Traceability is considered a key requirement for trustworthy artificial intelligence (AI), related
to the need to maintain a complete account of the provenance of data, processes, and …

In this paper, we foreground some of the key research challenges that arise in the design of
trustworthy human-AI partnerships. In particular, we focus on the challenges in designing …

After years of mostly theoretical research, Semantic Web Technologies are now reaching out
into application areas like bioinformatics, eCommerce, eGovernment, or Social Webs …

As increasing amounts of valuable information are produced and persist digitally, the ability
to determine the origin of data becomes important. In science, medicine, commerce, and …

Most of the large-scale scientific experiments modeled as scientific workflows produce a
large amount of data and require workflow parallelism to reduce workflow execution time …

Workflow provenance typically assumes that each module is a" black-box", so that each
output depends on all inputs (coarse-grained dependencies). Furthermore, it does not …

The purpose of this article is to highlight a main issue regarding reliable audit evidence
derived from Big Data—that of secure data provenance. Traditionally, audit evidence …

Background The automation of data analysis in the form of scientific workflows has become
a widely adopted practice in many fields of research. Computationally driven data-intensive …

One of the main challenges of scientific experiments is to allow scientists to manage and
exchange their scientific computational resources (data, programs, models, etc.). The …

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 …