Despite the proliferation of explainable AI (XAI) methods, little is understood about end-
users' explainability needs and behaviors around XAI explanations. To address this gap and …

The completeness axiom renders the explanation of a post-hoc XAI method only locally
faithful to the model, ie for a single decision. For the trustworthy application of XAI, in …

We present Backpacks: a new neural architecture that marries strong modeling performance
with an interface for interpretability and control. Backpacks learn multiple non-contextual …

Value decomposition is widely used in cooperative multi-agent reinforcement learning,
however, its implicit credit assignment mechanism is not yet fully understood due to black …

Abstract Generalized Additive Models (GAMs) have recently experienced a resurgence in
popularity due to their interpretability, which arises from expressing the target value as a …

Abstract Generalized Additive Models (GAMs) are a family of flexible and interpretable
models with old roots in statistics. GAMs are often used with pairwise interactions to improve …

Interpretability in machine learning has become increasingly important as machine learning
is being used in more and more applications, including those with high-stakes …

As an emerging interpretable technique, Generalized Additive Models (GAMs) adopt neural
networks to individually learn non-linear functions for each feature, which are then combined …

Deep neural networks (DNNs) have proven to be highly effective in a variety of tasks,
making them the go-to method for problems requiring high-level predictive power. Despite …

Neural networks have achieved remarkable success across various fields. However, the
lack of interpretability limits their practical use, particularly in critical decision-making …