Record summary
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Risk profile
How this risk is described and categorized.
"Reward tampering can be considered a special case of reward hacking (Everitt et al., 2021; Skalse et al., 2022),referring to AI systems corrupting the reward signals generation process (Ring and Orseau, 2011). Everitt et al.(2021) delves into the subproblems encountered by RL agents: (1) tampering of reward function, where the agentinappropriately interferes with the reward function itself, and (2) tampering of reward function input, which entailscorruption within the process responsible for translating environmental states into inputs for the reward function.When the reward function is formulated through feedback from human supervisors, models can directly influencethe provision of feedback (e.g., AI systems intentionally generate challenging responses for humans to comprehendand judge, leading to feedback collapse) (Leike et al., 2018)."
Suggested mitigations
Defenses that may help with related attacks.
Source
Research source for this risk, when available.
Included resource
AI Alignment: A Comprehensive Survey
Original source
MIT AI Risk Repository
Open the public repository used for AI risk records and taxonomy fields.
