The influence of artificial intelligence on the sense of agency in decision-making
Keywords:
субъектность, чувство субъектности, взаимодействие человека и цифровых технологий, искусственный интеллект, распределенная субъектность, принятие решений, ответственностьAbstract
This article analyzes the impact of artificial intelligence on the human sense of agency during decision-making processes. The study aims to conceptualize the transformation of the sense of agency within human-AI interaction and to identify the factors that contribute to either preserving or diminishing the experience of authorship. The methodological framework encompasses theoretical analysis, systematization, and synthesis of domestic and international research in the psychology of agency, cognitive psychology, and human-computer interaction. The findings demonstrate that artificial intelligence redistributes cognitive functions, thereby altering the perception of control and accountability. Furthermore, the article analyzes the effects of automation bias, reduced reflexivity, and the erosion of accountability. The study establishes that the degree of agency transformation depends on algorithmic transparency, the explainability of decisions, and the opportunity for user intervention. The theoretical significance of this research lies in refining the conceptual understanding of distributed agency within digital environments, while its practical value stems from establishing design principles for artificial intelligence systems that support human agency.Downloads
References
Абульханова К.А. Стратегия жизни. М.: Мысль, 1991. 299 с.
Знаков В.В. Субъектно-аналитический подход в психологии понимания // Психологические исследования. 2015. Т. 8. № 42. С. 12. DOI: 10.54359/ps.v8i42.536 EDN: TDXHCY
Рубинштейн С.Л. Основы общей психологии. СПб.: Питер, 2019. 713 с.
Сергиенко Е.А. Системно-субъектный подход: обоснование и перспектива // Психологический журнал. 2011. Т. 32. № 1. С. 120-132. EDN: NSIPQT
A meta-analysis of factors influencing the development of trust in automation / K.E. Schaefer [et al.] // Human Factors. 2016. Vol. 58. N 3. P. 377-400. DOI: 10.1177/0018720816634228
Automation technology and sense of control: A window on human agency / B. Berberian [et al.] // PLoS ONE. 2012. Vol. 7. N 3. URL: https://pubmed.ncbi.nlm.nih.gov/22479528/ (дата обращения: 20.03.2026). DOI: 10.1371/journal.pone.0034075
Bainbridge L. Ironies of automation // Automatica. 1983. Vol. 19. N 6. P. 775-779. DOI: 10.1016/0005-1098(83)90046-8
Bratman M.E. Shared Agency: A Planning Theory of Acting Together. Oxford: Oxford University Press, 2014. 208 p.
Buçinca Z., Malaya M.B., Gajos K.Z. To trust or to think: Cognitive forcing functions can reduce overreliance on AI in AI-assisted decision-making // Proceedings of the ACM on Human-Computer Interaction. 2021. Vol. 5. CSCW1. Article 188. P. 1-21. DOI: 10.1145/3449287
Coeckelbergh M. AI Ethics. Cambridge, MA: MIT Press, 2020. 248 p.
De Jaegher H., Di Paolo E. Participatory sense-making // Phenomenology and the Cognitive Sciences. 2007. Vol. 6. N 4. P. 485-507. DOI: 10.1007/s11097-007-9076-9 EDN: VSAWVN
Ehsan U., Riedl M.O. Human-centered Explainable AI: Towards a Reflective Sociotechnical Approach // HCI International 2020 – Late Breaking Papers: Multimodality and Intelligence. Cham: Springer, 2020. P. 449-466. DOI: 10.1007/978-3-030-60117-1_33
Epley N., Waytz A., Cacioppo J.T. On seeing human: A three-factor theory of anthropomorphism // Psychological Review. 2007. Vol. 114. N 4. P. 864-886. DOI: 10.1037/0033-295X.114.4.864
Explanations can reduce overreliance on AI systems during decision-making / H. Vasconcelos [et al.] // Proceedings of the ACM on Human-Computer Interaction. 2023. Vol. 7. P. 1-38. DOI: 10.1145/3579605
Floridi L., Cowls J. A Unified Framework of Five Principles for AI in Society // Harvard Data Science Review. 2019. Vol. 1. N 1. DOI: 10.1162/99608f92.8cd550d1
Frith C.D., Blakemore S.-J., Wolpert D.M. Explaining the symptoms of schizophrenia: Abnormalities in the awareness of action // Brain Research Reviews. 2000. Vol. 31. N 2-3. P. 357-363. DOI: 10.1016/S0165-0173(99)00052-1 EDN: AETHXL
Guidelines for Human-AI Interaction / S. Amershi [et al.] // Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems. 2019. Paper 3. P. 1-13. DOI: 10.1145/3290605.3300233
Haggard P. Sense of agency in the human brain // Nature Reviews Neuroscience. 2017. Vol. 18. N 4. P. 197-207. DOI: 10.1038/nrn.2017.14.
