Shapley values underlie one of the most popular model-agnostic methods within
explainable artificial intelligence. These values are designed to attribute the difference …

A number of techniques have been proposed to explain a machine learning model's
prediction by attributing it to the corresponding input features. Popular among these are …

Game-theoretic formulations of feature importance have become popular as a way to"
explain" machine learning models. These methods define a cooperative game between the …

Explaining AI systems is fundamental both to the development of high performing models
and to the trust placed in them by their users. The Shapley framework for explainability has …

Popular feature importance techniques compute additive approximations to nonlinear
models by first defining a cooperative game describing the value of different subsets of the …

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 …

The Shapley value has become the basis for several methods that attribute the prediction of
a machine-learning model on an input to its base features. The use of the Shapley value is …

In explainable machine learning, local post-hoc explanation algorithms and inherently
interpretable models are often seen as competing approaches. This work offers a partial …

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

As black box explanations are increasingly being employed to establish model credibility in
high stakes settings, it is important to ensure that these explanations are accurate and …