Establishing Heuristics for Improving the Usability of GUI Machine Learning Tools for Novice Users

TL;DR

This paper develops and empirically validates a set of usability heuristics specifically for GUI-based machine learning tools aimed at novice users, enhancing design and evaluation practices.

Contribution

It extends Nielsen's heuristics with new guidelines tailored for GUI ML tools and validates them through user testing with novices.

Findings

Proposed heuristics improve usability assessment of GUI ML tools.

User testing shows increased comprehension and interaction efficiency.

Heuristics serve as a practical resource for designers and evaluators.

Abstract

Machine learning (ML) tools with graphical user interfaces (GUI) are facing demand from novice users who do not have the background of their underlying concepts. These tools are frequently complex and pose unique challenges in terms of interaction and comprehension by novice users. There is yet to be an established set of usability heuristics to guide and assess GUI ML tool design. To address this gap, in this paper, we extend Nielsen's heuristics for evaluating GUI ML Tools through a set of empirical evaluations. To validate the proposed heuristics, user testing was conducted by novice users on a prototype that reflects those heuristics. Based on the results of the evaluations, our new heuristics set improves upon existing heuristics in the context of ML tools. It can serve as a resource for practitioners designing and evaluating these tools.