A Review of the Role of Causality in Developing Trustworthy AI Systems

State-of-the-art AI models largely lack an understanding of the cause-effect relationship that governs human understanding of the real world. Consequently, these models do not generalize to unseen data, often produce unfair results, and are difficult to interpret. This has led to efforts to improve the trustworthiness aspects of AI models. Recently, causal modeling and inference methods have emerged as powerful tools. This review aims to provide the reader with an overview of causal methods that have been developed to improve the trustworthiness of AI models. We hope that our contribution will motivate future research on causality-based solutions for trustworthy AI.Comment: 55 pages, 8 figures. Under revie

ELIXIR and Toxicology: a community in development [version 2; peer review: 2 approved]

Toxicology has been an active research field for many decades, with academic, industrial and government involvement. Modern omics and computational approaches are changing the field, from merely disease-specific observational models into target-specific predictive models. Traditionally, toxicology has strong links with other fields such as biology, chemistry, pharmacology, and medicine. With the rise of synthetic and new engineered materials, alongside ongoing prioritisation needs in chemical risk assessment for existing chemicals, early predictive evaluations are becoming of utmost importance to both scientific and regulatory purposes. ELIXIR is an intergovernmental organisation that brings together life science resources from across Europe. To coordinate the linkage of various life science efforts around modern predictive toxicology, the establishment of a new ELIXIR Community is seen as instrumental. In the past few years, joint efforts, building on incidental overlap, have been piloted in the context of ELIXIR. For example, the EU-ToxRisk, diXa, HeCaToS, transQST, and the nanotoxicology community have worked with the ELIXIR TeSS, Bioschemas, and Compute Platforms and activities. In 2018, a core group of interested parties wrote a proposal, outlining a sketch of what this new ELIXIR Toxicology Community would look like. A recent workshop (held September 30th to October 1st, 2020) extended this into an ELIXIR Toxicology roadmap and a shortlist of limited investment-high gain collaborations to give body to this new community. This Whitepaper outlines the results of these efforts and defines our vision of the ELIXIR Toxicology Community and how it complements other ELIXIR activities

Following SEIS Principles for Better FAIR Data Integration in EOSC

This study aimed to compare research trends regarding SEIS principles and FAIR data principles, in support of EOSC. Articles published in specific environmental policy areas, placed in the context of EU-Neighborhood South/East Regions Dialogue, were analyzed using content analysis. The evolution of EU interoperability was numerically classified in support of SEIS initiatives, compliant with FAIR principles, and able to benefit from the building of an EOSC community. The first proposed result refers to the extent of comparability of SEIS information storage and retrieval systems principles and FAIR data principles with respect to the implementation of the INSPIRE directive in Europe. This social comparison process occurred in two dimensions, trust concerning the resource FAIR and institutional loyalty according to SEIS. The relationships between SEIS and EOSC were estimated in its evolution according to five actions (cost, time, trust, best practices, cloud), and by exposing SEIS-friendly research infrastructures whose thematic data services points to the EOSC catalogue of services

One Health in a Digital World: Technology, Data, Information and Knowledge

Objectives: To describe the origins and growth of the One Health concept and its recent application in One Digital Health. Methods: Bibliometric review and critical discussion of emergent themes derived from co-occurrence of MeSH keywords. Results: The fundamental interrelationship between human health, animal health and the wider environment has been recognized since ancient times. One Health as a distinct term originated in 2004 and has been a rapidly growing concept of interest in the biomedical literature since 2017. One Digital Health has quickly established itself as a unifying construct that highlights the critical role of technology, data, information and knowledge to facilitate the interdisciplinary collaboration that One Health requires. The principal application domains of One Digital Health to date are in FAIR data integration and analysis, disease surveillance, antimicrobial stewardship and environmental monitoring. Conclusions: One Health and One Digital Health offer powerful lenses to examine and address crises in our living world. We propose thinking in terms of Learning One Health Systems that can dynamically capture, integrate, analyse and monitor application of data across the biosphere