Interpretability by Design: New Interpretable Machine Learning Models and Methods
As machine learning models are playing increasingly important roles in many real-life scenarios, interpretability has become a key issue for whether we can trust the predictions made by these models, especially when we are making some high-stakes decisions. Lack of transparency has long been a concern for predictive models in criminal justice and in healthcare. There have been growing calls for building interpretable, human understandable machine learning models, and "opening the black box" has become a debated issue in the media. My dissertation research addresses precisely the demand for interpretability and transparency in machine learning models. The key problem of this dissertation is: "Can we build machine learning models that are both accurate and interpretable?"
To address this problem, I will discuss the notion of interpretability as it relates to machine learning, and present several new interpretable machine learning models and methods I developed during my dissertation research. In Chapter 1, I will discuss two types of model interpretability -- predicate-based and case-based interpretability. In Chapters 2 and 3, I will present novel predicate-based interpretable models and methods, and their applications to understanding low-dimensional structured data. In particular, Chapter 2 presents falling rule lists, which extend regular decision lists by requiring the probabilities of the desired outcome to be monotonically decreasing down the list; Chapter 3 presents two-layer additive models, which are hybrids of predicate-based additive scoring models and small neural networks. In Chapter 4, I will present case-based interpretable deep models, and their applications to computer vision. Given the empirical evidence, I conclude in Chapter 5 that, by designing novel model architectures or regularization techniques, we can build machine learning models that are both accurate and interpretable.
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