Making AI Find Wrongdoing Unprofitable: Research Exploring Safety Measures Through Inverse Game Theory
Published
A new approach to improving AI (artificial intelligence) safety, based on the concept of "inverse game theory," is attracting attention. This content was reported by MIT Technology Review.
General game theory is a theory that considers how people should act in order to gain benefits. Inverse game theory, on the other hand, is a way of thinking that designs the rules and mechanisms so that desirable outcomes emerge. For example, suppose that the person who cuts a cake and the person who chooses a piece are separated. Then, the person cutting the cake will try to divide it evenly so as not to lose out, no matter which piece is chosen. In this way, people's behavior can be naturally guided in a desirable direction.
Research applying this concept to AI safety measures has emerged. The goal is to create mechanisms in which choosing harmful behavior brings no benefit to AI. In other words, it is an attempt to establish a state in which "crime does not pay" for AI as well. The article's summary does not reveal the specific details of the method.
AI is increasingly being used in various situations in our daily lives and work. As a result, the importance of mechanisms that keep AI from taking problematic actions is also growing. Attention is focused on how this research will progress in the future and whether it will be useful for actual safety measures.
Sources: MITテクノロジーレビュー
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