Year: 2025
Type: article
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.
Author: Rupinder Pal Singh
Language: English
Subfield: Safety Research
Field: Social Sciences
Domain: Social Sciences
Open Access status: hybrid
OpenAlex ID: W4414199887