita, K., Krzemińska, A., Kozlovska, P., & Miller, T. (2023). PSYCHOLOGICAL IMPACT OF ARTIFICIAL INTELLIGENCE ON THE SCIENCE. Матеріали конференцій МЦНД, (18.08.2023; Житомир, Україна), 235–240. вилучено із https://archive.mcnd.org.ua/index.php/conference-proceeding/article/view/713

Завантаження

Дані завантаження ще не доступні.

Авторське право (c) 2023 Klaudia Lewita, Adrianna Krzemińska, Polina Kozlovska, Tymoteusz Miller

Creative Commons License

Ця робота ліцензується відповідно до Creative Commons Attribution-ShareAlike 4.0 International License.

Google Scholar

Анотація

The rapid advancement of Artificial Intelligence (AI) technologies has permeated numerous facets of contemporary science, fundamentally transforming research methodologies, data interpretation, and predictive modeling. This paper explores the psychological implications of AI on scientists and researchers in terms of cognitive process, research ethics, decision-making, and emotional dynamics.

PDF

Посилання

  1. Emmert-Streib, F. (2021). From the digital data revolution toward a digital society: Pervasiveness of artificial intelligence. Machine Learning and Knowledge Extraction, 3(1), 284-298.
  2. Nguyen, G., Dlugolinsky, S., Bobák, M., Tran, V., López García, Á., Heredia, I., Malík P., Hluchý, L. (2019). Machine learning and deep learning frameworks and libraries for large-scale data mining: a survey. Artificial Intelligence Review, 52, 77-124.
  3. Ford, M. (2021). Rule of the robots: How artificial intelligence will transform everything. Hachette UK.
  4. Hunter, L. (Ed.). (1993). Artificial intelligence and molecular biology (Vol. 445). Menlo Park: Aaai Press.
  5. Garrett, K. A., Bebber, D. P., Etherton, B. A., Gold, K. M., Plex Sulá, A. I., & Selvaraj, M. G. (2022). Climate change effects on pathogen emergence: Artificial intelligence to translate big data for mitigation. Annual Review of Phytopathology, 60, 357-378.
  6. Bianchini, S., Müller, M., & Pelletier, P. (2022). Artificial intelligence in science: An emerging general method of invention. Research Policy, 51(10), 104604.
  7. Hasija, A., & Esper, T. L. (2022). In artificial intelligence (AI) we trust: A qualitative investigation of AI technology acceptance. Journal of Business Logistics, 43(3), 388-412.
  8. Jarrahi, M. H., Askay, D., Eshraghi, A., & Smith, P. (2023). Artificial intelligence and knowledge management: A partnership between human and AI. Business Horizons, 66(1), 87-99.
  9. Graham, S., Depp, C., Lee, E. E., Nebeker, C., Tu, X., Kim, H. C., & Jeste, D. V. (2019). Artificial intelligence for mental health and mental illnesses: an overview. Current psychiatry reports, 21, 1-18.
  10. Benbya, H., Pachidi, S., & Jarvenpaa, S. (2021). Special issue editorial: Artificial intelligence in organizations: Implications for information systems research. Journal of the Association for Information Systems, 22(2), 10.
  11. McCradden, M., Hui, K., & Buchman, D. Z. (2023). Evidence, ethics and the promise of artificial intelligence in psychiatry. Journal of Medical Ethics, 49(8), 573-579.
  12. Alkatheiri, M. S. (2022). Artificial intelligence assisted improved human-computer interactions for computer systems. Computers and Electrical Engineering, 101, 107950.
  13. Mhlanga, D. (2022). Human-centered artificial intelligence: the superlative approach to achieve sustainable development goals in the fourth industrial revolution. Sustainability, 14(13), 7804
  14. Ozmen Garibay, O., Winslow, B., Andolina, S., Antona, M., Bodenschatz, A., Coursaris, C., at el. (2023). Six human-centered artificial intelligence grand challenges. International Journal of Human–Computer Interaction, 39(3), 391-437.
  15. Damiano, L., & Stano, P. (2023). Explorative Synthetic Biology in AI: Criteria of Relevance and a Taxonomy for Synthetic Models of Living and Cognitive Processes. Artificial Life, 1-21.
  16. Morris, M. R. (2023). Scientists' Perspectives on the Potential for Generative AI in their Fields. arXiv preprint arXiv:2304.01420.
  17. Chubb, J., Cowling, P., & Reed, D. (2022). Speeding up to keep up: exploring the use of AI in the research process. AI & society, 37(4), 1439-1457.
  18. Saxena, P., Saxena, V., Pandey, A., Flato, U., & Shukla, K. (2023). Multiple Aspects of Artificial Intelligence. Book Saga Publications.
  19. Ali, S., Abuhmed, T., El-Sappagh, S., Muhammad, K., Alonso-Moral, J. M., Confalonieri, R., Guidotti R., Del Ser J., Diaz-Rodriguez N., Herrera, F. (2023). Explainable Artificial Intelligence (XAI): What we know and what is left to attain Trustworthy Artificial Intelligence. Information Fusion, 99, 101805.
  20. Yang, G., Ye, Q., & Xia, J. (2022). Unbox the black-box for the medical explainable AI via multi-modal and multi-centre data fusion: A mini-review, two showcases and beyond. Information Fusion, 77, 29-52.
  21. Kamila, M. K., & Jasrotia, S. S. (2023). Ethical issues in the development of artificial intelligence: recognizing the risks. International Journal of Ethics and Systems.
  22. Bjerring, J. C., & Busch, J. (2021). Artificial intelligence and patient-centered decision-making. Philosophy & Technology, 34, 349-371.
  23. Dwivedi, Y. K., Kshetri, N., Hughes, L., Slade, E. L., Jeyaraj, A., Kar, A. K., et. al (2023). “So what if ChatGPT wrote it?” Multidisciplinary perspectives on opportunities, challenges and implications of generative conversational AI for research, practice and policy. International Journal of Information Management, 71, 102642.
  24. Ferrara, E. (2023). Should chatgpt be biased? challenges and risks of bias in large language models. arXiv preprint arXiv:2304.03738.
  25. Ferrara, E. (2023). Fairness And Bias in Artificial Intelligence: A Brief Survey of Sources, Impacts, And Mitigation Strategies. arXiv preprint arXiv:2304.07683.
  26. Coeckelbergh, M. (2020). AI ethics. Mit Press.
  27. Bartoletti, I. (2019). AI in healthcare: Ethical and privacy challenges. In Artificial Intelligence in Medicine: 17th Conference on Artificial Intelligence in Medicine, AIME 2019, Poznan, Poland, June 26–29, 2019, Proceedings 17 (pp. 7-10). Springer International Publishing.
  28. Vollmer, S., Mateen, B. A., Bohner, G., Király, F. J., Ghani, R., Jonsson, P., ... & Hemingway, H. (2020). Machine learning and artificial intelligence research for patient benefit: 20 critical questions on transparency, replicability, ethics, and effectiveness. bmj, 368.
  29. Hornung, O., & Smolnik, S. (2022). AI invading the workplace: negative emotions towards the organizational use of personal virtual assistants. Electronic Markets, 1-16.
  30. Upadhyay, A. Artificial Intelligence and Ethics. https://doi.org/10.31995/Book.AB274-M23.Chapter8.
  31. Gamez, D. (2023). AI: Artificial Intelligence as philosophy: Machine consciousness and intelligence.
  32. Powers, T. M., & Ganascia, J. G. (2020). The Ethics of the Ethics of AI. The Oxford handbook of ethics of AI, 25-51.