Applying Usability Heuristics in the Context of Data Labeling Systems
Resumo
In recent times, Machine Learning (ML) techniques have been explored in everyday systems, such as banking systems, virtual assistants, and recommendation systems. To use ML techniques, it is necessary to perform some stages until the construction of the ML model. Within the ML model construction pipeline, there may be a need to develop systems that support the stages of this construction, such as the data labeling stage. For this stage, the User Interface (UI) of the systems must be designed so that the user does not feel bothered or bored during labeling, as this can decrease the quality of data labeling. If the data is wrong labeled, the ML model will learn incorrectly, leaving this model unusable, as it does not match the reality. Previous research focused on supporting the development of data labeling systems, but there is still a gap in guiding developers on how to create such UI for these systems. This paper aims to present the construction of guidelines for the development of data labeling systems UI that meet Nielsen’s heuristics for good user interaction. To evaluate these guidelines, we conducted a preliminary study where ML developers used them to build a UI prototype for a labeling system and provided their impressions. Based on the developer’s impressions, we analyzed each guideline, associating it to Nielsen’s heuristics. Our results showed that the use of guidelines helps the developers during the construction of a UI for data labeling systems and that these guidelines meet some of Nielsen’s heuristics.
