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AI Risk

Feedback Loops

"Feedback Loops. One of the best-known historical examples to illustrate destabilising dynamics in the context of autonomous agents is the 2010 flash crash, in which algorithmic trading agents entered into an unexpected feedback loop (Commission & Commission, 2010, see also Case Study 10).37 More generally, a feedback loop occurs when the output of a system is used as part of its input, creating a cycle that can e...

AI Risk7. AI System Safety, Failures, & Limitations7.6 > Multi-agent risks2 - Post-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

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"Feedback Loops. One of the best-known historical examples to illustrate destabilising dynamics in the context of autonomous agents is the 2010 flash crash, in which algorithmic trading agents entered into an unexpected feedback loop (Commission & Commission, 2010, see also Case Study 10).37 More generally, a feedback loop occurs when the output of a system is used as part of its input, creating a cycle that can either amplify or dampen the system’s behaviour. In multi-agent settings, feedback loops often arise from the interactions between agents, as each agent’s actions affect the environment and the behaviour of other agents, which in turn affect their own subsequent actions. Feedback loops can lead not only to financial crashes but to military conflicts (Richardson, 1960, see also ??) and ecological disasters (Holling, 1973)."

Domain7. AI System Safety, Failures, & Limitations
Subdomain7.6 > Multi-agent risks
Entity2 - AI
Intent2 - Unintentional
Timing2 - Post-deployment
CategoryDestabilising Dynamics
SubcategoryFeedback Loops

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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