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Motivations for AI safety include preventing accidents and misuse, such as bias, surveillance, weaponization, cyberattacks, and bioterrorism.
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).
Motivations for AI safety include preventing accidents and misuse, such as bias, surveillance, weaponization, cyberattacks, and bioterrorism.
A major driver is existential safety: the possibility that advanced AI could cause extreme, irreversible harm if control and alignment fail.
AI safety efforts combine technical research (robustness, monitoring, alignment) with governance and policy to keep pace with rapidly improving AI capabilities.
Efforts and concerns focused on preventing extreme outcomes, including potential loss of human control or human extinction from advanced AI.
The goal of steering AI systems to behave according to intended goals, preferences, or ethical principles.
Techniques for observing AI systems in operation to detect risks, estimate uncertainty, and identify malicious or abnormal behavior.
The ability of AI systems to resist inputs crafted to cause incorrect or harmful behavior, including attacks on classifiers and reward/evaluation models.
A maliciously implanted vulnerability that triggers harmful behavior under specific conditions, potentially bypassing standard safety measures.
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