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AI Risk

Violation of social norms

"Second, because LLMs are trained on internet text data, there is also a risk that model weights encode functions which, if deployed in particular contexts, would violate social norms of that context. Following the principles of contextual integrity, it may be that models deviate from information sharing norms as a result of their training. Overcoming this challenge requires two types of infrastructure: one for ke...

AI Risk1. Discrimination & Toxicity1.2 > Exposure to toxic content2 - Post-deployment

Record summary

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Techniques0Attack methods connected to this risk.
Mitigations0Defenses that may help with related attacks.
Domain1. Discrimination & ToxicityThe broad risk area this belongs to.

Risk profile

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"Second, because LLMs are trained on internet text data, there is also a risk that model weights encode functions which, if deployed in particular contexts, would violate social norms of that context. Following the principles of contextual integrity, it may be that models deviate from information sharing norms as a result of their training. Overcoming this challenge requires two types of infrastructure: one for keeping track of social norms in context, and another for ensuring that models adhere to them. Keeping track of what social norms are presently at play is an active research area. Surfacing value misalignments between a model’s behaviour and social norms is a daunting task, against which there is also active research (see Chapter 5)."

Domain1. Discrimination & Toxicity
Subdomain1.2 > Exposure to toxic content
Entity2 - AI
Intent2 - Unintentional
Timing2 - Post-deployment
CategoryPrivacy
SubcategoryViolation of social norms

Suggested mitigations

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

Source

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