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.

A multidisciplinary journal that focuses on public health and technology, public health informatics, mass media campaigns, surveillance, and innovation in public health practice and research. Also dedicated to rapid open data sharing during epidemics.

Background: In recent years new forms of syndromic surveillance that use data from the Internet have been proposed. These have been developed to assist the early prediction of epidemics in various cases and diseases. It has been found that these systems are accurate in monitoring and predicting outbreaks before these are observed in population and, therefore, they can be used as a complement to other methods. In this research, our aim is to examine a highly infectious disease, measles, as there is no extensive literature on forecasting measles using Internet data, Methods: This research has been conducted with official data on measles for 5 years (2013–2018) from the competent authority of the European Union (European Center of Disease and Prevention - ECDC) and data obtained from Google Trends by using scripts coded in Python. We compared regression models forecasting the development of measles in the five countries. Results: Results show that measles can be estimated and predicted through Google Trends in terms of time, volume and the overall spread. The combined results reveal a strong relationship of measles cases with the predicted cases (correlation coefficient R= 0.779 in two-tailed significance p< 0.01). The mean standard error was relatively low 45.2 (12.19%) for the combined results. However, major differences and deviations were observed for countries with a relatively low impact of measles, such as the United Kingdom and Spain. For these countries, alternative models were tested in an attempt to improve the results. Conclusions: The estimation of measles cases from Google Trends produces acceptable results and can help predict outbreaks in a robust and sound manner, at least 2 months in advance. Python scripts can be used individually or within the framework of an integrated Internet surveillance system for tracking epidemics as the one addressed here.

Recently proposed legal frameworks for Artificial Intelligence (AI) depart from some frameworks of concepts regarding ethical and trustworthy AI that provide the technical grounding for safety and risk. This is especially important in high-risk applications, such as those involved in decision-making support systems in the biomedical domain. Frameworks for trustworthy AI span diverse requirements, including human agency and oversight, technical robustness and safety, privacy and data governance, transparency, fairness, and societal and environmental impact. Researchers and practitioners who aim to transition experimental AI models and software to the market as medical devices or to use them in actual medical practice face the challenge of deploying processes, best practices, and controls that are conducive to complying with trustworthy AI requirements. While checklists and general guidelines have been proposed for that aim, a gap exists between the frameworks and the actual practices. This paper reports the first scoping review on the topic that is specific to decision-making systems in the biomedical domain and attempts to consolidate existing practices as they appear in the academic literature on the subject.

Engineering knowledge is a specific kind of knowledge that is oriented to the production of particular classes of artifacts, is typically related to disciplined design methods, and takes place in tool-intensive contexts. As a consequence, representing engineering knowledge requires the elaboration of complex models that combine functional and structural representations of the resulting artifacts with process and methodological knowledge. The different categories used in the engineering domain vary in their status and in the way they should be manipulated when building applications that support engineering processes. These categories include artifacts, activities, methods and models. This paper surveys existing models of engineering knowledge and discusses an upper ontology that abstracts the categories that crosscut different engineering domains. Such an upper model can be reused for particular engineering disciplines. The process of creating such elaborations is reported on the particular case study of Software Engineering as a concrete application example.