Special Issue on Trustworthy AI

Trustworthy Artificial Intelligence (TAI) systems have become a priority for the European Union and have
increased their importance worldwide. The European Commission has consulted a High-Level Expert Group that
has delivered a document on Ethics Guidelines for Trustworthy AI to promote Trustworthy AI principles.
TAI has three overarching components, which should be met throughout the system's entire life cycle: (1) it
should be lawful, complying with all applicable laws and regulations, (2) it should be ethical, ensuring adherence
to ethical principles and values, and (3) it should be robust, both from a technical and social perspective since,
even with good intentions, AI systems can cause unintentional harm. Each component in itself is necessary but
not sufficient for the achievement of TAI. Ideally, all three components work in harmony and overlap in their
operation. If, in practice, tensions arise between these components, society should endeavor to align them.
From a practical perspective these principles boil down into TAI dimensions, including:
● robustness
● reproducibility and replicability
● safety
● transparency and explainability
● diversity, non-discrimination and fairness
● auditing and independent oversight
● privacy and data governance
● sustainability
● accountability
In this special issue we call for surveys that address at least one dimension of TAI and provide a broad and
reasoned overview of the state of the art. We encourage reviewing and comparing methodologies that address
one specific trustworthiness dimension or the possible interplay and tensions between different dimensions. It
would be important to highlight how to assess the compliance and conformance to the TAI dimension chosen,
how to repair an existing system that does not comply with the dimension chosen, and how to design a
compliant-by-design system adhering to the dimension chosen. Connection with other disciplines related to the
dimension chosen is important to highlight the importance of multi-disciplinarity in AI.