As artificial intelligence becomes more powerful and a ubiquitous presence in
daily life, it is imperative to understand and manage the impact of AI systems
on our lives and decisions. Modern ML systems often change user behavior (e.g.
personalized recommender systems learn user preferences to deliver
recommendations that change online behavior). An externality of behavior change
is preference change. This article argues for the establishment of a
multidisciplinary endeavor focused on understanding how AI systems change
preference: Preference Science. We operationalize preference to incorporate
concepts from various disciplines, outlining the importance of meta-preferences
and preference-change preferences, and proposing a preliminary framework for
how preferences change. We draw a distinction between preference change,
permissible preference change, and outright preference manipulation. A
diversity of disciplines contribute unique insights to this framework.

本文提出成立跨学科组织，聚焦于理解 AI 系统对个体决策偏好的影响，运用各学科概念对偏好进行操作化，提出偏好变化的框架，并明确了可接受的和不可接受的变化。