Framing Discourses in Turkish News Coverage Regarding Artificial Intelligence Technologies’ Prospects and Challenges

Abstract

In recent years, the continuous and increasing implementation of artificial intelligence technologies in our daily lives has triggered discussions regarding its potentials and risks, especially in news media. However, the analysis of the way in which this technology is communicated to the public is limited. The framing approach can be applied to understand the structure of news stories and to explore the type of interpretation patterns that are used to inform the public about artificial intelligence technologies. For this purpose, a total of 209 news articles from two highly circulated Turkish newspapers were gathered between January 1, 2019 and December 31, 2019 and analyzed in order to discover how this emerging technology is framed in the media discourse with regard to its prospects and challenges. The results indicate that the following frames are used in Turkish news coverage to thematize the possibilities and potential risks of artificial intelligence: “Artificial intelligence as assistant/supporter”, “economic benefit”, “elimination of human error”, “human replacement”, and “ethical concerns”. Overall, the news coverage reveals a predominately positive tendency toward artificial intelligence by emphasizing the prospects and benefits for its users.

Keywords

• Journalism
• News Coverage
• Artificial Intelligence
• Framing
• Framing Analysis

Türkiye’de Yayınlanan Haberlerde Yapay Zekâ Teknolojilerinin Olanakları ve Zorlukları Hakkındaki Çerçevelemeler

Abstract

Son yıllarda, yapay zekânın günlük hayatımızdaki artan kullanımı bu teknolojinin olanakları ve riskleri hakkında özellikle medya alanında birçok tartışmaya yol açmıştır. Fakat bu teknolojilerin halka nasıl yansıtıldığı hakkında çok az inceleme yapılmıştır. Çerçeveleme yöntemi, haberlerin yapısını ortaya çıkarmayı hedeflemektedir. Halkı yapay zekâ hakkında bilgilendirirken ayrıca yorum modellerini anlamak için de kullanılmaktadır. Bu amaçla yüksek tiraja sahip iki Türkçe gazetede yayınlanmış 209 haber, 1 Ocak 2019 ve 31 Aralık 2019 tarihleri arasında toplanıp yapay zekânın olanakları ve potansiyel zorlukları açısından medyada nasıl çerçevelendiğini anlamak adına analiz edilmiştir. Sonuçlara göre, Türkiye’de yayınlanan haberlerde yapay zekânın olanakları ve risklerini temalandırmak için en çok kullanılan çerçeveler şunlardır: “Bir yardımcı/destekçi olarak yapay zekâ”, “ekonomik faydalar”, “insan hatasının ortadan kalkması”, “insanın yerini almak” ve “etik kaygılar”. Genel olarak, yapay zekâdan bahsederken kullanıcıları için yaratacağı olanakları ve faydaları vurgulayan iyimser tutumun yoğun bir şekilde sergilendiği görülmüştür.

Keywords

• Gazetecilik
• Habercilik
• Yapay Zeka
• Çerçeveleme
• Çerçeveleme Analizi

References

1. Backhaus, K., Erichson, B., Plinke, W., and Weiber, R. (2016). Multivariate Analysemethoden: Eine anwendungsorientierte Einführung. [Multivariate analysis methods: An application-oriented introduction.] Berlin: Springer Verlag.
2. Bateson, G. (1972). Steps to an ecology of mind: Collected essays in anthropology, psychiatry, evolution and epistemology. Chicago: Chicago University Press.
3. Chong, D., & Druckman, J. N. (2007). Framing theory. Annual Review of Political Science, 10, 103-126.
4. Cui, D., & Wu, F. (2019). The influence of media use on public perceptions of artificial intelligence in China: Evidence from an online survey. Information Development. DOI: 10.1177/026.666.6919893411.
5. Dahinden, U. (2006). Framing: Eine integrative Theorie der Massenkommunikation. [Framing: An integrative theory of mass media.] Konstanz: UVK-Verlags GmbH.
6. De Vreese, C. H. (2005). News framing: Theory and typology. Information Design Journal + Document Design, 13(1), 51-62.
7. Dunwoody, S. (1992). The media and public perceptions of risk: How journalists frame risk stories. In D. W. Bromley, & K. Segerson (Eds.), The social response to environmental risk: Policy formulation in an age of certainty (pp. 75-100). Boston: Kluwer Academic Publisher.
8. Entman, R. M. (1993). Framing: Toward clarification of a fractured paradigm. Journal of Communication, 43, 51-58.

