Artificial Intelligence is a liability

Abstract: According to Dr. Karl Branting of Mitre, “Artificial Intelligence” or AI can be described as “the field that seeks technology capable of human-like reasoning, perception, and control.” Machine learning is a form of AI that modifies its own programming as it “learns” via exposure to new data. Accordingly, unless otherwise differentiated, my use of “Artificial Intelligence” or “AI” refers to forms of machine learning. Artificial intelligence (AI) has swiftly shifted from the realm of specialized laboratories to the fabric of everyday life, driving applications in healthcare, finance, education, mobility and governance. Its advocates highlight its transformative potential, from productivity gains to novel forms of creativity. Yet the propagation of AI poses risks that are profound, systemic and, in many cases, irreversible. As the Stanford AI Index (2024) warns, “the pace of deployment has far outstripped the pace of governance,” creating a governance gap with real societal consequences. Drawing upon case studies from healthcare, autonomous vehicles, electoral misinformation, labor automation, privacy scandals and environmental costs, this paper argues that the unchecked propagation of AI undermines democracy, deepens inequality, erodes trust in institutions, threatens human dignity and jeopardizes ecological sustainability. Governance frameworks acknowledge systemic risks but remain insufficient to address the accelerating scale and complexity of frontier AI models. The paper concludes with recommendations for targeted moratoria, mandatory auditing, environmental accountability and democratic oversight.

Artificial intelligence, propagation, governance, risk, misinformation, labor markets, environment, deep fakes, accountability, precaution

1. Attri, P. S., Joshi, C., & Bapuji, H. (2021). Cisco Systems Inc.: Caste conundrum regarding diversity and inclusion. Ivey Publishing. [Google Scholar] [Crossref]

2. Bender, E., Gebru, T., McMillan-Major, A., & Shmitchell, S. (2021). On the dangers of stochastic parrots: Can language models be too big? Proceedings of FAccT. [Google Scholar] [Crossref]

3. Bengio, Y. (2019). The challenges of general AI. Keynote, NeurIPS Conference. [Google Scholar] [Crossref]

4. Burrell, J. (2016). How the machine “thinks”: Understanding opacity in machine learning algorithms. Big Data & Society, 3(1), 1–12. [Google Scholar] [Crossref]

5. Cadwalladr, C. (2018). The Cambridge Analytica files. The Guardian. [Google Scholar] [Crossref]

6. Carr, N. (2011). The shallows: What the Internet is doing to our brains. W. W. Norton. [Google Scholar] [Crossref]

7. Crawford, K. (2021). Atlas of AI: Power, politics, and the planetary costs of artificial intelligence. Yale University Press. [Google Scholar] [Crossref]

8. Creemers, R. (2018). China’s social credit system: An evolving practice of control. SSRN Working Paper. [Google Scholar] [Crossref]

9. European Commission. (2021). Artificial intelligence liability framework. Brussels: EC. [Google Scholar] [Crossref]

10. European Union. (2024). Artificial Intelligence Act. Official Journal of the European Union, July 12. [Google Scholar] [Crossref]

11. Google. (2024). Environmental report 2024 (FY2023 data). Google Sustainability Office. [Google Scholar] [Crossref]

12. International Energy Agency (IEA). (2025). Energy and AI: Projections to 2030. Paris: IEA. [Google Scholar] [Crossref]

13. International Labour Organization (ILO). (2022). The role of digital labour platforms in transforming the world of work. Geneva: ILO. [Google Scholar] [Crossref]

14. Le Monde. (2024). India’s general election is being impacted by deepfakes. Le Monde. [Google Scholar] [Crossref]

15. Lum, K., & Isaac, W. (2016). To predict and serve? Significance, 13(5), 14–19. [Google Scholar] [Crossref]

16. Marcus, G. (2022). The next decade in AI: Four false hopes. arXiv preprint. [Google Scholar] [Crossref]

17. Murati, M. (2023). Interview on limitations of AI. Financial Times. [Google Scholar] [Crossref]

18. National Institute of Standards and Technology (NIST). (2023). AI risk management framework (AI RMF 1.0). U.S. Department of Commerce. [Google Scholar] [Crossref]

19. National Transportation Safety Board (NTSB). (2019). Collision between vehicle controlled by developmental automated driving system and pedestrian, Tempe, Arizona, March 18, 2018 (NTSB/HAR-19/03). Washington, DC. [Google Scholar] [Crossref]

