In view of the growing complexity of modern software architectures, formal models are
increasingly used to understand why a system works the way it does, opposed to simply …

A new approach for defining causality and such related notions as degree of responsibility,
degrees of blame, and causal explanation. Causality plays a central role in the way people …

Neural networks have had discernible achievements in a wide range of applications. The
wide-spread adoption also raises the concern of their dependability and reliability. Similar to …

Causality is typically treated an all-or-nothing concept; either A is a cause of B or it is not. We
extend the definition of causality introduced by Halpern and Pearl [2004a] to take into …

An answer to a query has a well-defined lineage expression (alternatively called how-
provenance) that explains how the answer was derived. Recent work has also shown how to …

When a model does not satisfy a given specification, a counterexample is produced by the
model checker to demonstrate the failure. A user must then examine the counterexample …

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 …

Data provenance has evolved from a niche topic to a mainstream area of research in
databases and other research communities. This article gives a comprehensive introduction …

Provenance is often used to validate data, by verifying its origin and explaining its derivation.
When searching for “causes” of tuples in the query results or in general observations, the …

Large Language Models (LLMs) such as GPT and Llama2 are increasingly adopted in many
safety-critical applications. Their security is thus essential. Even with considerable efforts …