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.
Latest posts by Ryan Watkins (see all)
- Who Goes First? Influences of Human-AI Workflow on Decision Making in Clinical Imaging - May 20, 2022
- A Transparency Index Framework for AI in Education - May 20, 2022
- Role of Human-AI Interaction in Selective Prediction - May 19, 2022