Adapting the Threats of Artificial Intelligence Scale to Turkish

Öz

The present study aims to adapt the Threats of Artificial Intelligence Scale (TAIS) to Turkish. The study group consists of 285 adults, 175 (61.4%) females and 110 (38.6%) males, aged between 18 and 45 (Mean = 22.84, SD = 6.55). The Personal Information Form, the Threats of Artificial Intelligence Scale, and the Artificial Intelligence Anxiety Scale were used as data collection tools. Confirmatory Factor Analysis (CFA) was used to investigate the factorial structure. In addition, Correlation Analysis was utilized to test the criterion-related validity. CFA revealed that the four-dimensional structure of the Threats of Artificial Intelligence Scale provided sufficient evidence of the goodness of fit on the Turkish sample (χ2 = 71.04, df = 48, χ²/df = 1.48, NFI = .997, CFI = .998, RMSEA = .041 and SRMR = .039). For criterion-related validity, results showed that, significant positive correlations were found between artificial intelligence anxiety and the TAIS’s Recognition subscale (r = .13, p < .05), the Prediction subscale (r = .20, p < .01), the Recommendation subscale (r = .19, p < .01) and the Decision-Making subscale (r = .19, p < .01). Lastly, reliability analyses revealed that the internal consistency coefficient was α = .76 for the Recognition subscale, α = .83 for the Prediction subscale, α = .81 for the Recommendation subscale and α = .74 for the Decision-Making subscale. The findings were discussed in light of the literature.

Anahtar Kelimeler

• Artificial Intelligence
• Fear
• Fear from Artificial Intelligence
• Scale
• Adaptation

Yapay zekâ korkusu ölçeğinin Türkçe’ye uyarlanması

Öz

Bu araştırmanın amacı Yapay Zekâ Korkusu Ölçeğinin (YZKÖ) Türkçe’ ye uyarlanmasıdır. Araştırmanın çalışma grubunu yaşları 18 ile 45 arasında (Ort.= 22.84, SS = 6.55) olan 175’i (% 61.4) kadın, 110’u (% 38.6) erkek toplam 285 yetişkin bireyden meydana gelmektedir. Kişisel Bilgi Formu, Yapay Zekâ Korkusu Ölçeği ve Yapay Zekâ Kaygısı Ölçeği veri toplama aracı olarak kullanılmıştır. Faktör yapısını inceleme amacıyla Doğrulayıcı Faktör Analizi (DFA) kullanılmıştır. Ayrıca, ölçüt bağıntılı geçerliği test etmek amacıyla Pearson korelasyon analizlerinden yararlanılmıştır. DFA sonuçları, Türk örneklemi üzerinde Yapay Zekâ Korkusu Ölçeğinin dört boyutlu yapısının uyum iyiliğine dair yeterli kanıtlar sunduğunu ortaya çıkarmıştır (χ2 = 71.04, Sd = 48, χ²/Sd = 1.48, NFI = .997, CFI = .998, RMSEA = .041 ve SRMR = .039). Ölçüt bağıntılı geçerlik için sonuçlar yapay zekâ kaygısı ile YZKÖ’nün Tanımlama alt boyutu arasında (r = .13, p < .05), Tahmin alt boyutu arasında (r = .20, p < .01), Tavsiye alt boyutu arasında (r = .19, p < .01) ve Karar Verme alt boyutu arasında (r = .19, p < .01) pozitif yönde anlamlı ilişkilerin olduğunu göstermiştir. Son olarak, güvenirlik analizleri iç tutarlık katsayısını Tanımlama alt boyutu için α = .76, Tahmin alt boyutu için α = .83, Tavsiye alt boyutu için α = .81 ve Karar Verme alt boyutu için α = .74 olarak ortaya çıkarmıştır. Elde edilen bulgular alanyazın ışığında tartışılmıştır.

