The Multi-Layered and Transformative Impact of Big Data, Artificial Intelligence, and the Internet of Behaviors on the Social Sciences

Abstract

This study examines the multi-layered and transformative impact of big data, artificial intelligence (AI), and the Internet of Behaviors (IoB) on the social sciences. It argues that these technologies represent not merely new methodological tools, but a fundamental paradigm shift that challenges the field's core epistemological assumptions, ethical norms, and societal role. The analysis progresses through four key areas. First, it addresses the epistemological rupture, focusing on the "End of Theory" debate and the tension between correlation and causality in data-driven science. Second, it evaluates the methodological transformation, contrasting new research opportunities like predictive modeling and social simulation with significant risks, including algorithmic bias and the 'black-box' problem. Third, the study explores the profound ethical and political dimensions, such as surveillance, algorithmic authority, and power asymmetries. Finally, it analyzes the transition from the Internet of Things (IoT) to IoB, highlighting the shift from passive observation to active behavioral intervention, which poses significant threats to individual autonomy. The paper concludes that social scientists must adopt a critical role, moving beyond technical application to question the underlying power structures of these technologies and guide the development of a more just and democratic digital future.

Keywords

• Algorithmic authority
• artificial intelligence (AI)
• big data
• internet of behaviors (IoB)
• paradigm shift
• social sciences

Büyük Veri, Yapay Zeka ve Davranışların İnterneti'nin Sosyal Bilimlere Çok Katmanlı ve Dönüştürücü Etkisi

Abstract

Bu çalışma, büyük verinin, yapay zekanın (AI) ve Davranışların İnterneti'nin (IoB) sosyal bilimler üzerindeki çok katmanlı ve dönüştürücü etkisini incelemektedir. Bu teknolojilerin yalnızca yeni metodolojik araçları temsil etmediğini, aynı zamanda alanın temel epistemolojik varsayımlarına, etik normlarına ve toplumsal rolüne meydan okuyan temel bir paradigma değişimini temsil ettiğini ileri sürüyor. Analiz dört temel alanda ilerlemektedir. İlk olarak, "Teorinin Sonu" tartışmasına ve veriye dayalı bilimde korelasyon ile nedensellik arasındaki gerilime odaklanarak epistemolojik kopuşa değiniyor. İkinci olarak, tahmine dayalı modelleme ve sosyal simülasyon gibi yeni araştırma fırsatlarını algoritmik önyargı ve 'kara kutu' sorunu gibi önemli risklerle karşılaştırarak metodolojik dönüşümü değerlendirir. Üçüncüsü, çalışma gözetim, algoritmik otorite ve güç asimetrileri gibi derin etik ve politik boyutları araştırıyor. Son olarak, Nesnelerin İnterneti'nden (IoT) IoB'ye geçişi analiz ederek, pasif gözlemden bireysel özerkliğe önemli tehditler oluşturan aktif davranışsal müdahaleye geçişi vurguluyor. Makale, sosyal bilimcilerin, bu teknolojilerin altında yatan güç yapılarını sorgulamak ve daha adil ve demokratik bir dijital geleceğin gelişimine rehberlik etmek için teknik uygulamanın ötesine geçerek kritik bir rol üstlenmeleri gerektiği sonucuna varıyor.

Keywords

• Algoritmik otorite
• yapay zeka (AI)
• büyük veri
• davranışların interneti (IoB
• davranışların interneti (IoB)
• paradigma değişimi
• sosyal bilimler

References

1. Couldry, N., & Mejias, U. A. (2019). The costs of connection: How data is colonizing human life and appropriating it for capitalism. Stanford University Press.
2. Zuboff, S. (2019). The age of surveillance capitalism: The fight for a human future at the new frontier of power. PublicAffairs.
3. Russell, S., & Norvig, P. (2020). Artificial intelligence: A modern approach (4th ed.). Pearson.
4. Ashton, K. (2009). That ‘Internet of Things’ thing. RFID Journal, 22(7), 97–114.
5. Gartner. (2020). Top strategic technology trends for 2021: Internet of behaviors. Gartner Research.
6. Audi, R. (2010). Epistemology: A contemporary introduction to the theory of knowledge (3rd ed.). Routledge.
7. Kitchin, R. (2014). The data revolution: Big data, open data, data infrastructures and their consequences. Sage.
8. Frické, M. (2015). Big data and its epistemology. Journal of the Association for Information Science and Technology, 66(4), 651–661. https://doi.org/10.1002/asi.23212

