The Dark Side of Artificial Intelligence: A Systematic Literature Review and Bibliometric Analysis
DOI:
https://doi.org/10.66635/05yfwh48Keywords:
artificial intelligence, generative AI, dark side, systematic literature review, PRISMA 2020, PICO, bibliometric analysis, governance, ethics, sustainability, AI infrastructureAbstract
Artificial intelligence is increasingly embedded in organizational decision-making, public administration, healthcare, marketing, education, scientific communication, and digital infrastructure. This expansion has produced a growing body of research on the dark side of AI, including ethical failure, accountability gaps, strategic risk, privacy loss, surveillance, misinformation, labour disruption, and environmental burden. This manuscript presents a systematic literature review supported by bibliometric analysis. The review is framed through PICO logic and reported using PRISMA 2020 principles. The Scopus dataset and associated evidence sources identified 86 records across the review corpus, screened 81 records after deduplication, considered 32 original Scopus papers as the main bibliometric evidence base, and included 19 studies in the core qualitative synthesis, with 15 supplementary sustainability and infrastructure records used as thematic extension evidence. The bibliometric analysis shows accelerated annual scientific production after 2022, dispersed source outlets, uneven country-level production and citation influence, fragmented collaboration structures, and a thematic map organized around artificial intelligence, risk management, risk assessment, governance, and emerging socio-technical concerns. The qualitative synthesis identifies eight domains of dark-side AI: sustainability and infrastructure burden; governance and accountability; ethics and human interests; sector-specific adoption harms; strategic and organizational risk; privacy, surveillance, and datafication; generative AI controversy and information integrity; and computational-efficiency mitigation. The review further develops a conceptual framework in which AI lifecycle activities increase infrastructure and resource demand, which mediates environmental and climate outcomes, while mitigation strategies moderate the pathway and climate feedback loops can reinforce energy and cooling pressures.
References
1.Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., Shamseer, L., Tetzlaff, J. M., Akl, E. A., Brennan, S. E., Chou, R., Glanville, J., Grimshaw, J. M., Hróbjartsson, A., Lalu, M. M., Li, T., Loder, E. W., Mayo-Wilson, E., McDonald, S., McGuinness, L. A., Stewart, L. A., Thomas, J., Tricco, A. C., Welch, V. A., Whiting, P., & Moher, D. (2021). The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. BMJ, 372, n71. https://doi.org/10.1136/bmj.n71
2.PRISMA. (2021). PRISMA 2020 statement. https://www.prisma-statement.org/prisma-2020-statement
3.Cochrane. (2025). Cochrane Handbook for Systematic Reviews of Interventions, Chapter 3: Defining the criteria for including studies and how they will be grouped for the synthesis. https://www.cochrane.org/authors/handbooks-and-manuals/handbook/current/chapter-03
4.Cochrane Library. (n.d.). About PICO. https://www.cochranelibrary.com/about-pico
5.Ajagekar, A., & You, F. (2024). Variational Quantum Circuit Learning-Enabled Robust Optimization for AI Data Center Energy Control and Decarbonization. Advances in Applied Energy. https://doi.org/10.1016/j.adapen.2024.100179
6.Channi, H. K., & Kumar, P. (2024). Green Data Centers and Renewable Energy. Practice, Progress, and Proficiency in Sustainability, 161-186. https://doi.org/10.4018/979-8-3693-6567-0.ch009
7.Guidi, G. M., Dominici, F., Gilmour, J., Butler, K. A., Bell, E. F., Delaney, S., & Bargagli-Stoffi, F. J. (2024). Environmental Burden of United States Data Centers in the Artificial Intelligence Era. https://doi.org/10.48550/arxiv.2411.09786
8.Herrera, M., Xie, X., Menapace, A., Zanfei, A., & Brentan, B. (2025). Sustainable AI infrastructure: A scenario-based forecast of water footprint under uncertainty. https://doi.org/10.31223/x55m86
9.Jiruwala, A. (2024). The Power Paradox: A Review of the Challenges and Solutions to the Energy Efficiency of AI and Cloud Computing. International Journal of Engineering and Advanced Technology, 14(2), 11-18. https://doi.org/10.35940/ijeat.b4554.14021224
