Power to the People: The Role of Humans in Interactive Machine Learning

However, potential users of such applications, who are often domain experts for the application, have limited involvement in the process of developing them. The intricacies of applying machine-learning techniques to everyday problems have largely restricted their use to skilled practitioners. In the traditional applied machine-learning workflow, these practitioners collect data, select features to represent the data, preprocess and transform the data, choose a representation and learning algorithm to construct the model, tune parameters of the algorithm, and finally assess the quality of the resulting model. This assessment often leads to further iterations on many of the previous steps. Typically, any end-user involvement in this process is mediated by the practitioners and is limited to providing data, answering domain-related questions, or giving feedback about the learned model.