Exploring a Behavioral Model of "Positive Friction" in Human-AI Interaction
Zeya Chen, Ruth Schmidt
TL;DR
The paper tackles when friction can be beneficial in human-AI interaction, challenging the default push toward frictionless design. It introduces a Positive Friction Model that classifies friction into four quadrants across inhibiting/stimulating and goal-based/expansive axes, and situates this framework within AI user and developer contexts. The authors illustrate the model with real-world examples (FLI Open Letter, Tesla Autopilot, Grammarly) and propose three lens applications—Characterizing, Diagnostic, Generative—to guide assessment, diagnostics, and design of friction in AI systems. They discuss implications for AI governance and collaborative AI–human hybrids, emphasizing context-specific deployment and multi-stakeholder considerations to achieve beneficial, equitable outcomes in evolving AI landscapes.
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.