10.Khatib, L., Pham, A., Ahmed, K., & Frenkel, V. S. (2025). Data Centers and Water: Challenges and Solutions for Sustainable Cooling. Journal American Water Works Association, 117(8), 48-53. https://doi.org/10.1002/awwa.2504
11.Elsworth, D., et al. (2025). How much energy does Google’s AI use? We did the math. Google Cloud Blog. https://cloud.google.com/blog/products/infrastructure/measuring-the-environmental-impact-of-ai-inference/
12.Ren, S. (2023). How much water does AI consume? OECD.AI Wonk. https://oecd.ai/en/wonk/how-much-water-does-ai-consume
13.Li, P., Yang, J., Islam, M. A., & Ren, S. (2025). Making AI Less Thirsty: Uncovering and Addressing the Secret Water Footprint of AI Models. https://arxiv.org/abs/2304.03271
14.Jegham, N., et al. (2025). How Hungry is AI? Benchmarking Energy, Water, and Carbon Footprint of LLM Inference. https://arxiv.org/abs/2505.09598
15.Husom, E. J., et al. (2024). The Price of a Prompt: Profiling Energy Use in Large Language Models Inference. https://arxiv.org/abs/2407.16893
16.Bashir, N., et al. (2025). Explained: Generative AI’s environmental impact. MIT News. https://news.mit.edu/2025/explained-generative-ai-environmental-impact-0117
17.MIT GenAI Impact Lab. (2024). The Climate and Sustainability Implications of Generative AI. https://mit-genai.pubpub.org/pub/8ulgrckc/release/2
18.International Energy Agency. (2025). Energy and AI: Energy demand from AI. https://www.iea.org/reports/energy-and-ai/energy-demand-from-ai
19.Rockström, J., et al. (2009). A safe operating space for humanity. Nature, 461, 472-475. https://doi.org/10.1038/461472a
20.Ryberg, M. W., Owsianiak, M., Richardson, K., & Hauschild, M. Z. (2018). Development of a life-cycle impact assessment methodology linked to the Planetary Boundaries framework. Ecological Indicators, 88, 250-262. https://doi.org/10.1016/j.ecolind.2017.12.065
21.Rockström, J., Steffen, W., Noone, K., Persson, Å., Chapin III, F. S., Lambin, E. F., Lenton, T. M., Scheffer, M., Folke, C., Schellnhuber, H. J., Nykvist, B., De Wit, C. A., Hughes, T., Van Der Leeuw, S., Rodhe, H., Sörlin, S., Snyder, P. K., Costanza, R., Svedin, U., ... Foley, J. A. (2009). A safe operating space for humanity. Nature, 461(7263), 472–475. https://doi.org/10.1038/461472a
22.Santarius, T. (2012). Green growth unravelled: How rebound effects baffle sustainability targets when the economy keeps growing. Wuppertal Institute for Climate, Environment and Energy.
23.Richardson, K., Steffen, W., Lucht, W., Bendtsen, J., Cornell, S. E., Donges, J. F., Drüke, M., Fetzer, I., Bala, G., Von Bloh, W., Feulner, G., Fiedler, S., Gerten, D., Gleeson, T., Hofmann, M., Huiskamp, W., Kummu, M., Mohan, C., Nogués-Bravo, D., ... Rockström, J. (2023). Earth beyond six of nine planetary boundaries. Science Advances, 9(37), eadh2458. https://doi.org/10.1126/sciadv.adh2458
24.Freitag, C., Berners-Lee, M., Widdicks, K., Knowles, B., Blair, G. S., & Friday, A. (2021). The real climate and transformative impact of ICT: A critique of estimates, trends, and regulations. Patterns, 2(9), 100340. https://doi.org/10.1016/j.patter.2021.100340
8.2 Bibliography of Original Scopus Papers Considered
1.Agrawal A.; Singh J.. (2025). The dark side of AI: Risks, ethics, and safeguarding human interests. Ethical AI Solutions for Addressing Social Media Influence and Hate Speech. https://doi.org/10.4018/979-8-3693-9904-0.ch007
2.Balola Y.H.A.. (2026). Government as a Strategic Actor: A Framework for Assessing Regulatory Risk in Digital Markets. Thunderbird International Business Review. https://doi.org/10.1002/tie.70137
3.Bhatt S.; Jhawar A.. (2025). Navigating the dark side of metaverse adoption in healthcare: ethical challenges, risks, and strategic interventions. International Journal of Pharmaceutical and Healthcare Marketing. https://doi.org/10.1108/IJPHM-12-2024-0138
4.Bibri S.E.; Allam Z.. (2022). The Metaverse as a Virtual Form of Data-Driven Smart Urbanism: On Post-Pandemic Governance through the Prism of the Logic of Surveillance Capitalism. Smart Cities. https://doi.org/10.3390/smartcities5020037
5.Burdyak P.. (2023). Model of risk management in railway transport. E3S Web of Conferences. https://doi.org/10.1051/e3sconf/202340206017
