Artificial intelligence (AI) with deep learning models has been widely applied in numerous
domains, including medical imaging and healthcare tasks. In the medical field, any judgment …

The trustworthiness of machine learning has emerged as a critical topic in the field,
encompassing various applications and research areas such as robustness, security …

Abstract Concept erasure aims to remove specified features from a representation. It can
improve fairness (eg preventing a classifier from using gender or race) and interpretability …

Obtaining human-interpretable explanations of large, general-purpose language models is
an urgent goal for AI safety. However, it is just as important that our interpretability methods …

The impressive performance of recent language models across a wide range of tasks
suggests that they possess a degree of abstract reasoning skills. Are these skills general …

The last decade of machine learning has seen drastic increases in scale and capabilities.
Deep neural networks (DNNs) are increasingly being deployed in the real world. However …

This work identifies 18 foundational challenges in assuring the alignment and safety of large
language models (LLMs). These challenges are organized into three different categories …

End-to-end neural Natural Language Processing (NLP) models are notoriously difficult to
understand. This has given rise to numerous efforts towards model explainability in recent …

Mechanistic interpretability seeks to understand the internal mechanisms of machine
learning models, where localization--identifying the important model components--is a key …

Explainability methods for NLP systems encounter a version of the fundamental problem of
causal inference: for a given ground-truth input text, we never truly observe the …