No abstract

No abstract

Software developers are rooted in the written form of their code, yet they often draw diagrams representing their code. Unfortunately, we still know little about how and why they create these diagrams, and so there is little research to inform the design of visual tools to support developers' work. This paper presents findings from semi-structured interviews that have been validated with a structured survey. Results show that most of the diagrams had a transient nature because of the high cost of changing whiteboard sketches to electronic renderings. Diagrams that documented design decisions were often externalized in these temporary drawings and then subsequently lost. Current visualization tools and the software development practices that we observed do not solve these issues, but these results suggest several directions for future research.

Data scientists require rich mental models of how AI systems behave to effectively train, debug, and work with them. Despite the prevalence of AI analysis tools, there is no general theory describing how people make sense of what their models have learned. We frame this process as a form of sensemaking and derive a framework describing how data scientists develop mental models of AI behavior. To evaluate the framework, we show how existing AI analysis tools fit into this sensemaking process and use it to design AIFinnity , a system for analyzing image-and-text models. Lastly, we explored how data scientists use a tool developed with the framework through a think-aloud study with 10 data scientists tasked with using AIFinnity to pick an image captioning model. We found that AIFinnity ’s sensemaking workflow reflected participants’ mental processes and enabled them to discover and validate diverse AI behaviors.

No abstract

No abstract