Exploring a Behavioral Model of "Positive Friction" in Human-AI Interaction

TL;DR

This paper introduces a 'positive friction' model in human-AI interaction, highlighting how deliberate delays and challenges can improve reflection, diversity, and discovery in AI user and developer experiences.

Contribution

It proposes a new model to identify and leverage beneficial friction in AI design and development, emphasizing a hybrid AI+human perspective.

Findings

Friction can enhance reflection and prevent biased behaviors.

Positive friction can foster diversity and unexpected discoveries.

The model guides when and how to implement beneficial friction in AI contexts.

Abstract

Designing seamless, frictionless user experiences has long been a dominant trend in both applied behavioral science and artificial intelligence (AI), in which the goal of making desirable actions easy and efficient informs efforts to minimize friction in user experiences. However, in some settings, friction can be genuinely beneficial, such as the insertion of deliberate delays to increase reflection, preventing individuals from resorting to automatic or biased behaviors, and enhancing opportunities for unexpected discoveries. More recently, the popularization and availability of AI on a widespread scale has only increased the need to examine how friction can help or hinder users of AI; it also suggests a need to consider how positive friction can benefit AI practitioners, both during development processes (e.g., working with diverse teams) and to inform how AI is designed into offerings. This paper first proposes a "positive friction" model that can help characterize how friction is currently beneficial in user and developer experiences with AI, diagnose the potential need for friction where it may not yet exist in these contexts, and inform how positive friction can be used to generate solutions, especially as advances in AI continue to be progress and new opportunities emerge. It then explores this model in the context of AI users and developers by proposing the value of taking a hybrid "AI+human" lens, and concludes by suggesting questions for further exploration.