Building interpretable machine learning models for sequential data
Creators: name: Hong, Dat
Publisher: name: University of Iowa
Year: 2023
Subjects: Artificial Intelligence
Explainable Artificial Intelligence
Interpretable Machine Learning
Reinforcement Learning
Sequential Data
public abstract
Machine Learning

description: Machine learning is progressing at an astounding rate.The past decade has seen an explosion in the amount of machine learning research, including deep learning techniques. These modern techniques have proven highly effective in a variety of domains, such as healthcare, finance, genomics, image processing, text analytics, etc. Despite the successes, machine learning has its own limitations and drawbacks. The most significant one is the lack of transparency behind their behaviors, which leaves users with little understanding of how particular decisions are made by these models. There has recently been an increasing amount of work in explanatory artificial intelligence (XAI). This research area tackles the important problem that complex machines and algorithms often cannot provide insights into their behavior and thought processes. XAI allows parts of the internal system to be more transparent to users, providing explanations of its decisions in some level of detail. These explanations are important to identify potential biases/problems of the predictive models, and to ensure the fairness of the algorithms.
description: Although there is a large quantity of research and empirical success of XAI research, most of the existing work has focused on tabular data and image processing techniques. Interpreting machine learning models for sequential data has received much less attention comparatively. Given recent machine learning advancements in highly sequential domains such as natural language processing, e-commerce, healthcare, etc., the need for sequential explanations is, more than ever, critical.
description: In this thesis, we propose several methods for explaining and understanding sequential machine learning models. This thesis consists of three parts. In the first part, we discuss AdaAX - an effective method to learn deterministic finite automaton (DFA) as a post-hoc explanation of recurrent neural networks (RNN). A DFA, which defines a set of states and transition rules between them, can be used to explain the internal behavior of any sequence model. Comprehensive evaluations show that AdaAX can generate DFA that is easy to interpret and outperforms other baseline models on both synthetic and real-world datasets. In the second part, we introduce ProtoryNet, a model with inherent interpretability based on the concept of prototype trajectory. ProtoryNet will transform any text input into a prototypical text sequence, which is a sequence of trained prototypical sentences, before generating the prediction. Extensive experimental results on a variety of text datasets demonstrate the superior performance of ProtoryNet. Finally, in the third part, we introduce Personalized Path Recourse (PPR), a novel method that generates alternative action paths for a given path from an agent, with the goal of achieving better outcomes while satisfying both similarity and personalization requirements. The proposed method is applicable to both reinforcement learning and supervised learning settings for correcting or improving sequences of actions of sequences of data to achieve a pre-determined goal. The method is evaluated in various settings and demonstrates promising results.