20. Noble, S. (2018). Algorithms of oppression: How search engines reinforce racism. NYU Press. [Google Scholar] [Crossref]

21. O’Connor, C., & Weatherall, J. (2019). The misinformation age: How false beliefs spread. Yale University Press. [Google Scholar] [Crossref]

22. Patterson, D., Gonzalez, J., & Le, Q. V. (2021). The carbon footprint of AI training. arXiv preprint. [Google Scholar] [Crossref]

23. Reuters. (2018). Amazon scraps secret AI recruiting tool that showed bias against women. Reuters Newswire. [Google Scholar] [Crossref]

24. Stanford University. (2024). AI Index Report 2024. Human-Centered AI Institute. [Google Scholar] [Crossref]

25. STAT News. (2018). IBM’s Watson recommended ‘unsafe and incorrect’ cancer treatments, internal documents show. STAT News. [Google Scholar] [Crossref]

26. Turkle, S. (2017). Alone together: Why we expect more from technology and less from each other. Basic Books. [Google Scholar] [Crossref]

27. UNESCO. (2021). Recommendation on the ethics of artificial intelligence. Paris: UNESCO. [Google Scholar] [Crossref]

28. United Nations. (2021). The right to privacy in the digital age. Office of the High Commissioner for Human Rights. [Google Scholar] [Crossref]

29. United Nations. (2022). Global e-waste monitor 2022. UN University. [Google Scholar] [Crossref]

30. Universities UK. (2023). Generative AI and higher education: Challenges and opportunities. London: Universities UK. [Google Scholar] [Crossref]

31. Urbina, F., et al. (2022). Dual-use of artificial intelligence: Risks and regulation. Nature Machine Intelligence, 4(5), 476–484. [Google Scholar] [Crossref]

32. Vosoughi, S., Roy, D., & Aral, S. (2018). The spread of true and false news online. Science, 359(6380), 1146–1151. [Google Scholar] [Crossref]

33. World Economic Forum (WEF). (2024). Year of elections: Lessons from India’s fight against AI-generated misinformation and deepfakes. Geneva: WEF. [Google Scholar] [Crossref]

34. Zuboff, S. (2019). The age of surveillance capitalism. PublicAffairs. [Google Scholar] [Crossref]

35. Kathuria, R. (2025). What do they know of AI who only AI know. The Tribune. [Google Scholar] [Crossref]

36. Partrick E. McSharry, Promoting AI Ethics Through Awareness and Case Studies. [Google Scholar] [Crossref]

37. Neil Selwyn. One the Limits of Artificial intelligence (AI) in Education. [Google Scholar] [Crossref]

38. Sayed Fayaz Ahmad, et al. Impact of artificial intelligence on human loss in decision making, laziness and safety in education. [Google Scholar] [Crossref]

39. Vidsushi Marda, Artificial intelligence policy in India: a framework for engaging the limits of data-driven decision-making. [Google Scholar] [Crossref]

40. Patricia Gomes, Carlos Denner dos santos, Josivania Silva Farias. Artificial Intelligence Regulation: a framework for governance. [Google Scholar] [Crossref]

41. European Union’s AI Act [Google Scholar] [Crossref]

42. U.S. National Institute of Standards and Technology (NIST) AI Risk Management Framework (AI RMF) [Google Scholar] [Crossref]

43. The OECD’s AI Principles, the Canadian government’s Directive on Automated Decision-Making and instruction by UK’s Centre for Data Ethics and Innovation. [Google Scholar] [Crossref]

44. MIT AI Risk Mitigation Taxonomy [Google Scholar] [Crossref]

45. Data from organizations like the World Economic Forum and Goldman Sachs [Google Scholar] [Crossref]

46. The Stanford AI Index (2024) [Google Scholar] [Crossref]

47. Marda, V. (2018). Artificial intelligence policy in India: a framework for engaging the limits of data-driven decision-making. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 376(2133), 20180087. [Google Scholar] [Crossref]

• Advanced Techniques for Fake News Detection on Twitter Using NLP and AI: A Comprehensive Review
• Review of Self Compacting Geopolymer Concrete Using Slag Sand as Fine Aggregate
• Performance of Local Construction Contractors – Case Study of Registered Contractors in Monrovia, Liberia
• Cross-Cultural Perspectives on Innovation Management in Multinational Organizations
• Modeling of Reaction Between Dissolved Oxygen (DO) And Biological Oxygen Demand (BOD) in Degradation River