THE ETHICAL, LEGAL, AND SOCIAL IMPLICATIONS OF DEPLOYING AGENTIC AI: EXAMINING AUTONOMY, ACCOUNTABILITY, AND HUMAN OVERSIGHT IN HIGHLY AUTOMATED DECISION-MAKING SYSTEMS
DOI:
https://doi.org/10.29121/digisecforensics.v2.i1.2025.83Keywords:
Agentic AI, Ethical Implications, Legal Accountability, Human Oversight, Autonomous Decision-Making, Ai Governance, Social Impacts, Algorithmic BiasAbstract
The deployment of agentic AI—autonomous systems capable of independent decision-making—raises significant ethical, legal, and social challenges, particularly in relation to autonomy, accountability, and human oversight. This study adopts a mixed-methods approach, integrating a scoping review of 25 scholarly sources published between 2016 and 2024, an analysis of 150 documented AI-related incidents from publicly accessible databases reported between 2020 and 2024, and survey responses from 500 stakeholders engaged in AI governance and policy discourse. The findings indicate that approximately 78% of reported incidents are associated with insufficient human oversight, contributing to accountability gaps in high-risk domains such as healthcare and finance. Emerging regulatory frameworks, including the early provisions of the EU AI Act (2024), emphasize the necessity of human oversight, yet preliminary analyses suggest limitations in operational clarity and enforcement preparedness. Furthermore, survey data reveal that 62% of respondents express distrust toward highly autonomous AI systems, primarily due to perceived risks associated with diminished human control. The study underscores the importance of hybrid human–AI decision-making models to reconcile efficiency with ethical responsibility. It concludes by advocating for interdisciplinary governance strategies that enhance transparency, accountability, and equity, thereby supporting the sustainable and responsible integration of agentic AI into socio-technical systems.
References
Arora, P., and Bhardwaj, S. (2022). An Analysis of Artificial Intelligence Methods for Network Intrusion Detection and Prevention to Improve User Privacy. International Journal of Innovative Research in Computer and Communication Engineering, 10(11).
Arora, P., and Bhardwaj, S. (2022). Integrating Wireless Sensor Networks and the Internet of Things: A Hierarchical and Security-Based Analysis. International Journal of Multidisciplinary Research in Science, Engineering and Technology (IJMRSET), 5(5).
Arora, P., and Bhardwaj, S. (2023). Examining Cloud Computing Data Confidentiality Techniques to Achieve Higher Security in Cloud Storage. International Journal of Multidisciplinary Research in Science, Engineering and Technology (IJMRSET), 6(10).
Arora, P., and Bhardwaj, S. (2023). Techniques to Implement Security Solutions and Improve Data Integrity and Security in Distributed Cloud Computing. International Journal of Multidisciplinary Research in Science, Engineering and Technology (IJMRSET), 6(6).
Bartsch, S. C., Benlian, A., and Sunyaev, A. (2024). Accountability in Artificial Intelligence: Conceptual Foundations, Governance Mechanisms, and Research Directions. Information Systems Frontiers, 26(1), 1–17. https://doi.org/10.1007/s10796-022-10246-3
Bhardwaj, S., Dwivedi, A., Pandey, A., Perwej, Y., and Khan, P. R. (2023). Machine Learning-Based Crowd Behavior Analysis and Forecasting. International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT).
Cheong, B. C. (2024). Transparency and Accountability in AI Systems: Safeguarding Wellbeing in the Age of Algorithmic Decision-Making. Frontiers in Human Dynamics, 6, Article 1421273. https://doi.org/10.3389/fhumd.2024.1421273
Floridi, L., Cowls, J., Beltrametti, M., Chatila, R., Chazerand, P., Dignum, V., Luetge, C., Madelin, R., Pagallo, U., Rossi, F., Schafer, B., Valcke, P., and Vayena, E. (2018). AI4 People: An Ethical Framework for a Good AI Society: Opportunities, Risks, Principles, and Recommendations. Minds and Machines, 28(4), 689–707. https://doi.org/10.1007/s11023-018-9482-5
Floridi, L., and Taddeo, M. (2016). What Is Data Ethics? Philosophical Transactions of the Royal Society A, 374(2083). https://doi.org/10.1098/rsta.2016.0360
Hagendorff, T. (2020). The Ethics of AI Ethics: An Evaluation of Guidelines. Minds and Machines, 30(1), 99–120. https://doi.org/10.1007/s11023-020-09517-8
Helberger, N., Pierson, J., and Poell, T. (2018). Governing Online Platforms: From Contested to Cooperative Responsibility. The Information Society, 34(1), 1–14. https://doi.org/10.1080/01972243.2017.1391913
Larsson, S. (2017). On the Legitimacy of Algorithmic Decision Systems: Law Enforcement and the Prediction of Recidivism.
McKinsey and Company. (2024). The State of AI in Early 2024.
Mittelstadt, B. D., Allo, P., Taddeo, M., Wachter, S., and Floridi, L. (2016). The Ethics of Algorithms: Mapping the Debate. Big Data and Society, 3(2). https://doi.org/10.1177/2053951716679679
Papagiannidis, E., Mikalef, P., and Gupta, M. (2024). Responsible Artificial Intelligence: A Systematic Review and Future Research Agenda. The Journal of Strategic Information Systems, 33(2), Article 101860. https://doi.org/10.1016/j.jsis.2024.101860
Pew Research Center. (2022). Americans' Views of Artificial Intelligence.
Ryan, M., and Stahl, B. C. (2021). Artificial Intelligence Ethics Guidelines for Developers and Users: Clarifying Their Content and Normative Implications. Journal of Information, Communication and Ethics in Society, 19(1), 61–86. https://doi.org/10.1108/JICES-12-2019-0138
Sharma, S. (2020). The Rising Threat of Deepfakes: Security and Privacy Implications. Journal of Artificial Intelligence and Cyber Security (JAICS), 4(1), 1–6.
Sharma, S. (2021). Multi-Cloud Environments: Reducing Security Risks in Distributed Architectures. Journal of Artificial Intelligence and Cyber Security (JAICS), 5(1), 1–6.
Tambi, V. K. (2023). Efficient Message Queue Prioritization in Kafka for Critical Systems. The Research Journal (TRJ), 9(1), 1–16.
Tambi, V. K. (2023). Real-Time Data Stream Processing with Kafka-Driven AI Models. International Journal of Current Engineering and Scientific Research (IJCESR).
Tambi, V. K., and Singh, N. (2019). Development of a Project Risk Management System Based on Industry 4.0 Technology and Its Practical Implications. International Journal of Innovative Research in Computer and Communication Engineering, 7(11).
Tambi, V. K., and Singh, N. (2019). Enhancing Safety Through Cyberattack Mitigation and Traffic Impact Analysis for Connected Automated Vehicles. International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering, 8(1).
Tambi, V. K., and Singh, N. (2021). New Applications of Machine Learning and Artificial Intelligence in Cybersecurity Vulnerability Management. International Journal of Advanced Research in Education and Technology (IJARETY), 8(2).
Vamplew, P., and Dazeley, R. (2021). A Multi-Dimensional View of the Fairness of AI Under the Lens of the EU AI Act. Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, 144–150. https://doi.org/10.1145/3461702.3462562
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