Credit risk assessment plays a key role for correctly supporting financial institutes in defining
their bank policies and commercial strategies. Over the last decade, the emerging of social …

Human explanations of high-level decisions are often expressed in terms of key concepts
the decisions are based on. In this paper, we study such concept-based explainability for …

The rise of sophisticated machine learning models has brought accurate but obscure
decision systems, which hide their logic, thus undermining transparency, trust, and the …

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

There is a growing demand to be able to “explain” machine learning (ML) systems' decisions
and actions to human users, particularly when used in contexts where decisions have …

Understanding complex machine learning models such as deep neural networks with
explanations is crucial in various applications. Many explanations stem from the model …

With the availability of large datasets and ever-increasing computing power, there has been
a growing use of data-driven artificial intelligence systems, which have shown their potential …

Abstract With Artificial Intelligence becoming part of a rapidly increasing number of industrial
applications, more and more requirements about their transparency and trustworthiness are …

Safety engineering and artificial intelligence are two fields that still need investigation on
their reciprocal interactions. Safety should be guaranteed when autonomous decision may …

Decision support tools are key components of intelligent sociotechnical systems, and their
successful implementation faces a variety of challenges, including the multiplicity of …