Gender and sex bias in COVID-19 epidemiological data through the lens of causality Díaz Rodríguez, Natalia Ana Binkyte, Ruta Bakkali, Wafae Bookseller, Sannidhi Tubaro, Paola Bacevičius, Andrius Zhioua, Sami Chatila, Raja Explainability Causality Causal fairness COVID-19 Sex Gender Equality Artificial intelligence Healthcare The COVID-19 pandemic has spurred a large amount of experimental and observational studies reporting clear correlation between the risk of developing severe COVID-19 (or dying from it) and whether the individual is male or female. This paper is an attempt to explain the supposed male vulnerability to COVID-19 using a causal approach. We proceed by identifying a set of confounding and mediating factors, based on the review of epidemiological literature and analysis of sex-dis-aggregated data. Those factors are then taken into consideration to produce explainable and fair prediction and decision models from observational data. The paper outlines how non-causal models can motivate discriminatory policies such as biased allocation of the limited resources in intensive care units (ICUs). The objective is to anticipate and avoid disparate impact and discrimination, by considering causal knowledge and causalbased techniques to compliment the collection and analysis of observational big-data. The hope is to contribute to more careful use of health related information access systems for developing fair and robust predictive models. 2023-03-09T09:20:28Z 2023-03-09T09:20:28Z 2023-01-12 journal article Natalia Díaz-Rodríguez... [et al.]. Gender and sex bias in COVID-19 epidemiological data through the lens of causality, Information Processing & Management, Volume 60, Issue 3, 2023, 103276, ISSN 0306-4573, [https://doi.org/10.1016/j.ipm.2023.103276] https://hdl.handle.net/10481/80485 10.1016/j.ipm.2023.103276 eng info:eu-repo/grantAgreement/EC/H2020/835294 http://creativecommons.org/licenses/by-nc-nd/4.0/ open access Attribution-NonCommercial-NoDerivatives 4.0 Internacional Elsevier