DeepAI
Log In Sign Up

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

04/29/2021
by   Natalia Díaz Rodríguez, et al.
0

The COVID-19 pandemic has spurred a large amount of observational studies reporting linkages between the risk of developing severe COVID-19 or dying from it, and sex and gender. By reviewing a large body of related literature and conducting a fine grained analysis based on sex-disaggregated data of 61 countries spanning 5 continents, we discover several confounding factors that could possibly explain the supposed male vulnerability to COVID-19. We thus highlight the challenge of making causal claims based on available data, given the lack of statistical significance and potential existence of biases. Informed by our findings on potential variables acting as confounders, we contribute a broad overview on the issues bias, explainability and fairness entail in data-driven analyses. Thus, we outline a set of discriminatory policy consequences that could, based on such results, lead to unintended discrimination. To raise awareness on the dimensionality of such foreseen impacts, we have compiled an encyclopedia-like reference guide, the Bias Catalog for Pandemics (BCP), to provide definitions and emphasize realistic examples of bias in general, and within the COVID-19 pandemic context. These are categorized within a division of bias families and a 2-level priority scale, together with preventive steps. In addition, we facilitate the Bias Priority Recommendations on how to best use and apply this catalog, and provide guidelines in order to address real world research questions. The objective is to anticipate and avoid disparate impact and discrimination, by considering causality, explainability, bias and techniques to mitigate the latter. With these, we hope to 1) contribute to designing and conducting fair and equitable data-driven studies and research; and 2) interpret and draw meaningful and actionable conclusions from these.

READ FULL TEXT

page 1

page 2

page 3

page 4

08/12/2020

The Past, Present, and Future of COVID-19: A Data-Driven Perspective

Epidemics and pandemics have ravaged human life since time. To combat th...
07/27/2022

Causal foundations of bias, disparity and fairness

The study of biases, such as gender or racial biases, is an important to...
04/04/2020

Identifying Radiological Findings Related to COVID-19 from Medical Literature

Coronavirus disease 2019 (COVID-19) has infected more than one million i...
09/20/2022

Closing the Gender Wage Gap: Adversarial Fairness in Job Recommendation

The goal of this work is to help mitigate the already existing gender wa...
03/22/2022

A Survey on Techniques for Identifying and Resolving Representation Bias in Data

The grand goal of data-driven decision-making is to help humans make dec...