DECISION SYNERGY MECHANISM (DSM): INTEGRATING LEADERSHIP AND GENERATIVE AI TO MITIGATE COGNITIVE BIASES
Abstract
In turbulent managerial environments, the exponential growth of data and the rapid spread of generative AI amplify the cognitive vulnerabilities of organizational leaders, particularly automation and confirmation biases in high stakes decisions. These vulnerabilities are further intensified by the lack of structured frameworks that govern human-algorithm interaction in real organizational contexts. This article aims to develop and conceptually justify the Decision Synergy Mechanism (DSM) as a novel human-algorithm framework that integrates strategic leadership, generative AI capabilities, and rigorous ethical oversight to mitigate such biases. The study applies an integrative literature review of peer reviewed sources from 2020–2026 indexed in Scopus and Web of Science, combining multi-disciplinary insights from management, organizational psychology, and information systems. Using concept matrices, it synthesizes Dual Process Theory, the socio cognitive model of trust in automation, and the Hybrid Intelligence paradigm to deductively derive the comprehensive DSM architecture. The results introduce a three level DSM model that explicitly links contextual leadership preconditions, AI enabled synergy processes, and trust calibration outcomes. Furthermore, the framework reconceptualizes generative AI from a passive information "oracle" into an active cognitive opponent that creates deliberate cognitive friction through advanced AI Reasoning, dialectical questioning, and structured prompt engineering. The article formulates three distinct propositions regarding the inverted U shaped relationship between AI literacy and decision weight allocation, the role of contextual AI explanations in calibrating user trust, and the bias reducing effect of AI generated counterfactuals under extreme time pressure and task complexity. The proposed mechanism effectively resolves the "trust paradox" by preventing both blind rejection and blind reliance on AI systems, and can directly inform the design of hybrid decision architectures, leadership development programs, and AI governance policies in ethically sensitive, high complexity operational domains.
References
Wen Y., Wang J., Chen X. Trust and AI Weight: Human-AI Collaboration in Organizational Management Decision-Making. Frontiers in Organizational Psychology. 2025. Vol. 3. Art. 1419403. DOI: https://doi.org/10.3389/forgp.2025.1419403.
Li H., Tian F. Advancing Decision-Making through AI-Human Collaboration: A Systematic Review and Conceptual Framework. 2025. DOI: https://doi.org/10.21203/rs.3.rs-6885768/v1.
Albashrawi M. Generative AI for Decision-Making: A Multidisciplinary Perspective. Journal of Innovation & Knowledge. 2025. Vol. 10. № 4. Art. 100751. DOI: https://doi.org/10.1016/j.jik.2025.100751.
Bertoncini A. L., Matsushita R., Da Silva S. AI, Ethics, and Cognitive Bias: An LLM-Based Synthetic Simulation for Education and Research. AI in Education. 2025. Vol. 1. № 1. Art. 3. DOI: https://doi.org/10.3390/aieduc1010003.
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. P. 1-21. DOI: https://doi.org/10.1145/3449287.
Hybrid Intelligence / D. Dellermann et al. Business & Information Systems Engineering. 2019. Vol. 61. № 5. P. 637-643. DOI: https://doi.org/10.1007/s12599-019-00595-2.
Evans J. St. B. T., Stanovich K. E. Dual-Process Theories of Higher Cognition: Advancing the Debate. Perspectives on Psychological Science. 2013. Vol. 8. № 3. P. 223-241. DOI: https://doi.org/10.1177/1745691612460685.
As’ad M., Faran N., Joharji H. AI-Supported Shared Decision-Making [AI-SDM]: Conceptual Framework. JMIR AI. 2025. Vol. 4. Art. e75866. DOI: https://doi.org/10.2196/75866.
Hoff K. A., Bashir M. Trust in Automation: Integrating Empirical Evidence on Factors That Influence Trust. Human Factors. 2015. Vol. 57. № 3. P. 407-434. DOI: https://doi.org/10.1177/0018720814547570.