9. Anderson, C. (2008, June 23). The end of theory: The data deluge makes the scientific method obsolete. Wired. https://www.wired.com/2008/06/pb-theory/
10. Han, B.-C. (2017). Psychopolitics: Neoliberalism and new technologies of power. Verso Books.
11. Leonelli, S. (2020). Philosophy of data: Why data science is not a replacement for the scientific method. Harvard Data Science Review, 2(1). https://doi.org/10.1162/99608f92.74f2d3f6
12. Savage, M., & Burrows, R. (2007). The coming crisis of empirical sociology. Sociology, 41(5), 885–899. https://doi.org/10.1177/0038038507080443
13. Lazer, D., Pentland, A., Adamic, L., Aral, S., Barabási, A.-L., Brewer, D., … Van Alstyne, M. (2009). Computational social science. Science, 323(5915), 721–723. https://doi.org/10.1126/science.1167742
14. Salganik, M. J. (2019). Bit by bit: Social research in the digital age. Princeton University Press.
15. Pang, B., & Lee, L. (2008). Opinion mining and sentiment analysis. Foundations and Trends in Information Retrieval, 2(1–2), 1–135. https://doi.org/10.1561/1500000011
16. Tumasjan, A., Sprenger, T. O., Sandner, P. G., & Welpe, I. M. (2010). Predicting elections with Twitter: What 140 characters reveal about political sentiment. ICWSM, 10(1), 178–185.
17. Breiman, L. (2001). Statistical modeling: The two cultures. Statistical Science, 16(3), 199–231. https://doi.org/10.1214/ss/1009213726
18. Kleinberg, J., Ludwig, J., Mullainathan, S., & Obermeyer, Z. (2015). Prediction policy problems. American Economic Review, 105(5), 491–495. https://doi.org/10.1257/aer.p20151023
19. Floridi, L., & Chiriatti, M. (2020). GPT-3: Its nature, scope, limits, and consequences. Minds and Machines, 30(4), 681–694. https://doi.org/10.1007/s11023-020-09548-1
20. Bommasani, R., Hudson, D. A., Adeli, E., Altman, R., Arora, S., von Arx, S., … Liang, P. (2021). On the opportunities and risks of foundation models. arXiv. https://doi.org/10.48550/arXiv.2108.07258
21. Tufekci, Z. (2014). Engineering the public: Big data, surveillance and computational politics. First Monday, 19(7). https://doi.org/10.5210/fm.v19i7.4901
22. Grimmer, J. (2015). We are all social scientists now: How big data, machine learning, and causal inference work together. PS: Political Science & Politics, 48(1), 80–83. https://doi.org/10.1017/S1049096514001784
23. Kitchin, R. (2014). Big data, new epistemologies and paradigm shifts. Big Data & Society, 1(1). https://doi.org/10.1177/2053951714528481
24. Lum, K., & Isaac, W. (2016). To predict and serve? Significance, 13(5), 14–19. https://doi.org/10.1111/j.1740-9713.2016.00960.x
25. Lipton, Z. C. (2018). The mythos of model interpretability. Queue, 16(3), 31–57. https://doi.org/10.1145/3236386.3241340
26. Voigt, P., & Von dem Bussche, A. (2017). The EU General Data Protection Regulation (GDPR): A practical guide. Springer.
27. Doshi-Velez, F., & Kim, B. (2017). Towards a rigorous science of interpretable machine learning. arXiv. https://doi.org/10.48550/arXiv.1702.08608
28. Mittelstadt, B. D., Allo, P., Taddeo, M., Wachter, S., & Floridi, L. (2016). The ethics of algorithms: Mapping the debate. Big Data & Society, 3(2). https://doi.org/10.1177/2053951716679679
29. Sharon, T. (2016). The Googlization of health research: From disruptive innovation to disruptive ethics. Personalized Medicine, 13(6), 563–574. https://doi.org/10.2217/pme-2016-0057
30. Barocas, S., & Selbst, A. D. (2016). Big data’s disparate impact. California Law Review, 104(3), 671–732. https://doi.org/10.2139/ssrn.2477899
31. Noble, S. U. (2018). Algorithms of oppression: How search engines reinforce racism. NYU Press.
32. Andrejevic, M. (2014). Surveillance and alienation in the online economy. Surveillance & Society, 12(3), 381–397. https://doi.org/10.24908/ss.v12i3.5113
33. Ferguson, A. G. (2017). The rise of big data policing: Surveillance, race, and the future of law enforcement. NYU Press.
34. Yeung, K. (2017). ‘Hypernudge’: Big Data as a mode of regulation by design. Information, Communication & Society, 20(1), 118–136. https://doi.org/10.1080/1369118X.2016.1186713
35. Sunstein, C. R. (2016). The ethics of influence: Government in the age of behavioral science. Cambridge University Press.
36. Thaler, R. H., & Sunstein, C. R. (2008). Nudge: Improving decisions about health, wealth, and happiness. Yale University Press.
37. Isaak, J., & Hanna, M. J. (2018). User data privacy: Facebook, Cambridge Analytica, and privacy protection. Computer, 51(8), 56–59. https://doi.org/10.1109/MC.2018.3191268