Engineering Trustworthy Automation: Design Principles and Evaluation for AutoML Tools for Novices

2025-11-27

AI Summary

Existing AutoML systems for novice users prioritize algorithmic sophistication over usability, trust, and interpretability, hindering effective adoption. Method: This paper proposes an end-to-end abstracted pipeline tailored for novices, spanning data ingestion, guided configuration, training, evaluation, and inference. Grounded in four human-centered design principles—ensuring first-model success to boost self-efficacy, providing explanations to foster accurate mental models, applying contextual abstraction to maintain the zone of proximal development, and enhancing predictability and safety to strengthen perceived control—we employed user-centered design to develop a prototype and conducted a controlled 24-participant user study. Contribution/Results: UEQ (User Experience Questionnaire) results confirmed that all participants successfully built models, with significantly positive ratings for usability, trust, and comprehensibility. Domain-expert evaluations further corroborated the effectiveness of the design principles in supporting novice skill development.

Technology Category

• Humans and AI: User Experience and Usability
• Machine Learning: Auto ML and Hyperparameter Tuning
• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness

Application Category

• User Modeling, Personalization and Recommendation: Metrics for user behavior and evaluating success
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI

Abstract

AutoML systems targeting novices often prioritize algorithmic automation over usability, leaving gaps in users' understanding, trust, and end-to-end workflow support. To address these issues, we propose an abstract pipeline that covers data intake, guided configuration, training, evaluation, and inference. To examine the abstract pipeline, we report a user study where we assess trust, understandability, and UX of a prototype implementation. In a 24-participant study, all participants successfully built their own models, UEQ ratings were positive, yet experienced users reported higher trust and understanding than novices. Based on this study, we propose four design principles to improve the design of AutoML systems targeting novices: (P1) support first-model success to enhance user self-efficacy, (P2) provide explanations to help users form correct mental models and develop appropriate levels of reliance, (P3) provide abstractions and context-aware assistance to keep users in their zone of proximal development, and (P4) ensure predictability and safeguards to strengthen users' sense of control.

Problem

• Addresses usability gaps in AutoML tools for novices
• Proposes design principles to enhance user trust and understanding
• Evaluates prototype for novice-friendly automated machine learning

Innovation

• Abstract pipeline covering data to inference
• User study assessing trust and usability
• Design principles for novice-focused AutoML systems