Hybrid Intelligence / D. Dellermann [et al.] // Business & Information Systems Engineering. 2019. Vol. 61. N 5. P. 637-643. DOI: 10.1007/s12599-019-00595-2 EDN: ZEGZJW
Jarrahi M.H. Artificial intelligence and the future of work: Human-AI symbiosis in organizational decision making // Business Horizons. 2018. Vol. 61. N 4. P. 577-586. DOI: 10.1016/j.bushor.2018.03.007
Latour B. Reassembling the Social: An Introduction to Actor-Network-Theory. Oxford: Oxford University Press, 2005. 320 p. DOI: 10.1093/oso/9780199256044.001.0001
Long D., Magerko B. What is AI Literacy? Competencies and Design Considerations // Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems. 2020. P. 1-16. DOI: 10.1145/3313831.3376727
Miller T. Explanation in artificial intelligence: Insights from the social sciences // Artificial Intelligence. 2019. Vol. 267. P. 1-38. DOI: 10.1016/j.artint.2018.07.007
Moore J.W. What is the sense of agency and why does it matter? // Frontiers in Psychology. 2016. Vol. 7. URL: https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2016.01272/full (дата обращения: 20.03.2026). DOI: 10.3389/fpsyg.2016.01272
On the Dangers of Stochastic Parrots: Can Language Models Be Too Big?/ E.M. Bender [et al.] // Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency. 2021. P. 610-623. DOI: 10.1145/3442188.3445922.
O’Neil C. Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy. New York: Crown Publishing Group, 2016. 259 p.
Parasuraman R., Manzey D.H. Complacency and bias in human use of automation: An attentional integration // Human Factors. 2010. Vol. 52. N 3. P. 381-410. DOI: 10.1177/0018720810376055
Parasuraman R., Riley V. Humans and automation: Use, misuse, disuse, abuse // Human Factors. 1997. Vol. 39. N 2. P. 230-253. DOI: 10.1518/001872097778543886
Power to the people: The role of humans in interactive machine learning / S. Amershi [et al.] // AI Magazine. 2014. Vol. 35. N 4. P. 105-120. DOI: 10.1609/aimag.v35i4.2513
Rahwan I. Society-in-the-loop: Programming the algorithmic social contract // Ethics and Information Technology. 2018. Vol. 20. N 1. P. 5-14. DOI: 10.1007/s10676-017-9430-8 EDN: MCMADR
Rai A. Explainable AI: From black box to glass box // Journal of the Academy of Marketing Science. 2020. Vol. 48. N 1. P. 137-141. DOI: 10.1007/s11747-019-00710-5 EDN: MNBEIB
Shneiderman B. Human-Centered Artificial Intelligence: Reliable, Safe & Trustworthy // International Journal of Human-Computer Interaction. 2020. Vol. 36. N 6. P. 495-504. DOI: 10.1080/10447318.2020.1741118 EDN: ENPGDI
Synofzik M., Vosgerau G., Newen A. Beyond the comparator model: A multifactorial two-step account of agency // Consciousness and Cognition. 2008. Vol. 17. N 1. P. 219-239. DOI: 10.1016/j.concog.2007.03.010
Waytz A., Heafner J., Epley N. The mind in the machine: Anthropomorphism increases trust in an autonomous vehicle // Journal of Experimental Social Psychology. 2014. Vol. 52. P. 113-117. DOI: 10.1016/j.jesp.2014.01.005
Wegner D.M. The Illusion of Conscious Will. Cambridge, MA: MIT Press, 2002. 419 p. DOI: 10.7551/mitpress/3650.001.0001
Abul’khanova K.A. Life Strategy. Moscow: Mysl Publishers, 1991. 299 p.