9. Eubanks, V. (2018). Automating inequality: How high-tech tools profile, police and punish the poor. New York: St. Martin’s Press.
10. European Commission (2019). A definition of AI. Main capabilities and disciplines. Retrieved from: https:// ec.europa.eu/newsroom/dae/document.cfm?doc_id=56341 (21. 02. 2021).
11. European Commission (2018). Communication from the Commission to the European Parliament, the European Council, the Council, the European Economic and Social Committee and The Committee of the Regions. Artificial intelligence for Europe. Retrieved from: https://ec.europa.eu/transparency/ regdoc/rep/1/2018/EN/COM-2018-237-F1-EN-MAIN-PART-1.PDF (21. 02. 2021).
12. Fast, E., & Horvitz, E. (2017). Long-term trends in the public perception of artificial intelligence. Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence (AAAI-17), 963-969.
13. Gamson, W. A., & Modigliani, A. (1987). The changing culture of affirmative action. In R. G. Braungart, & M. M. Braungart (Eds.), Research in political sociology (pp. 137-177). Greenwich: JAI Press.
14. Garvey, C., & Maskal, C. (2020). Sentiment analysis of the news media on artificial intelligence does not support claims of negative bias against artificial intelligence. Omics – A Journal of Integrative Biology, 24(5), 286-299.
15. Gazete Tirajları (2019). Gazetelerin haftalık satış raporu. Retrieved from: http://gazetetirajlari.com/ HaftalikTirajlar.aspx (21. 02. 2021).
16. Gerhards, J., & Rucht, D. (1992). Mesomobilization: Organizing and framing in two protest campaigns in West Germany. American Journal of Sociology, 98(3), 555-596.
17. Goffman, E. (1974). Frame Analysis: An essay on the organization of experience. New York: Harper and Row.
18. Matthes, J. (2007). Framing-Effekte: Zum Einfluss der Politikberichterstattung auf die Einstellungen der Rezipienten. [The influence of political reporting on the attitudes of the recipients.] München: Verlag Reinhard Fischer.
19. Matthes, J., & Kohring, M. (2008). The content analysis of media frames: Toward improving reliability and validity. Journal of Communication, 58, 258-279.
20. Matthes, J., & Kohring, M. (2004). Die empirische Erfassung von Medien-Frames. [The empirical measuring of media frames.] Medien & Kommunikationswissenschaft, 52(1), 56-75.
21. Mayring, P. (2008). Qualitative Inhaltsanalyse: Grundlagen und Techniken. [Qualitative content analysis: Basics and techniques.] Weinheim, Basel: Beltz Verlag.
22. McCarthy, J., Minsky, M., Rochester, N., & Shannon, C. (1955). A proposal for the Dartmouth summer research project on artificial intelligence. Retrieved from: http://jmc.stanford.edu/articles/dartmouth/ dartmouth.pdf (21. 02. 2021).
23. McCombs, M. E., & Shaw, D. L. (1972). The agenda-setting function of mass media. Public Opinion Quarterly, 36(2), 176-187.
24. McQuail, D. (2009). McQuail’s mass communication theory. London: Sage Publications.
25. Natale, S., & Ballatore, A. (2017). Imagining the thinking machine: Technological myths and the rise of artificial intelligence. Convergence: The International Journal of Research into New Media Technologies, 26(1), 3-18.
26. Noble, S. U. (2018). Algorithms of oppression: How search engines reinforce racism. New York: New York University Press.
27. Pan, Z., & Kosicki, G. M. (1993). Framing analysis: An approach to news discourse. Political Communication, 10(1), 55-75.
28. Pew Research Center (2018a). Public attitudes toward computer algorithms. Retrieved from: https://www. pewresearch.org/internet/wp-content/uploads/sites/9/2018/11/PI_2018.11.19_algorithms_FINAL. pdf (21. 02. 2021).
29. Pew Research Center (2018b). Artificial intelligence and the future of humans. Retrieved from: https://www. pewinternet.org/wp-content/uploads/sites/9/2018/12/PI_2018.12.10_future-of-ai_FINAL1.pdf (21. 02. 2021).
30. Pew Research Center (2017). Automation in everyday life. Retrieved from: https://www.pewresearch.org/ internet/wp-content/uploads/sites/9/2017/10/PI_2017.10.04_Automation_FINAL.pdf (21. 02. 2021).
31. Pew Research Center (2008). Key news audiences now blend online and traditional sources. Audience segments in a changing news environment. Retrieved from: https://www.people-press.org/2008/08/17/keynews- audiences-now-blend-online-and-traditional-sources/ (21. 02. 2021).
32. Scheufele, B. (2004). Framing-effects approach: A theoretical and methodological critique. Communications, 29, 401-428.
33. Scheufele, B. (2003). Frames – Framing – Framing-Effekte: Theoretische und methodische Grundlegung des Framing-Ansatzes sowie empirische Befunde zur Nachrichtenproduktion. [Frames – Framing – Framing effects: Theoretical and methodological foundation of the framing approach and empirical findings on news production.] Wiesbaden: Westdeutscher Verlag.
34. Searle, J. R. (1980). Minds, brains, and programs. The Behavioral and Brain Sciences, 3(3), 417-457.
35. Taylor, S. E., & Crocker, J. (1981). Schematic bases of social information processing. In E. T. Higgins, C. P. Herman, & M. P. Zanna (Eds.), Social Cognition. The Ontario Symposium. Volume 1 (pp. 89-134). Hillsdale: Lawrence Erlbaum Associates.
36. Tuchman, G. (1976). Telling stories. Journal of Communication, 26(4), 93-97.
37. TUİK (2019). Hanehalkı bilişim teknolojileri kullanım araştırması: Son üç ay içinde internet kullanan bireylerin interneti kişisel kullanma amaçları. Retrieved from: http://tuik.gov.tr/PreIstatistikTablo. do?istab_id=2603 (21. 02. 2021).
38. Yu, K., & Kohane, I. S. (2018). Framing the challenges of artificial intelligence in medicine. BMJ Quality & Safety, 28(3), 238-241.