Bias in algorithms of AI systems developed for COVID-19: A scoping review.

To analyze which ethically relevant biases have been identified by academic literature in artificial intelligence (AI) algorithms developed either for patient risk prediction and triage, or for contact tracing to deal with the COVID-19 pandemic. Additionally, to specifically investigate whether the role of social determinants of health (SDOH) have been considered in these AI developments or not. We conducted a scoping review of the literature, which covered publications from March 2020 to April 2021. ​Studies mentioning biases on AI algorithms developed for contact tracing and medical triage or risk prediction regarding COVID-19 were included. From 1054 identified articles, 20 studies were finally included. We propose a typology of biases identified in the literature based on bias, limitations and other ethical issues in both areas of analysis. Results on health disparities and SDOH were classified into five categories: racial disparities, biased data, socio-economic disparities, unequal accessibility and workforce, and information communication. SDOH needs to be considered in the clinical context, where they still seem underestimated. Epidemiological conditions depend on geographic location, so the use of local data in studies to develop international solutions may increase some biases. Gender bias was not specifically addressed in the articles included. The main biases are related to data collection and management. Ethical problems related to privacy, consent, and lack of regulation have been identified in contact tracing while some bias-related health inequalities have been highlighted. There is a need for further research focusing on SDOH and these specific AI apps.

[Efficiency is not enough. Ethical analysis and recommendations for the allocation of scarce resources in a pandemic situation]. / La eficiencia no basta. Análisis ético y recomendaciones para la distribución de recursos escasos en situación de pandemia.

OBJECTIVE: To develop a support tool to decision-making in the framework of the COVID-19 pandemic. METHOD: Different ethical recommendations that emerged in Spain on prioritizing scarce health resources in the COVID-19 pandemic first wave were searched; it was conducted a narrative review of theoretical models on distribution in pandemics to define an ethical foundation. Finally, recommendations are drawn to be applied in different healthcare settings. RESULTS: Three principles are identified; strict equality, equity and efficiency, which are substantiated in specific distribution criteria. CONCLUSIONS: A model for the distribution of scarce health resources in a pandemic situation is proposed, starting with a decision-making procedure and adapting the distribution criteria to different healthcare scenarios: primary care settings, nursing homes and hospitals.