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Mash: software tools for developing interactive and transparent machine learning systems

We present Mash, a library that provides software tools for developing interactive and transparent machine learning systems. Mash provides tools for (1) back end: specifying machine learning models, and (2) front end: generating User Interfaces (UIs) that allow nontechnical end-users to interpret and interact with the models. In the back-end, Mash includes probabilistic modeling and computation graph utilities. On the front-end, UIs generated by Mash enable end users to examine the model’s predictions, manipulate model’s parameters, and observe changes in model’s predictions. Together, Mash can help developers rapidly build new models and obtain feedback from end users. We present two case studies in which Mash UIs enable end-users to: (1) assess model fairness in predicting student performance, and (2) explore movie recommendations. To support future work, we will open source Mash.