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AI safety is motivated by the need to prevent both near-term harms and potentially catastrophic outcomes from AI systems. Scholars and agencies highlight risks such as failures in critical systems, bias and unfairness, and AI-enabled surveillance. They also point to misuse risks including weaponization, automated cyberattacks, and even bioterrorism, alongside societal disruptions like technological unemployment and digital manipulation. A central motivation is “existential safety,” the concern that sufficiently advanced AI could lead to extreme outcomes such as loss of human control or even human extinction. While experts disagree on how severe these risks are and what their primary sources would be, surveys indicate that many researchers take high-consequence scenarios seriously. This motivates research and policy efforts aimed at ensuring AI systems remain safe as capabilities grow, including technical work (robustness, monitoring, alignment) and governance approaches (norms, regulation, and institutional coordination).
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