English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

The COVID-19 pandemic has spurred a large amount of observational studiesreporting linkages between the risk of developing severe COVID-19 or dying fromit, and sex and gender. By reviewing a large body of related literature andconducting a fine grained analysis based on sex-disaggregated data of 61countries spanning 5 continents, we discover several confounding factors thatcould possibly explain the supposed male vulnerability to COVID-19. We thushighlight the challenge of making causal claims based on available data, giventhe lack of statistical significance and potential existence of biases.Informed by our findings on potential variables acting as confounders, wecontribute a broad overview on the issues bias, explainability and fairnessentail in data-driven analyses. Thus, we outline a set of discriminatory policyconsequences that could, based on such results, lead to unintendeddiscrimination. To raise awareness on the dimensionality of such foreseenimpacts, we have compiled an encyclopedia-like reference guide, the BiasCatalog for Pandemics (BCP), to provide definitions and emphasize realisticexamples of bias in general, and within the COVID-19 pandemic context. Theseare categorized within a division of bias families and a 2-level priorityscale, together with preventive steps. In addition, we facilitate the BiasPriority Recommendations on how to best use and apply this catalog, and provideguidelines in order to address real world research questions. The objective isto anticipate and avoid disparate impact and discrimination, by consideringcausality, explainability, bias and techniques to mitigate the latter. Withthese, we hope to 1) contribute to designing and conducting fair and equitabledata-driven studies and research; and 2) interpret and draw meaningful andactionable conclusions from these.

Questioning causality on sex, gender and COVID-19, and identifying bias  in large-scale data-driven analyses: the Bias Priority Recommendations and  Bias Catalog for Pandemics