Abstract
This study analyzes how artificial intelligence algorithms embedded in everyday life influence human decision-making and the configuration of social subjectivity. A quantitative approach based on socio-technical simulation was adopted, using synthetic data and decision models to assess the impact of algorithmic personalization, exposure diversity, and system explainability. The results show that personalization systematically increases the likelihood of adherence to recommendations, reduces the structural diversity of the choice environment, and modulates subjective variables such as perceived agency and algorithmic dependence. Sensitivity analysis indicates that personalization acts as a highly sensitive parameter, generating predictable and stable changes in decision behavior. These findings confirm that everyday algorithms not only optimize decisions, but also progressively reshape the human decision-making experience.
References
[2] M. Jesse and D. Jannach, “Digital nudging with recommender systems: Survey and future directions,” Computers in Human Behavior Reports, vol. 3, p. 100052, 2021, doi: 10.1016/j.chbr.2020.100052.
[3] D. Kahneman and A. Tversky, “Prospect theory: An analysis of decision under risk,” Econometrica, vol. 47, no. 2, pp. 263–291, 1979, doi: 10.2307/1914185.
[4] H. E. Chapman and A. Abraham, “Because you watched: How do streaming services’ recommender systems influence aesthetic choice?” Behavioral Sciences, vol. 15, no. 11, p. 1544, 2025, doi: 10.3390/bs15111544.
[5] V. Jylhä, N. Hirvonen, and J. Haider, “Algorithmic recommendations in the everyday life of young people: Imaginaries of agency and resources,” Information, Communication & Society, 2025, doi: 10.1080/1369118X.2025.2470227.
[6] C. F. Unruh et al., “Human autonomy in algorithmic management,” in Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society, 2022, doi: 10.1145/3514094.3534168.
[7] K. Zielnicki, G. Aridor, A. Bibaut, A. Tran, W. Chou, and N. Kallus, “The value of personalized recommendations: Evidence from Netflix,” arXiv, 2025, doi: 10.48550/arXiv.2511.07280.
[8] S. Barocas and A. D. Selbst, “Big data’s disparate impact,” California Law Review, vol. 104, pp. 671–732, 2016, doi: 10.15779/Z38BG31.
[9] M. T. Ribeiro, S. Singh, and C. Guestrin, “Why should I trust you?: Explaining the predictions of any classifier,” in Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 2016, doi: 10.1145/2939672.2939778.
[10] R. Guidotti et al., “A survey of methods for explaining black box models,” arXiv, 2018, doi: 10.1145/3236009.
[11] S. Amershi et al., “Guidelines for human-AI interaction,” in Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, 2019, doi: 10.1145/3290605.3300233.
[12] M. De-Arteaga, R. Fogliato, and A. Chouldechova, “A case for humans-in-the-loop: Decisions in the presence of erroneous algorithmic scores,” Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–12, 2020, https://doi.org/10.1145/3313831.3376638.
[13] E. K. Lee, M. R. Shaffer, and J. A. Konstan, “Algorithmic mediation and human decision-making: A systematic review,” ACM Computing Surveys, vol. 55, no. 7, pp. 1–36, 2023, doi: 10.1145/3565967.
[14] F. Ricci, L. Rokach, and B. Shapira, Recommender Systems Handbook, 3rd ed. New York, NY, USA: Springer, 2022, doi: 10.1007/978-1-0716-2197-4.
[15] C. Castelluccia and D. Le Métayer, “Understanding algorithmic decision-making: Opportunities and risks,” Philosophical Transactions of the Royal Society A, vol. 376, no. 2133, Art. no. 20180092, 2018.
[16] J. Grgić-Hlača, M. Redmiles, K. P. Gummadi, and A. Weller, “Human perceptions of fairness in algorithmic decision making,” Proceedings of the 2018 World Wide Web Conference (WWW), pp. 903–912, 2018, doi: 10.1145/3178876.3186138.
[17] R. Parasuraman and V. Riley, “Humans and automation: Use, misuse, disuse, abuse,” Human Factors, vol. 39, no. 2, pp. 230–253, 1997, doi: 10.1518/001872097778543886.
[18] T. Miller, “Explanation in artificial intelligence: Insights from the social sciences,” Artificial Intelligence, vol. 267, pp. 1–38, 2019, doi: 10.1016/j.artint.2018.07.007.
[19] J. W. Brehmer, “Dynamic decision making: Human control of complex systems,” Acta Psychologica, vol. 81, no. 3, pp. 211–241, 1992, doi: 10.1016/0001-6918(92)90019-A.
[20] P. Mohri, A. Rostamizadeh, and A. Talwalkar, Foundations of Machine Learning, 2nd ed. Cambridge, MA, USA: MIT Press, 2018.
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