This body of work explores an emerging aspect of human-robot interaction, transparency. Socially guided machine learning has proven that highly immersive robotic behaviors have yielded better results than lesser interactive behaviors for performance and shorter training time. While other work explores this transparency in learning by demonstration using non-verbal cues to point out the importance or preference users may have towards behaviors, my work follows this argument and attempts to extend it by offering cues to the internal task representation. What I show is that task-transparency, or the ability to connect and discuss the task in a fluent way implores the user to shape and correct the learned goal in ways that may be impossible by other present day learning by demonstration methods. Additionally, some participants are shown to prefer task-transparent robots which appear to have the ability of "introspection" in which it can modify the learned goal by other methods than just demonstration.