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 …

Heterogeneous tabular data are the most commonly used form of data and are essential for
numerous critical and computationally demanding applications. On homogeneous datasets …

While several types of post hoc explanation methods have been proposed in recent
literature, there is very little work on systematically benchmarking these methods. Here, we …

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 …

Artificial Intelligence (AI) is making a profound impact in almost every domain. A vital enabler
of its great success is the availability of abundant and high-quality data for building machine …

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 …

As machine learning (ML) models becomemore widely deployed in high-stakes
applications, counterfactual explanations have emerged as key tools for providing …

The complexity of state-of-the-art modeling techniques for image classification impedes the
ability to explain model predictions in an interpretable way. A counterfactual explanation …

As predictive models are increasingly being employed to make consequential decisions,
there is a growing emphasis on developing techniques that can provide algorithmic …