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AI Safety
Abstract
This study proposes a comprehensive framework for AI safety as the foundation for developing secure, controllable, and human-aligned artificial intelligence systems. Through a systematic review of literature from 2021 to 2025, it identifies three core pillars: technical robustness against disruptions, ethical value alignment, and socio-regulatory governance. The analysis covers real-world cases of medical diagnostic vulnerabilities, autonomous vehicle failures, and manipulation risks in large language models (LLMs). Mitigation approaches such as safe reinforcement learning, formal verification, and human-in-the-loop mechanisms are explored. The study also highlights the importance of global standards, including ISO/IEC 23894 and the NIST AI Risk Management Framework, in ensuring transparency and accountability. Findings suggest that cross-disciplinary collaboration among scientists, regulators, and civil society is crucial to building a trustworthy and ethically responsible AI ecosystem.
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