Absent intention, AI development causes malicious use, arms race, accidental leaks, and goes rogue.

Catastrophic AI risks can be grouped under four key categories which we explore below, and in greater depth in CAIS’ linked paper: Malicious use: People could intentionally harness powerful AIs to cause widespread harm. AI could be used to engineer new pandemics or for propaganda, censorship, and surveillance, or released to autonomously pursue harmful goals. To reduce these risks, we suggest improving biosecurity, restricting access to dangerous AI models, and holding AI developers liable for harms. AI race: Competition could push nations and corporations to rush AI development, relinquishing control to these systems. Conflicts could spiral out of control with autonomous weapons and AI-enabled cyberwarfare. Corporations will face incentives to automate human labor, potentially leading to mass unemployment and dependence on AI systems. As AI systems proliferate, evolutionary dynamics suggest they will become harder to control. We recommend safety regulations, international coordination, and public control of general-purpose AIs. Organizational risks: There are risks that organizations developing advanced AI cause catastrophic accidents, particularly if they prioritize profits over safety. AIs could be accidentally leaked to the public or stolen by malicious actors, and organizations could fail to properly invest in safety research. We suggest fostering a safety-oriented organizational culture and implementing rigorous audits, multi-layered risk defenses, and state-of-the-art information security. Rogue AIs: We risk losing control over AIs as they become more capable. AIs could optimize flawed objectives, drift from their original goals, become power-seeking, resist shutdown, and engage in deception. We suggest that AIs should not be deployed in high-risk settings, such as by autonomously pursuing open-ended goals or overseeing critical infrastructure, unless proven safe. We also recommend advancing AI safety research in areas such as adversarial robustness, model honesty, transparency, and removing undesired capabilities.