Risks Associated with the Use of Artificial Intelligence: A Comprehensive Review
DOI:
https://doi.org/10.67308/irjist.056Keywords:
Artificial Intelligence, AI Risks, Data Privacy, Algorithmic Bias, Explainable AI, Responsible AI, AI Governance;, Cyber SecurityAbstract
Artificial Intelligence (AI) is increasingly being adopted across healthcare, finance, manufacturing, cybersecurity, education, and other sectors because of its ability to support prediction, automation, pattern recognition, and decision-making. However, the growing use of AI also introduces technical, security, economic, ethical, and social risks. This study examines the major risks associated with AI and classifies them into data privacy and security, algorithmic bias, lack of transparency and explainability, cybersecurity threats, economic and employment effects, and ethical and social concerns. A qualitative review-based methodology is used to examine relevant research and established AI risk-management frameworks. The study further discusses how these risks can affect different industries and reviews mitigation approaches including data governance, explainable AI, cybersecurity controls, human oversight, continuous monitoring, and governance mechanisms. The findings indicate that AI risk management should be addressed throughout the AI lifecycle rather than only after system deployment. A coordinated approach involving technical safeguards, organizational practices, human oversight, and appropriate governance can support more responsible and risk-aware deployment of AI systems.
Downloads
References
[1] E. Tabassi, “Artificial Intelligence Risk Management Framework (AI RMF 1.0),” National Institute of Standards and Technology, NIST AI 100-1, 2023,
DOI: 10.6028/NIST.AI.100-1.
[2] L. Weidinger et al., “Taxonomy of Risks Posed by Language Models,” Proceedings of the 2022 ACM Conference on Fairness, Accountability, and Transparency, pp. 214–229, 2022,
DOI: 10.1145/3531146.3533088.
[3] E. M. Bender, T. Gebru, A. McMillan-Major, and S. Shmitchell, “On the Dangers of Stochastic Parrots: Can Language Models Be Too Big?” Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency, pp. 610–623, 2021,
DOI: 10.1145/3442188.3445922.
[4] T. Gebru et al., “Datasheets for Datasets,” Communications of the ACM, vol. 64, no. 12, pp. 86–92, 2021,
DOI: 10.1145/3458723.
[5] J. Buolamwini and T. Gebru, “Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification,” Proceedings of Machine Learning Research, vol. 81, pp. 77–91, 2018.
[6] M. T. Ribeiro, S. Singh, and C. Guestrin, “‘Why Should I Trust You?’: Explaining the Predictions of Any Classifier,” Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1135–1144, 2016,
DOI: 10.1145/2939672.2939778.
[7] S. M. Lundberg and S.-I. Lee, “A Unified Approach to Interpreting Model Predictions,” Advances in Neural Information Processing Systems, vol. 30, 2017.
[8] A. Ilyas et al., “Adversarial Examples Are Not Bugs, They Are Features,” Advances in Neural Information Processing Systems, vol. 32, 2019.
[9] B. Biggio, I. Corona, D. Maiorca, B. Nelson, N. Šrndić, P. Laskov, G. Giacinto, and F. Roli, “Poisoning Attacks against Support Vector Machines,” Proceedings of the 29th International Conference on Machine Learning, pp. 1467–1474, 2012.
[10] P. Bisht, “AI-Based Detection of Phishing Attacks in Digital Payment Systems Using Explainable Machine Learning,” International Research Journal of Innovation in Science and Technology (IRJIST), vol. 1, no. 3, 2026, Article ID IRJIST-2026-083,
DOI: 10.67308/irjist.083.
[11] Telefónica, “EU Artificial Intelligence Act: Risk levels,” Telefónica, 2022.
[12] R. Sharma, “AI Security: Threats, Frameworks, Best Practices & More,” Markovate, July 15, 2025.
[13] PwC Singapore, “Data Risk Framework,” PwC Singapore, 2026.
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Sujal Mutgekar, Sanika More, Vipul Zope, Vedant Bhangale (Author)
This work is licensed under a Creative Commons Attribution 4.0 International License.
Articles published in the International Research Journal of Innovation in Science and Technology (IRJIST) are licensed under the Creative Commons Attribution 4.0 International License (CC BY 4.0).
This license permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Authors retain the copyright of their work and grant the journal the right of first publication. Proper attribution to the authors and the journal is required for any reuse of the published content.
