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AI Risk

Malign belief distributions

"Christiano (2016) argues that the universal distribution M (Hutter, 2005; Solomonoff, 1964a,b, 1978) is malign. The argument is somewhat intricate, and is based on the idea that a hypothesis about the world often includes simulations of other agents, and that these agents may have an incentive to influence anyone making decisions based on the distribution. While it is unclear to what extent this type of problem w...

AI Risk7. AI System Safety, Failures, & Limitations7.3 > Lack of capability or robustness1 - Pre-deployment

Record summary

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Techniques0Attack methods connected to this risk.
Mitigations0Defenses that may help with related attacks.
Domain7. AI System Safety, Failures, & LimitationsThe broad risk area this belongs to.

Risk profile

How this risk is described and categorized.

"Christiano (2016) argues that the universal distribution M (Hutter, 2005; Solomonoff, 1964a,b, 1978) is malign. The argument is somewhat intricate, and is based on the idea that a hypothesis about the world often includes simulations of other agents, and that these agents may have an incentive to influence anyone making decisions based on the distribution. While it is unclear to what extent this type of problem would affect any practical agent, it bears some semblance to aggressive memes, which do cause problems for human reasoning (Dennett, 1990)."

Domain7. AI System Safety, Failures, & Limitations
Subdomain7.3 > Lack of capability or robustness
Entity3 - Other
Intent3 - Other
Timing1 - Pre-deployment
CategoryMalign belief distributions
Subcategoryn/a

Suggested mitigations

Defenses that may help with related attacks.

No propagated mitigations. No defense is available through the connected attack methods.

Source

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