(untitled) (Contemporary Journal of Social Science Review, 2025)
The advent of highly sophisticated autonomous systems, ranging from self-driving vehicles and lethal autonomous weapons to AI-driven diagnostic tools in healthcare, has precipitated a profound crisis in traditional moral and legal frameworks. Central to this crisis is the "responsibility gap"—a phenomenon where the increasing autonomy of machines threatens to decouple human agency from mechanical outcomes. This research provides a comprehensive social and ethical analysis of human responsibility in the age of artificial intelligence. By examining the evolution of "learning automata" and the resulting challenges to traditional theories of blameworthiness, the paper explores the move from regulative control to "meaningful human control" (MHC). Through an expanded literature review and in-depth case studies—including the 2018 Uber autonomous vehicle fatality, the Boeing 737 MAX MCAS failures, the COMPAS algorithmic bias controversy, and the failure of IBM Watson for Oncology—this analysis identifies the emergence of "moral crumple zones" and "accountability shields." Furthermore, the research integrates 2024–2025 statistical data and international policy trajectories from the United Nations Group of Governmental Experts (GGE) to argue for a transition toward collective, forward-looking responsibility models. Ultimately, the paper concludes that while individual blame may be increasingly difficult to attribute, moral responsibility can be preserved through the rigorous design of socio-technical infrastructures that prioritize tracking human values and tracing causal accountability.