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

arXiv: https://arxiv.org/abs/2402.09683 · HTML: https://mlsanity.com/paper/2402.09683/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 70 references that are in the map.
- First posted on arXiv 15 Feb 2024.

## Builds on (most cited first, 5 of 5)

- [AI Fairness 360: An Extensible Toolkit for Detecting, Understanding, and Mitigating Unwanted Algorithmic Bias](https://mlsanity.com/paper/1810.01943/) (2018)
- [Manipulating and Measuring Model Interpretability](https://mlsanity.com/paper/1802.07810/) (2018)
- [Gender-Inclusive Grammatical Error Correction through Augmentation](https://mlsanity.com/paper/2306.07415/) (2023)
- [Why They're Worried: Examining Experts' Motivations for Signing the 'Pause Letter'](https://mlsanity.com/paper/2306.00891/) (2023)
- [Persuasive Technology For Human Development: Review and Case Study](https://mlsanity.com/paper/1708.08758/) (2017)

## Built on by (most cited first, 9 of 9)

- [Dynamic Epistemic Friction in Dialogue](https://mlsanity.com/paper/2506.10934/) (2025)
- [Trapped by Expectations: Functional Fixedness in LLM-Enabled Chat Search](https://mlsanity.com/paper/2504.02074/) (2025)
- [Architecting Trust in Artificial Epistemic Agents](https://mlsanity.com/paper/2603.02960/) (2026)
- [Autonomy by Design: Preserving Human Autonomy in AI Decision-Support](https://mlsanity.com/paper/2506.23952/) (2025)
- [Alignment-Process-Outcome: Rethinking How AIs and Humans Collaborate](https://mlsanity.com/paper/2603.08017/) (2026)
- [Beyond Compliance: How AI Could Help Creative Writers by Refusing Them](https://mlsanity.com/paper/2605.16272/) (2026)
- [It Matters How You Say It: Exploring Rhetorical Patterns for AI-Assisted Information Evaluation](https://mlsanity.com/paper/2607.17627/) (2026)
- [AI as Friction for Reflection Support in Ideation](https://mlsanity.com/paper/2607.26827/) (2026)
- [DocuTeam: Mixed-Initiative Multi-Agent Discussions around Evolving Documents](https://mlsanity.com/paper/2609.29309/) (2026)

## Abstract (arXiv)

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.