Znakov V.V. The Subject-Analytic Approach in the Psychology of Understanding // Psychological Studies. 2015. Vol. 8. N 42. P. 12. DOI 10.54359/ps.v8i42.536 EDN: TDXHCY
Rubinshtein S.L. Fundamentals of General Psychology. Saint Petersburg: Piter Publishing House, 2019. 713 p.
Sergienko E.A. The System-Subject Approach: Substantiation and Prospects // Psychological Journal. 2011. Vol. 32. N 1. P. 120-132. EDN: NSIPQT
A meta-analysis of factors influencing the development of trust in automation / K.E. Schaefer [et al.] // Human Factors. 2016. Vol. 58. № 3. P. 377-400. DOI: 10.1177/0018720816634228
Automation technology and sense of control: A window on human agency / B. Berberian [et al.] // PLoS ONE. 2012. Vol. 7. № 3. URL: https://pubmed.ncbi.nlm.nih.gov/22479528/ (accessed: 20.03.2026). DOI: 10.1371/journal.pone.0034075
Bainbridge L. Ironies of automation // Automatica. 1983. Vol. 19. N 6. P. 775-779. DOI: 10.1016/0005-1098(83)90046-8
Bratman M.E. Shared Agency: A Planning Theory of Acting Together. Oxford: Oxford University Press, 2014. 208 p.
Buçinca Z., Malaya M.B., Gajos K.Z. To trust or to think: Cognitive forcing functions can reduce overreliance on AI in AI-assisted decision-making // Proceedings of the ACM on Human-Computer Interaction. 2021. Vol. 5. CSCW1. Article 188. P. 1-21. DOI: 10.1145/3449287
Coeckelbergh M. AI Ethics. Cambridge, MA: MIT Press, 2020. 248 p.
De Jaegher H., Di Paolo E. Participatory sense-making // Phenomenology and the Cognitive Sciences. 2007. Vol. 6. N 4. P. 485-507. DOI: 10.1007/s11097-007-9076-9 EDN: VSAWVN
Ehsan U., Riedl M.O. Human-centered Explainable AI: Towards a Reflective Sociotechnical Approach // HCI International 2020 – Late Breaking Papers: Multimodality and Intelligence. Cham: Springer, 2020. P. 449-466. DOI: 10.1007/978-3-030-60117-1_33
Epley N., Waytz A., Cacioppo J.T. On seeing human: A three-factor theory of anthropomorphism // Psychological Review. 2007. Vol. 114. N 4. P. 864-886. DOI: 10.1037/0033-295X.114.4.864
Explanations can reduce overreliance on AI systems during decision-making / H. Vasconcelos [et al.] // Proceedings of the ACM on Human-Computer Interaction. 2023. Vol. 7. P. 1-38. DOI: 10.1145/3579605
Floridi L., Cowls J. A Unified Framework of Five Principles for AI in Society // Harvard Data Science Review. 2019. Vol. 1. N 1. DOI: 10.1162/99608f92.8cd550d1
Frith C.D., Blakemore S.-J., Wolpert D.M. Explaining the symptoms of schizophrenia: Abnormalities in the awareness of action // Brain Research Reviews. 2000. Vol. 31. N 2-3. P. 357-363. DOI: 10.1016/S0165-0173(99)00052-1 EDN: AETHXL
Guidelines for Human-AI Interaction / S. Amershi [et al.] // Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems. 2019. Paper 3. P. 1-13. DOI: 10.1145/3290605.3300233
Haggard P. Sense of agency in the human brain // Nature Reviews Neuroscience. 2017. Vol. 18. N 4. P. 197-207. DOI: 10.1038/nrn.2017.14.
