Domain experts increasingly use automated data science tools to incorporate machine learning (ML) models in their work but struggle to "debug" these models when they are incorrect. For these experts, semantic interactions can provide an accessible avenue to guide and refine ML models without having to programmatically dive into its technical details. In this research, we conduct an elicitation study using data and visual design probes to examine if and how experts with a spectrum of ML expertise use semantic interactions to update a simple classification model. We use our design probes to facilitate an interactive dialogue with 20 participants and codify their interactions as a set of target-interaction pairs. Interestingly, our findings revealed that many targets of semantic interactions do not directly map to ML model parameters, but instead aim to augment the data a model uses for training. We also identify reasons that participants would hesitate to interact with ML models, including burdens of cognitive load and concerns of injecting bias. Unexpectedly participants also saw the value of using semantic interactions to work collaboratively with members of their team. Participants with less ML expertise found this to be a useful mechanism for communicating their concerns to ML experts. This was an especially important observation, as our study also shows the different needs that correspond to diverse ML expertise. Collectively, we demonstrate that design probes are effective tools for proactively gathering the affordances that should be offered in an interactive machine learning system.

通过使用数据和可视设计探针进行调查研究，我们发现了专家在使用语义交互来更新简单分类模型时的行为模式，并确定了他们的需求和关注点，研究结果显示设计探针是一种主动收集可交互机器学习系统所需功能的有效工具。

通过数据和视觉设计探测用户引发模型导向交互