Anahtar Kelimeler

• Yapay Zekâ
• Korku
• Yapay Zekâ Korkusu
• Ölçek
• Uyarlama

Kaynakça

1. Acemoglu, D., Autor, D., Hazell, J., & Restrepo, P. (2020). AI and jobs: Evidence from online vacancies (No. w28257). National Bureau of Economic Research. https://doi.org/10.3386/w28257
2. Akkaya, B., Özkan, A., & Özkan, H. (2021). Yapay Zekâ Kaygı (YZK) Ölçeği: Türkçeye uyarlama, geçerlik ve güvenirlik çalışması. Alanya Akademik Bakış, 5(2), 1125–1146.
3. Bentley, P. J. (2018). The three laws of artificial intelligence: Dispelling common myths. In should we fear artificial intelligence? European Parliamentary Research Centre. https://doi.org/10.2861/412165
4. Berent, I. (2020, February 27). Op-Ed: The real reason we’re afraid of robots. https://www.latimes.com/opinion/story/2020-07-26/artificial-intelligence-robots-psychology-fears
5. Büyüköztürk, Ş., Kılıç Çakmak, E., Akgün, Ö. E., Karadeniz, Ş., & Demirel, F. (2021). Eğitimde bilimsel araştırma yöntemleri. Pegem Akademi.
6. Carillo, M. R. (2020). Artificial intelligence: From ethics to law. Telecommunications Policy, 44(6), 1–16. https://doi.org/10.1016/j.telpol.2020.101937
7. Circiumaru, A. (2021). Futureproofing EU law the case of algorithmic discrimination [Unpublished master's thesis]. University of Oxford.
8. Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2th ed.). Lawrence Erlbaum Associates.

