In this work, we provide some insights and develop some ideas, with few technical details,
about the role of explanations in Data Quality in the context of data-based machine learning …

In this article we review the main concepts around database repairs and consistent query
answering, with emphasis on tracing back the origin, motivation, and early developments …

We investigate the application of the Shapley value to quantifying the contribution of a tuple
to a query answer. The Shapley value is a widely known numerical measure in cooperative …

We propose a simple definition of an explanation for the outcome of a classifier based on
concepts from causality. We compare it with previously proposed notions of explanation, and …

On line analytical processing (OLAP) is an essential element of decision-support systems.
OLAP tools provide insights and understanding needed for improved decision making …

Attribution scores can be applied in data management to quantify the contribution of
individual items to conclusions from the data, as part of the explanation of what led to these …

Conflicting information in an agent's knowledge base may lead to a semantical defect, that
is, a situation where it is impossible to draw any plausible conclusion. Finding out the …

We propose and investigate a concrete numerical measure of the inconsistency of a
database with respect to a set of integrity constraints. It is based on a database repair …

In this expository article we highlight the relevance of explanations for artificial intelligence,
in general, and for the newer developments in explainable AI, referring to origins and …

Causality has been recently introduced in databases, to model, characterize, and possibly
compute causes for query answers. Connections between QA-causality and consistency …