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 …

Despite a sea of interpretability methods that can produce plausible explanations, the field
has also empirically seen many failure cases of such methods. In light of these results, it …

The proposed EU regulation for Artificial Intelligence (AI), the AI Act, has sparked some
debate about the role of explainable AI (XAI) in high-risk AI systems. Some argue that black …

Link Prediction (LP) aims at tackling Knowledge Graph incompleteness by inferring new,
missing facts from the already known ones. The rise of novel Machine Learning techniques …

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

This paper develops a rigorous argument for why the use of Shapley values in explainable
AI (XAI) will necessarily yield provably misleading information about the relative importance …

Abstract Although standard Machine Learning models are optimized for making predictions
about observations, more and more they are used for making predictions about the results of …

As machine learning has gradually entered into ever more sectors of public and private life,
there has been a growing demand for algorithmic explainability. How can we make the …

When users receive either a positive or negative outcome from an automated system,
Explainable AI (XAI) has almost exclusively focused on how to mutate negative outcomes …

The recent criticisms of the robustness of post hoc model approximation explanation
methods (like LIME and SHAP) have led to the rise of model-precise abductive explanations …