Explainable Machine Learning

We are interested in studying method to explain relationships between inputs and outputs of black-box machine learning models, particularly in the context of challenging NLU tasks such as fact checking.

Publications

Sparse attention has been claimed to increase model interpretability under the assumption that it highlights influential inputs. Yet …

Recent developments in machine learning have introduced models that approach human performance at the cost of increased architectural …

Adversarial attacks reveal important vulnerabilities and flaws of trained models. One potent type of attack are universal adversarial …

While state-of-the-art NLP explainability (XAI) methods focus on supervised, per-instance end or diagnostic probing task evaluation[4, …

This paper provides the first study of how fact checking explanations can be generated automatically based on available claim context, …