Feature attributions based on the Shapley value are popular for explaining machine
learning models. However, their estimation is complex from both theoretical and …

We present a brief history of the field of interpretable machine learning (IML), give an
overview of state-of-the-art interpretation methods and discuss challenges. Research in IML …

Causal Machine Learning (CausalML) is an umbrella term for machine learning methods
that formalize the data-generation process as a structural causal model (SCM). This …

Explaining complex or seemingly simple machine learning models is an important practical
problem. We want to explain individual predictions from such models by learning simple …

Researchers have proposed a wide variety of model explanation approaches, but it remains
unclear how most methods are related or when one method is preferable to another. We …

An increasing number of model-agnostic interpretation techniques for machine learning
(ML) models such as partial dependence plots (PDP), permutation feature importance (PFI) …

Explainability in AI is crucial for model development, compliance with regulation, and
providing operational nuance to predictions. The Shapley framework for explainability …

Many existing approaches for estimating feature importance are problematic because they
ignore or hide dependencies among features. A causal graph, which encodes the …

Local feature attribution methods are increasingly used to explain complex machine
learning models. However, current methods are limited because they are extremely …

The most common way to listen to recorded music nowadays is via streaming platforms,
which provide access to tens of millions of tracks. To assist users in effectively browsing …