Interpretability and Fairness in Machine Learning: A Formal Methods Approach

The last decades have witnessed significant progress in machine learning with applications in different safety-critical domains, such as medical, law, education, and transportation. In high-stake domains, machine learning predictions have farreaching consequences on the end-users. With the aim of applying machine learning for societal goods, there have been increasing efforts to regulate machine learning by imposing interpretability, fairness, robustness, privacy, etc. in predictions. Towards responsible and trustworthy machine learning, we propose two research themes in our dissertation research: interpretability and fairness of machine learning classifiers. In particular, we design algorithms to learn interpretable rule-based classifiers, formally verify fairness, and explain the sources of unfairness. Prior approaches to these problems are often limited by scalability, accuracy, or both. To overcome these limitations, we closely integrate automated reasoning and formal methods with fairness and interpretability to develop scalable and accurate solutions.