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AI Risk

Safe exploration problem with widely deployed AI assistants

"Moreover, we can expect assistants – that are widely deployed and deeply embedded across a range of social contexts – to encounter the safe exploration problem referenced above Amodei et al. (2016). For example, new users may have different requirements that need to be explored, or widespread AI assistants may change the way we live, thus leading to a change in our use cases for them (see Chapters 14 and 15). To...

AI Risk7. AI System Safety, Failures, & Limitations7.3 > Lack of capability or robustness2 - Post-deployment

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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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"Moreover, we can expect assistants – that are widely deployed and deeply embedded across a range of social contexts – to encounter the safe exploration problem referenced above Amodei et al. (2016). For example, new users may have different requirements that need to be explored, or widespread AI assistants may change the way we live, thus leading to a change in our use cases for them (see Chapters 14 and 15). To learn what to do in these new situations, the assistants may need to take exploratory actions. This could be unsafe, for example a medical AI assistant when encountering a new disease might suggest an exploratory clinical trial that results in long-lasting ill health for participants."

Domain7. AI System Safety, Failures, & Limitations
Subdomain7.3 > Lack of capability or robustness
Entity2 - AI
Intent2 - Unintentional
Timing2 - Post-deployment
CategoryCapability failures
SubcategorySafe exploration problem with widely deployed AI assistants

Suggested mitigations

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

Source

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