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.

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

Purpose: Research in the area of technology‐enhanced learning (TEL) throughout the last decade has largely focused on sharing and reusing educational resources and data. This effort has led to a fragmented landscape of competing metadata schemas, or interface mechanisms. More recently, semantic technologies were taken into account to improve interoperability. The linked data approach has emerged as the de facto standard for sharing data on the web. To this end, it is obvious that the application of linked data principles offers a large potential to solve interoperability issues in the field of TEL. This paper aims to address this issue.Design/methodology/approach: In this paper, approaches are surveyed that are aimed towards a vision of linked education, i.e. education which exploits educational web data. It particularly considers the exploitation of the wealth of already existing TEL data on the web by allowing its exposure as linked data and by taking into account automated enrichment and interlinking techniques to provide rich and well‐interlinked data for the educational domain.Findings: So far web‐scale integration of educational resources is not facilitated, mainly due to the lack of take‐up of shared principles, datasets and schemas. However, linked data principles increasingly are recognized by the TEL community. The paper provides a structured assessment and classification of existing challenges and approaches, serving as potential guideline for researchers and practitioners in the field.Originality/value: Being one of the first comprehensive surveys on the topic of linked data for education, the paper has the potential to become a widely recognized reference publication in the area.

Institutions in higher education (HE) continuously strive to develop and deliver impactful educational programs. At the same time, they should continue to fulfill their mission to educate students in basic applied subjects and in parallel respond to the need to equip students with new skills. For this reason, higher education institutions (HEI) perform periodical curricular reviews adhering to internal and external quality assurance systems. The subsequent curricular reforms are of a transformative nature, preparing graduates to tackle the challenges of globalization, unemployment and vanishing professions. For these reforms to lead to sustainable curricula, the integration of quality into educational programs is instrumental. A suggested way of achieving a transformative stance is to provide the context for the application and diffusion of quality metrics in teaching and learning. This research intends to provide a discussion of key performance indicators (KPIs) related to quality. This paper presents the second round of qualitative interviews with higher education administrators and professors as a promising vehicle for advancing towards the formulation of KPIs based on their understanding of the different independent dimensions of the quality construct. These KPIs will provide valuable insights into improving teaching, learning and assessment and will eventually lead to sustainable curricula. Research findings outline the significance of the time invested to design and update a course, indicate that technology-enhanced learning solutions are perceived as key quality drivers, and point out the need to align courses with industry requirements and real-world problems. Additionally, findings indicate that the quality and impact of teaching and learning is promoted by the multi/inter-disciplinary character of a course, the engagement of students in interactive discussions and student research as part of summative assessment. The main contribution of this research is an analytic discussion of perceptions of higher education administrators and professors about quality, leading to a significant enrichment of the relevant literature. A set of innovative generic KPIs which can be used in multidimensional quality assessment in higher education is eventually proposed.