Pramod D. Decoding Responsible AI Use: The Influence of Digital Literacy and Ethical Awareness. Cogent Education. 2025. Vol. 12. № 1. Art. 2592371. DOI: https://doi.org/10.1080/2331186X.2025.2592371.
Logg J. M., Minson J. A., Moore D. A. Algorithm Appreciation: People Prefer Algorithmic to Human Judgment. Organizational Behavior and Human Decision Processes. 2019. Vol. 151. P. 90-103. DOI: https://doi.org/10.1016/j.obhdp.2018.12.005.
Toward a Science of Human-AI Teaming for Decision Making: A Complementarity Framework / C. Gonzalez et al. PNAS Nexus. 2026. Vol. 5. № 3. Art. pgag030. DOI: https://doi.org/10.1093/pnasnexus/pgag030.
Amplifying Minority Voices: AI-Mediated Devil’s Advocate System for Inclusive Group Decision-Making / S. Lee et al. Companion Proceedings of the 30th International Conference on Intelligent User Interfaces. Cagliari, Italy : ACM, 2025. P. 17-21. DOI: https://doi.org/10.1145/3708557.3716334.
Enhancing AI-Assisted Group Decision Making through LLM-Powered Devil’s Advocate / C.-W. Chiang et al. Proceedings of the 29th International Conference on Intelligent User Interfaces. Greenville, SC, USA : ACM, 2024. P. 103-119. DOI: https://doi.org/10.1145/3640543.3645199.
Hsu W.-C., Wang M.-C., Ting H.-I. Reducing Emotional Bias in Investment Decisions: The Role of GPT-4 in Financial Analysis. Asia-Pacific Journal of Business Administration. 2025. P. 1-26. DOI: https://doi.org/10.1108/APJBA-03-2025-0181.
Bastone A., Mandiello A., Zeuli F. Generative Artificial Intelligence and Decision-Making: Evidence from a Participant Observation with Latent Entrepreneurs. European Journal of Innovation Management. 2026. P. 1-21. DOI: https://doi.org/10.1108/EJIM-03-2025-0388.
AI-Driven Leadership: Decision-Making, Competencies, and Ethical Challenges—A Systematic Review / A. Sacavém et al. Administrative Sciences. 2026. Vol. 16. № 4. Art. 173. DOI: https://doi.org/10.3390/admsci16040173.
Snyder H. Literature Review as a Research Methodology: An Overview and Guidelines. Journal of Business Research. 2019. Vol. 104. P. 333-339. DOI: https://doi.org/10.1016/j.jbusres.2019.07.039.
Torraco R. J. Writing Integrative Literature Reviews: Using the Past and Present to Explore the Future. Human Resource Development Review. 2016. Vol. 15. № 4. P. 404-428. DOI: https://doi.org/10.1177/1534484316671606.
Dressel J., Farid H. The Accuracy, Fairness, and Limits of Predicting Recidivism. Science Advances. 2018. Vol. 4. № 1. Art. eaao5580. DOI: https://doi.org/10.1126/sciadv.aao5580.
Dissecting Racial Bias in an Algorithm Used to Manage the Health of Populations / Z. Obermeyer et al. Science. 2019. Vol. 366. № 6464. P. 447-453. DOI: https://doi.org/10.1126/science.aax2342.
Porkodi S., Cedro T. L. The Ethical Role of Generative Artificial Intelligence in Modern HR Decision-Making: A Systematic Literature Review. European Journal of Business and Management Research. 2025. Vol. 10. № 1. P. 44-55. DOI: https://doi.org/10.24018/ejbmr.2025.10.1.2535.
Okamura K., Yamada S. Adaptive Trust Calibration for Human-AI Collaboration. PLoS ONE. 2020. Vol. 15. № 2. Art. e0229132. DOI: https://doi.org/10.1371/journal.pone.0229132.
Copyright (c) 2026 Арсеній Бойко
This work is licensed under a Creative Commons Attribution 4.0 International License.
