Arshia S.'s Senior Project Blog!

As the field of Artificial Intelligence grows, researchers are looking for ways to enhance predictability in various situations. Interpretable machine learning models and black-box models are close competitors when it comes to deciding which approach to adopt. Interpretable models, such as linear models, have the benefit of being easy to understand and explain, while black-box models, such as deep neural networks, often achieve better predictive performance on complicated tasks. These two types of models have been distinctive choices for practitioners. In this project, I am working at the Computer History Museum to combine a linear model with a black box model to make joint decisions. This approach connects black-boxes and transparent-boxes into a continuum of hybrid models where the linear model determines part of the data while the black-box model determines the rest, creating a partition of the data space. As the partition changes, I’m using this hybrid model to smoothly transit from one extreme to the other, striking a balance between predictive accuracy and the explainability of the decision/prediction-making process.