Investigating How Users Design Everyday Intelligent Systems in Use

Abstract

Intelligent systems learn and evolve depending on what kinds of input are given and how people actually use them after deployment. While such a characteristic may be a troubling property for AI user experience designers, it also imbues an intelligent system with an open-ended quality, empowering end-users to ‘design’ their own system in use to achieve more desired experiences. In light of this, we conducted in-depth interviews with 16 users of various AI-based everyday recommender systems, investigating how people design their AI user experiences in actual use contexts. Exploring people’s current experiences of adopting and adapting those systems to achieve their own desired experiences, we discovered three styles of end-user design of their experiences: teaching, resisting, and repurposing. We end with a discussion of the implications of our findings, recognizing end-users’ motivation to challenge a prescribed experience of an intelligent system.