Interpretable machine learning aims at unveiling the reasons behind predictions returned by
uninterpretable classifiers. One of the most valuable types of explanation consists of …

Machine learning is increasingly used to inform decision making in sensitive situations
where decisions have consequential effects on individuals' lives. In these settings, in …

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 …

Machine learning plays a role in many deployed decision systems, often in ways that are
difficult or impossible to understand by human stakeholders. Explaining, in a human …

There has been a recent resurgence of interest in explainable artificial intelligence (XAI) that
aims to reduce the opaqueness of AI-based decision-making systems, allowing humans to …

Existing and planned legislation stipulates various obligations to provide information about
machine learning algorithms and their functioning, often interpreted as obligations to …

We introduce a denoising diffusion algorithm to discover microstructures with nonlinear fine-
tuned properties. Denoising diffusion probabilistic models are generative models that use …

Representation learning constructs low-dimensional representations to summarize essential
features of high-dimensional data. This learning problem is often approached by describing …

Feature attributions are a common paradigm for model explanations due to their simplicity in
assigning a single numeric score for each input feature to a model. In the actionable …

Necessity and sufficiency are the building blocks of all successful explanations. Yet despite
their importance, these notions have been conceptually underdeveloped and inconsistently …