Nudging is a behavioral strategy aimed at influencing people's thoughts and actions. Nudging techniques can be found in many situations in our daily lives, and these nudging techniques can targeted at human fast and unconscious thinking, e.g., by using images to generate fear or the more careful and effortful slow thinking, e.g., by releasing information that makes us reflect on our choices. In this paper, we propose and discuss a value-based AI-human collaborative framework where AI systems nudge humans by proposing decision recommendations. Three different nudging modalities, based on when recommendations are presented to the human, are intended to stimulate human fast thinking, slow thinking, or meta-cognition. Values that are relevant to a specific decision scenario are used to decide when and how to use each of these nudging modalities. Examples of values are decision quality, speed, human upskilling and learning, human agency, and privacy. Several values can be present at the same time, and their priorities can vary over time. The framework treats values as parameters to be instantiated in a specific decision environment.

翻译：助推是一种旨在影响人们思想和行为的策略。日常生活中存在多种助推情境，这些技术既可以针对人类快速无意识的思考（例如通过图像引发恐惧），也可以针对更谨慎费力的慢速思考（例如通过发布信息促使反思选择）。本文提出并探讨了一种基于价值的人机协作框架，其中AI系统通过提供决策建议来对人类进行助推。根据建议呈现给人类的不同时机，我们定义了三种不同的助推模式，分别旨在激发人类的快思考、慢思考或元认知。与特定决策情境相关的价值被用于决定何时以及如何运用每种助推模式。典型价值包括决策质量、速度、人类技能提升与学习、人类自主性和隐私。多种价值可同时存在，且其优先级会随时间动态变化。该框架将价值视为可在特定决策环境中实例化的参数。