Statistical topic modeling is a common tool for summarizing the themes in a document corpus. Due to the complexity of topic modeling algorithms, however, their results are not accessible to non-expert users. Recent work in interactive topic modeling looks to incorporate the user into the inference loop, for example, by allowing them to view a model then update it by specifying important words and words that should be ignored. However, the majority of interactive topic modeling work has been performed without fully understanding the needs of the end user and does not adequately consider challenges that arise in interactive machine learning. With one workshop paper and two full research papers, we explore a subset of interactive machine learning design challenges with specific considerations for interactive topic modeling. For each challenge, we propose solutions based on prior work and our own preliminary findings and identify open questions to guide future work. I participanted in a NSF research project about interactive modeling with Alison Smith, Leah Findlator, Jordan Boyd-Graber, Kevin Seppi, and Niklas Elmqvist.