6.Chen D.. (2020). The possibility of evolution from non-strategic risk to strategic risk: The role of strategic assets. IOP Conference Series: Materials Science and Engineering. https://doi.org/10.1088/1757-899X/806/1/012045
7.Crocco E.; Ballesio E.; Broccardo L.; Alghafes R.. (2026). Welcome to the dark side: unveiling the potential risks related to the implementation of Artificial Intelligence. Journal of Risk Research. https://doi.org/10.1080/13669877.2025.2611954
8.Dadwal S.; Kumar P.; Verma R.; Singh G.. (2024). Drivers of SME growth and sustainability in emerging markets. Drivers of SME Growth and Sustainability in Emerging Markets. https://doi.org/10.4018/979-8-3693-0111-1
9.Demir C.M.; Demir Y.. (2026). Limits and Risks: The Dark Side of AI and Ethical Quandaries in Developing Marketing Stories. Developing Narrative Advertising and Marketing Discourse in the AI Era. https://doi.org/10.4018/979-8-3373-5666-2.ch009
10.Duan Y.; Shuplat O.; Matsuka V.; Lukash S.; Horbashevska M.; Kyslova L.. (2023). Risk Management Strategy for International Investment Projects of an Innovative Enterprise in the Context of Industry 4.0. Economic Affairs (New Delhi). https://doi.org/10.46852/0424-2513.4.2023.16
11.Freitas A.C.; Oliveira C.R.S.D.; Gomes P.F.D.O.; Chiroli D.M.D.G.; Gerônimo B.M.; Zola F.C.; Aragão F.V.. (2025). Risk management in smart cities: influence analysis using DEMATEL. Environmental Hazards. https://doi.org/10.1080/17477891.2025.2467637
12.Jagadhabi N.. (2025). Explainable AI-Driven Decision Support for Strategic Risk Management in Financial Services. International Academic Journal of Science and Engineering. https://doi.org/10.71086/IAJSE/V12I3/IAJSE1254
13.Kalvala V.; Gupta A.. (2025). Integrating Machine Learning and Statistical Models in Enterprise Risk Analysis. 4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025 - Proceedings. https://doi.org/10.1109/ICSADL65848.2025.10933366
14.Kasim A.; Anastasiou M.; Loizou E.; Dimakakos A.; Georganaki M.; Mourelatos I.; Karelis P.; Esposito M.; Zhou H.; Chen T.-T.; Skaltsas D.; Stockman P.. (2026). Strategic risk assessment in oncology: Utilizing single-agent activity to boost combination therapy approvals. Contemporary Clinical Trials Communications. https://doi.org/10.1016/j.conctc.2025.101590
15.Keong L.M.; Ling C.P.; Seong C.M.; Hau T.C.. (2026). AI-Driven Predictive Analytics in Business Risk Management. Studies in Systems, Decision and Control. https://doi.org/10.1007/978-3-032-11612-3_53
16.Kramer K.J.; Mousavi D.; Schmidt M.. (2022). Strategic Supply Chain Risk Management Artificial Intelligence and Big Data to Support Strategic Supply Chain Risk Management; [Strategisches Supply-Chain-Risikomanagement Einsatz von Künstlicher Intelligenz und Big Data zur Unterstützung des strategischen Supply-Chain-Risikomanagements]. ZWF Zeitschrift fuer Wirtschaftlichen Fabrikbetrieb. https://doi.org/10.1515/zwf-2022-1055
17.Kumkum S.; Tapan Mahmud M.; Adnan A.; Hasan M.K.. (2026). Impact of artificial intelligence on decision-making quality in mobile financial services in Bangladesh: the mediating role of risk mitigation. Journal of Decision Systems. https://doi.org/10.1080/12460125.2026.2620371
18.Madanchian M.; Taherdoost H.. (2025). Ethical theories, governance models, and strategic frameworks for responsible AI adoption and organizational success. Frontiers in Artificial Intelligence. https://doi.org/10.3389/frai.2025.1619029
19.Morabito, G. (2025). The intricate balance between artificial intelligence, ethics, and algorithmic standards. In D. Marino & M. A. Monaca (Eds.), Generative artificial intelligence and fifth industrial revolution (pp. 33–46). Springer. https://doi.org/10.1007/978-3-031-73880-7_4
20.Panazan O.; GheorgheC.. (2025). Strategic Risk Spillovers from Rare Earth Markets to Critical Industrial Sectors. International Journal of Financial Studies. https://doi.org/10.3390/ijfs13030156
21.Pantano E.; Marikyan D.; Papagiannidis S.. (2024). The dark side of artificial intelligence for industrial marketing management: Threats and risks of AI adoption. Industrial Marketing Management. https://doi.org/10.1016/j.indmarman.2023.11.008
22.Pokrovskaya M.. (2019). Risk mitigation based on innovative solutions. Proceedings of the International Astronautical Congress, IAC.
