Desinformación en tiempos de pandemia: tipología de los bulos sobre la Covid-19

This study introduces the concept of “nobody-fools-me perception”, a cognitive bias consisting of overconfidence in one’s own ability to detect disinformation, associated with the belief that one is more immune to false content than almost everyone else. Specifically, it examines the extent to which variables such as age and education determine the conviction that one is able to spot false content, and influence the skills and habits of checking and sharing potentially unverified information on health, a serious problem in the context of the Covid-19 pandemic. Based on two face-to-face focus groups and one online focus group made up of Spanish people between the ages of 25 and 54, this qualitative research study explored the behaviour of regular citizens when assessing the truthfulness of health-related news, and their habits about believing it. The results reveal that younger people tended to distrust the ability of older people to spot false content, and vice versa. They also show that people with a higher educational level were more confident about their own immunity to disinformation. By introducing the concept of “nobody-fools-me perception”, this study contributes to our understanding of how subjective perceptions lead to believing in false news.

Historically, biomedical research has been led by and focused on men. The recent introduction of Artificial Intelligence (AI) in this area has further proven this practice to be discriminatory for other sexes and genders, more noticeably for women. To move towards a fair AI development, it is essential to include sex and gender diversity both in research practices and in the workplace. In this context, the Bioinfo4women (B4W) program of the Barcelona Supercomputing Center (i) promotes the participation of women scientists by improving their visibility, (ii) fosters international collaborations between institutions and programs and (iii) advances research on sex and gender bias in AI and health. In this article, we discuss methodology and results of a series of conferences, titled “Sex and Gender Bias in Artificial Intelligence and Health, organized by B4W and La Caixa Foundation from March to June 2021 in Barcelona, Spain. The series consisted of nine hybrid events, composed of keynote sessions and seminars open to the general audience, and two working groups with invited experts from different professional backgrounds (academic fields such as biology, engineering, and sociology, as well as NGOs, journalists, lawyers, policymakers, industry). Based on this awareness-raising action, we distilled key recommendations to facilitate the inclusion of sex and gender perspective into public policies, educational programs, industry, and biomedical research, among other sectors, and help overcome sex and gender biases in AI and health.

Journal of Medical Internet Research - International Scientific Journal for Medical Research, Information and Communication on the Internet #Preprint #PeerReviewMe: Warning: This is a unreviewed preprint. Readers are warned that the document has not been peer-reviewed by expert/patient reviewers or an academic editor, may contain misleading claims, and is likely to undergo changes before final publication, if accepted, or may have been rejected/withdrawn. Readers with interest and expertise are encouraged to sign up as peer-reviewer, if the paper is within an open peer-review period. Please cite this preprint only for review purposes or for grant applications and CVs (if you are the author). This paper carried out a research study on the perception and use of artificial intelligence (AI) for personalized medicine in transgender people. We applied the communicative methodology whose distinctive aspect is the active involvement of end-users and stakeholders through an egalitarian dialogue acknowledging cultural intelligence. During the design of the data collection-instruments, particularly in the elaboration of questions, multiple prior meetings with representatives of the trans community were organised to adapt the documents and co-create the workflow. Through the implementation of three focus groups, different problems and challenges that occur in the application of AI in the development of the precision medicine were discussed with trans adult people. The results show main obstacles that the participating people identified as well as possible solutions and proposed improvements. The work seeks to promote quality of life from a trans-inclusive and multidisciplinary perspective in a little-explored field of study.

This research study deepens the existing theory on information disorders by pro-viding, through an experimental methodology, new empirical evidence on the dy-namics of spreading health-related hoaxes and false content in the context of theCOVID-19 pandemic. Based on a diversified sample of digital news users in Spain(n=1,063), the study investigates the ability to distinguish legitimate news frommisinformation on 20 online publications about COVID-19. First, the study analy-ses the socio-demographic profiles of participants as education, age, gender, religion,ideology, technological knowledge and their ability to detect misinformation from le-gitimate news. Second, the characteristics of the news are analysed concerning thedifferent elements constitutive of the news and the importance on subjects to decideon the veracity of the piece of news. Multilevel logistic regression models were fittedon the socio-demographic profiles of participants. The individual factors, education,religion, age and technological literacy were found to be important predictors forcorrecting identifying the type of news, while the gender and ideology were inconclu-sive in both models. When looking at the message level, the headline, the previouslyread information and the presence of sources are the most important aspects for thesubject to inform their decision.

Precision medicine aims at tailoring treatments to individual patient needs. In this context, artificial intelligence (AI)-based technologies are viewed as revolutionary since they have the capacity to identify key features that link genomic and phenotypic traits at the individual level. AI techniques therefore depend on the quantity and quality of patient data. When variables like sex, age, or race are ignored in sample records, it can result in biased predictions as they will not be considered in the training of the AI algorithm. To this end, the European Genome-phenome Archive (EGA) took action in 2018 and put into place a rule that requires data providers to declare the sex of donor samples uploaded into their repository to improve data quality and prevent the spread of biased results. In this work we quantified biases in sex classification over time in human data from studies deposited in EGA and the database of Genotypes and Phenotypes (dbGaP), which represents the EGA's equivalent in the USA. The main result is that the EGA policy is effective to fight sex classification biases because there are significantly less samples classified as unknown after 2018 in this repository than in dbGaP. Additionally, we qualitatively assessed public opinion on this issue. A survey addressed to users, creators, maintainers, and developers of biological databases revealed that specialized training and additional knowledge about diversity criteria are required. Based on our findings, we raise awareness of sample bias problems and provide a list of recommendations for enhancing biomedical research practices.