# Addressing UX Practitioners' Challenges in Designing ML Applications: an Interactive Machine Learning Approach

arXiv: https://arxiv.org/abs/2302.11843 · HTML: https://mlsanity.com/paper/2302.11843/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Feb 2023.

## Built on by (most cited first, 3 of 3)

- [Prototyping Multimodal GenAI Real-Time Agents with Counterfactual Replays and Hybrid Wizard-of-Oz](https://mlsanity.com/paper/2510.06872/) (2025)
- [Interactivity x Explainability: Toward Understanding How Interactivity Can Improve Computer Vision Explanations](https://mlsanity.com/paper/2504.10745/) (2025)
- [Unlocking Fair Use in the Generative AI Supply Chain: A Systematized Literature Review](https://mlsanity.com/paper/2408.00613/) (2024)

## Abstract (arXiv)

UX practitioners face novel challenges when designing user interfaces for machine learning (ML)-enabled applications. Interactive ML paradigms, like AutoML and interactive machine teaching, lower the barrier for non-expert end users to create, understand, and use ML models, but their application to UX practice is largely unstudied. We conducted a task-based design study with 27 UX practitioners where we asked them to propose a proof-of-concept design for a new ML-enabled application. During the task, our participants were given opportunities to create, test, and modify ML models as part of their workflows. Through a qualitative analysis of our post-task interview, we found that direct, interactive experimentation with ML allowed UX practitioners to tie ML capabilities and underlying data to user goals, compose affordances to enhance end-user interactions with ML, and identify ML-related ethical risks and challenges. We discuss our findings in the context of previously established human-AI guidelines. We also identify some limitations of interactive ML in UX processes and propose research-informed machine teaching as a supplement to future design tools alongside interactive ML.