Interpretability in machine learning (ML) is crucial for high stakes decisions and
troubleshooting. In this work, we provide fundamental principles for interpretable ML, and …

Classification models are often used to make decisions that affect humans: whether to
approve a loan application, extend a job offer, or provide insurance. In such applications …

One of the most important obstacles to deploying predictive models is the fact that humans
do not understand and trust them. Knowing which variables are important in a model's …

The group testing problem concerns discovering a small number of defective items within a
large population by performing tests on pools of items. A test is positive if the pool contains …

We present a machine learning algorithm for building classifiers that are comprised of a
small number of short rules. These are restricted disjunctive normal form models. An …

Abstract The rise of “Big Data” analytics over unstructured text has led to renewed interest in
information extraction (IE). We surveyed the landscape of IE technologies and identified a …

Abstract Machine learning algorithms increasingly influence our decisions and interact with
us in all parts of our daily lives. Therefore, just as we consider the safety of power plants …

This paper considers the learning of Boolean rules in either disjunctive normal form (DNF,
OR-of-ANDs, equivalent to decision rule sets) or conjunctive normal form (CNF, AND-of …

Modern machine learning methods typically produce “black box” models that are opaque to
interpretation. Yet, their demand has been increasing in the Human-in-the-Loop processes …

Risk scores are simple classification models that let users make quick risk predictions by
adding and subtracting a few small numbers. These models are widely used in medicine …