Thank you for participating in our survey. As part of our ongoing research, we are exploring advanced methods for evaluating and interpreting machine learning models used in clinical settings. One of the significant challenges in this area is ensuring that these models are trustworthy and reliable. To address this, we have developed a novel error profiling visualization framework designed to provide deeper insights into model performance and decision-making processes. 

 

This survey aims to gather your valuable feedback on the effectiveness of these visualizations. You will be presented with a series of figures from three use cases that illustrate the error profiles of different machine learning models across various clinical datasets. Each figure is accompanied by a brief explanation to help you understand the context and the information being conveyed. You will be asked: 1) Objective quiz-type questions that assess your ability to correctly extract information from the figures; 2) Subjective questions that assess your opinion on the presentation and utility of the figures. Note that the visualizations are a new type of figure for the purpose of summarizing complex information, and will require careful consideration.

 

We are particularly interested in your perspectives on the following aspects: 

- Clarity and understandability of the visualizations 

- Impact of the visualizations on your trust in the models 

- Usefulness of the visualizations in identifying problematic subpopulations and understanding model performance 

 

Your insights will be crucial in refining these visualizations and making them more useful for clinical practitioners and researchers. The survey should take approximately 20-30 minutes to complete. Your responses will be kept confidential and used solely for research purposes. We appreciate your time and input in helping us improve the reliability and transparency of machine learning models in healthcare.