9. Creswell, J. W. (2014). Research design: Qualitative, quantitative, andmixed methods approaches. Sage.
10. Dilsizian, S. E., & Siegel, E. L. (2014). Artificial intelligence in medicine and cardiac imaging: harnessing big data and advanced computing to provide personalized medical diagnosis and treatment. Current Cardiology Reports, 16, 441. https://doi.org/10.1007/s11886-013-0441-8
11. DiStefano, C., & Morgan, G. B. (2014). A comparison of diagonal weighted least squares robust estimation techniques for ordinal data. Structural Equation Modeling: A Multidisciplinary Journal, 21(3), 425–438. https://doi.org/10.1080/10705511.2014.915373
12. Dreyer, K., & Allen, B. (2018). Artificial intelligence in health care: Brave new world or golden opportunity? Journal of the American College of Radiology, 15(4), 655–657. https://doi.org/10.1016/j.jacr.2018.01.010
13. Field, A. (2013). Discovering statistics using IBM SPSS statistics (4th ed.). Sage Publications Ltd.
14. Fornell, C., & Larcker, D. F. (1981). Structural equation models with unobservable variables and measurement error: Algebra and statistics. Journal of Marketing Research, 18(3), 382–388. https://doi.org/10.1177/002224378101800313
15. Fourtané, S. (2019, February 26). Artificial intelligence and the fear of the unknown. https://interestingengineering.com/innovation/artificial-intelligence-and-the-fear-of-the-unknown
16. Gherkeş, V. (2018) Why are we afraıd of artifıcial intelligence (AI)? European Review of Applied Sociology, 11(17), 6–15. https://doi.org/10.1515/eras-2018-0006
17. Gillath, O., Ai, T., Branicky, M. S., Keshmiri, S., Davison, R. B., & Spaulding, R. (2021). Attachment and trust in artificial intelligence. Computers in Human Behavior, 115, 106607. https://doi.org/10.1016/j.chb.2020.106607
18. Hair Jr, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2010). Multivariate data analysis (7th ed.). Pearson.
19. Hair Jr, J. F., Hult, G. T. M., Ringle, C., & Sarstedt, M. (2016). A primer on partial least squares structural equation modeling (PLS-SEM) (2nd ed.). Sage Publications.
20. Heires, K. (2015). The rise of artificial intelligence. Risk Management, 62(4), 38–42.
21. Hu, L. T., & Bentler, P. M. (1999). Cut off criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6(1), 1–55. https://doi.org/10.1080/10705519909540118
22. Huang, M. H., & Rust, R. T. (2018). Artificial intelligence in service. Journal of Service Research, 21(2), 155–172. https://doi.org/10.1177/1094670517752459
23. Kaya, F., Schepman, A., Aydın, F., Rodway, P., Yetişensoy, O., & Demir-Kaya, M. (2024). The roles of personality traits, AI anxiety, and demographic factors in attitudes towards artificial intelligence. International Journal of
24. Human-Computer Interaction, 40(2), 97–514. https://doi.org/10.1080/10447318.2022.2151730
25. Kieslich, K., Lünich, M., & Marcinkowski, F. (2021). The Threats of Artificial Intelligence Scale (TAI) development, measurement and test over three application domains. International Journal of Social Robotics, 13, 1563–1577. https://doi.org/10.1007/s12369-020-00734-w
26. Kile, F. (2013). Artificial intelligence and society: A furtive transformation. AI and Society, 28, 107–115. https://doi.org/10.1007/s00146-012-0396-0
27. Kline, R. B. (2016). Principles and practice of structural equation modeling (4th ed.). Guilford Press.
28. Leahy, S., M., Holland, C., & Ward, F. (2019). The digital frontier: Envisioning future technologies impact on the classroom. Futures, 113, 102422. https://doi.org/ 10.1016/j.futures.2019.04.009
29. Leavy, P. (2017). Research design: Quantitative, qualitative, mixed methods, arts-based, and community-based participatory research approaches. Guilford Publications.
30. Li, C. H. (2016). The performance of ML, DWLS, and ULS estimation with robust corrections in structural equation models with ordinal variables. Psychological Methods, 21(3), 369–387. https://doi.org/10.1037/met0000093
31. Li, J., & Huang, J.-S. (2020). Dimensions of artificial intelligence anxiety based on the Integrated Fear Acquisition Theory. Technology in Society, 63, 101410. https://doi.org/10.1016/j.techsoc.2020.101
32. Liang, Y., & Lee, S.A. (2017). Fear of autonomous robots and artificial intelligence: Evidence from national representative data with probability sampling. International Journal of Social Robotics, 9, 379–384. https://doi.org/10.1007/s12369-017-0401-3
33. Loops. (2021, February 25). Why are people scared of AI? https://medium.com/geekculture/why-are-people-scared-of-ai-75f9e527797.
34. Noble, S. (2018). Algorithms of oppression: how search engines reinforce racism. New York, NYU Press.
35. Palma, M. (2022, February 24). Should we fear artificial intelligence? https://medium.com/geekculture/should-we-fear-artificial-intelligence-9c43a486fca9
36. Rajnerowicz, K. (2022, February 19). Will ai take your job? Fear of AI and AI trends for 2023. https://www.tidio.com/blog/ai-trends/
37. Robitzski, D. (2018, February 21). Five experts share what scares them the most about AI. https://futurism.com/artificial-intelligence-experts-fear
38. Rodriguez-Ruiz, A., Lång, K., Gubern-Merida, A., Broeders, M., Gennaro, G., Clauser, P. ... & Sechopoulos, I. (2019). Stand-alone artificial intelligence for breast cancer detection in mammography: comparison with 101 radiologists. JNCI: Journal of the National Cancer Institute, 111(9), 916–922. https://doi.org/10.1093/jnci/djy222
39. Rossi, F. (2018). Building trust in artificial intelligence. Journal of international Affairs, 72(1), 127–134.
40. Sánchez-Nicolás, E. (2019, January 20). All “big five” tech firms listened to private conversations. https://euobserver.com/ science/145759
41. Schepman, A., & Rodway, P. (2020). Initial validation of the general attitudes towards Artificial Intelligence Scale. Computers in Human Behavior Reports, 1, 100014. https://doi.org/10.1016/j.chbr.2020.100014
42. Schepman, A., & Rodway, P. (2023). The General Attitudes towards Artificial Intelligence Scale (GAAIS): Confirmatory validation and associations with personality, corporate distrust, and general trust. International Journal of Human–Computer Interaction, 39(13), 2724–2741. https://doi.org/10.1080/10447318.2022.2085400
43. Schmelzer, R. (2019, February 22). Should we be afraid of AI? https://www.forbes.com/sites/cognitiveworld/2019/10/31/should-we-be-afraid-of-ai/?sh=23f94aa44331
44. Stevens, J. P. (2009). Applied multivariate statistics for social sciences (5th ed.). Routledge Taylor & Francis Group.
45. Şencan, H. (2005). Sosyal ve davranışsal ölçümlerde güvenilirlik ve geçerlilik. Seçkin Yayınları.
46. Terzi, R. (2020). An adaptation of Artificial Intelligence Anxiety Scale into Turkish: Reliability and validity study. International Online Journal of Education and Teaching, 7(4), 1501–1515. https://eric.ed.gov/?id=EJ1271031
47. Wang, Y. Y., & Wang, Y. S. (2022). Development and validation of an artificial intelligence anxiety scale: An initial application in predicting motivated learning behavior. Interactive Learning Environments, 30(4), 619–634. https://doi.org/10.1080/10494820.2019.1674887
48. Yurdugül, H., & Alsancak Sırakaya, D. (2013). Çevrimiçi Öğrenme Hazır Bulunuşluluk Ölçeği: Geçerlik ve güvenirlik çalışması. Eğitim ve Bilim, 38(169), 391–406. http://eb.ted.org.tr/index.php/EB/article/view/2420