Locally interpretable model agnostic explanations (LIME) method is one of the most popular methods used to explain black-box models at a per example level.

Machine learning (ML) models are known to leak information about their training data.

We present a case study on malware detection--a binary classification problem on byte sequences where classifier evasion is a well-established threat model.

Machine learning models often fail on out-of-distribution data.

Receiver Operating Characteristics (ROC) is a popular tool to describe the trade-off between the True Positive Rate (TPR) and False Positive Rate (FPR) of a scoring function.

Through considerable effort and intuition, several recent works have reverse-engineered nontrivial behaviors of transformer models. This paper systematizes the mechanistic interpretability process they followed. First, researchers choose a metric and dataset that elicit the desired model behavior.

In human-AI collaboration systems for critical applications, in order to ensure minimal error, users should set an operating point based on model confidence to determine when the decision should be delegated to human experts.