23.Rodriguez, E., & Edwards, J. S. (2020). AI in smart cities development: A perspective of strategic risk management. In M. M. Cruz-Cunha, E. Portela, & R. Rijo (Eds.), Data analytics and AI (pp. 133–150). Auerbach Publications. https://doi.org/10.1201/9781003019855-8
24.Skvortsova M.; Grout V.. (2018). Basic approaches to assessing risks and threats in decision support systems. Proceedings of the 2018 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering, ElConRus 2018. https://doi.org/10.1109/EIConRus.2018.8317397
25.Sposato M.; Dittmar E.C.; Portillo J.P.V.. (2026). Navigating the dark side of AI in service ecosystems: an ethical leadership framework for risk mitigation; [穿越服务生态系统中人工智能的阴暗面：基于伦理领导力的风险缓解框架]. Service Industries Journal. https://doi.org/10.1080/02642069.2026.2643384
26.Trzaska R.; Sus A.. (2023). Industry 4.0 business strategic risks based on the scalability 4.0 concept. Artificial Intelligence area. Procedia Computer Science. https://doi.org/10.1016/j.procs.2023.10.319
27.Wach K.; Duong C.D.; Ejdys J.; Kazlauskaitė R.; Korzynski P.; Mazurek G.; Paliszkiewicz J.; Ziemba E.. (2023). The dark side of generative artificial intelligence: A critical analysis of controversies and risks of ChatGPT. Entrepreneurial Business and Economics Review. https://doi.org/10.15678/EBER.2023.110201
28.Wirtz B.W.; Weyerer J.C.; Sturm B.J.. (2020). The Dark Sides of Artificial Intelligence: An Integrated AI Governance Framework for Public Administration. International Journal of Public Administration. https://doi.org/10.1080/01900692.2020.1749851
29.Wu Y.; Zheng H.; Li D.. (2026). Generative Artificial Intelligence and Corporate Risk-Taking: A Stylized Theoretical Framework with Evidence. Proceedings of 2026 2nd International Conference on Generative Artificial Intelligence and Digital Media, GADM 2026. https://doi.org/10.1145/3803730.3803756
30.Xu B.. (2025). Artificial Intelligence-Driven Strategic Risk Forecasting: Integration of Random Forest and GBDT Models with Real-Time Updates. ISMSIT 2025 - 9th International Symposium on Multidisciplinary Studies and Innovative Technologies, Proceedings. https://doi.org/10.1109/ISMSIT67332.2025.11267847
31.Zecca P.A.; Marcella R.; Andrea B.; Marina P.; Marina B.; Mario R.. (2023). The Dark Side of Artificial Intelligence: The Possible Risk of Falsifying Images for Scientific Articles. Microscopy and Microanalysis. https://doi.org/10.1093/micmic/ozad093
32.van der Rest J.-P.; Heidary K.. (2024). Personalised pricing under the current European legal framework: a call for ethics, sustainability and responsibility in the age of artificial intelligence. Research in Hospitality Management. https://doi.org/10.1080/22243534.2024.2357037
