With the rise of big data and machine learning, there have been unprecedented applications in the real world, from climate change adaptation and mitigation to human trafficking detection. However, a downside to these advances is that many of these models are not interpretable, in that we don't know what is going on inside and how the models are making their decisions and predictions. Dubbed "black boxes," these models are dangerous because their decisions can contain unforeseen biases. In this talk, we discuss the variety of issues that black box machine learning models present and ways in which we can open them up. These include conducting in-depth ablation studies. It surely is time-consuming and many times unappealing to work on breaking open black boxes, but this is necessary for security and equity in machine learning research and deployment.