Hybrid Intelligence / D. Dellermann [et al.] // Business & Information Systems Engineering. 2019. Vol. 61. N 5. P. 637-643. DOI: 10.1007/s12599-019-00595-2 EDN: ZEGZJW
Jarrahi M.H. Artificial intelligence and the future of work: Human-AI symbiosis in organizational decision making // Business Horizons. 2018. Vol. 61. N 4. P. 577-586. DOI: 10.1016/j.bushor.2018.03.007
Latour B. Reassembling the Social: An Introduction to Actor-Network-Theory. Oxford: Oxford University Press, 2005. 320 p. DOI: 10.1093/oso/9780199256044.001.0001
Long D., Magerko B. What is AI Literacy? Competencies and Design Considerations // Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems. 2020. P. 1-16. DOI: 10.1145/3313831.3376727
Miller T. Explanation in artificial intelligence: Insights from the social sciences // Artificial Intelligence. 2019. Vol. 267. P. 1-38. DOI: 10.1016/j.artint.2018.07.007
Moore J.W. What is the sense of agency and why does it matter? // Frontiers in Psychology. 2016. Vol. 7. URL: https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2016.01272/full (accessed: 20.03.2026). DOI: 10.3389/fpsyg.2016.01272
On the Dangers of Stochastic Parrots: Can Language Models Be Too Big?/ E.M. Bender [et al.] // Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency. 2021. P. 610-623. DOI: 10.1145/3442188.3445922.
O’Neil C. Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy. New York: Crown Publishing Group, 2016. 259 p.
Parasuraman R., Manzey D.H. Complacency and bias in human use of automation: An attentional integration // Human Factors. 2010. Vol. 52. N 3. P. 381-410. DOI: 10.1177/0018720810376055
Parasuraman R., Riley V. Humans and automation: Use, misuse, disuse, abuse // Human Factors. 1997. Vol. 39. N 2. P. 230-253. DOI: 10.1518/001872097778543886
Power to the people: The role of humans in interactive machine learning / S. Amershi [et al.] // AI Magazine. 2014. Vol. 35. N 4. P. 105-120. DOI: 10.1609/aimag.v35i4.2513
Rahwan I. Society-in-the-loop: Programming the algorithmic social contract // Ethics and Information Technology. 2018. Vol. 20. N 1. P. 5-14. DOI: 10.1007/s10676-017-9430-8 EDN: MCMADR
Rai A. Explainable AI: From black box to glass box // Journal of the Academy of Marketing Science. 2020. Vol. 48. N 1. P. 137-141. DOI: 10.1007/s11747-019-00710-5 EDN: MNBEIB
Shneiderman B. Human-Centered Artificial Intelligence: Reliable, Safe & Trustworthy // International Journal of Human-Computer Interaction. 2020. Vol. 36. N 6. P. 495-504. DOI: 10.1080/10447318.2020.1741118 EDN: ENPGDI
Synofzik M., Vosgerau G., Newen A. Beyond the comparator model: A multifactorial two-step account of agency // Consciousness and Cognition. 2008. Vol. 17. N 1. P. 219-239. DOI: 10.1016/j.concog.2007.03.010
Waytz A., Heafner J., Epley N. The mind in the machine: Anthropomorphism increases trust in an autonomous vehicle // Journal of Experimental Social Psychology. 2014. Vol. 52. P. 113-117. DOI: 10.1016/j.jesp.2014.01.005
Wegner D.M. The Illusion of Conscious Will. Cambridge, MA: MIT Press, 2002. 419 p. DOI: 10.7551/mitpress/3650.001.0001
