Exploring Incentive Compatibility to bridge technical and societal components for AI alignment in sociotechnical systems.
우리는 목표를 달성하기 위해 명확한 조건부 학습이 필요하다.
Exploring the use of Incentive Compatibility to bridge the gap between technical and societal components in AI systems for alignment with human values.
AI systems can surpass human capabilities by leveraging easy-to-hard generalization through process-supervised reward models, enhancing performance on complex tasks.
The author argues that achieving incentive compatibility can address both technical and societal components in the alignment phase, enabling AI systems to maintain consensus with human societies in various contexts.