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Epistemic Side Effects & Avoiding Them (Sometimes)
Toryn Klassen · Parand Alizadeh Alamdari · Sheila McIlraith
AI safety research has investigated the problem of negative side effects -- undesirable changes made by AI systems in pursuit of an underspecified objective. However, the focus has been on physical side effects, such as a robot breaking a vase while moving. In this paper we introduce the notion of epistemic side effects, unintended changes made to the knowledge or beliefs of agents, and describe a way to avoid negative epistemic side effects in reinforcement learning, in some cases.
Author Information
Toryn Klassen (University of Toronto)
Parand Alizadeh Alamdari (University of Toronto)
Sheila McIlraith (University of Toronto and Vector Institute)
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