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 artifacts involved in the production of an AI model. Traceability in AI shares part of its scope with general purpose recommendations for provenance as W3C PROV, and it is also supported to different extents by specific tools used by practitioners as part of their efforts in making data analytic processes reproducible or repeatable. Here, we review relevant tools, practices, and data models for traceability in their connection to building AI models and systems. We also propose some minimal requirements to consider a model traceable according to the assessment list of the High-Level Expert Group on AI. Our review shows how, although a good number of reproducibility tools are available, a common approach is currently lacking, together with the need for shared semantics. Besides, we have detected that some tools have either not achieved full maturity, or are already falling into obsolescence or in a state of near abandonment by its developers, which might compromise the reproducibility of the research trusted to them.

The Knowledge Society is the new context of our living and working. Towards this milestone, the International Journal of Knowledge and Learning reveals a scientific debate where academics, practitioners, researchers, policy makers, as well as government bodies, industry and nonprofit organisations provide their understanding for the integrated character of knowledge and learning. In this position document, we comment on the nature of Knowledge Society and we develop a ''Roadmap'' for our future discussions and contributions.

Delivered at the CRIS2016 Conference in St Andrews; published in Procedia Computer Science 106 (Mar 2017).-- Contains conference paper (7 pages) and presentation (11 slides).The increasing concern for the availability of scientific data has resulted in a number of initiatives promoting the archival and curation of datasets as a legitimate research outcome. Among them, dataset repositories fill the gap of providing long-term preservation of diverse kinds of data along with its meta-descriptions, and support citation. Unsurprisingly, the concern for quality arises as in the publication of papers. However, repositories support a larger variety of use cases, and many of them implement minimal control on the data uploaded by users. An approach to tackle with quality control in repositories is that of letting communities of users to filter the relevant resources for them, at the same time providing some form of trust to users of the data. However, there is a lack of knowledge of the extent to which this social approach that relies on communities self-organizing actually contributes to the effective organization inside repositories. This paper reports the results of a study on the Zenodo repository, describing its main contents and how communities have emerged naturally around the deposited contents