To analyze which ethically relevant biases have been identified by academic literature in artificial intelligence 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 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. (shrink)

The main aim of this article is to reflect on the impact of biases related to artificial intelligence (AI) systems developed to tackle issues arising from the COVID-19 pandemic, with special focus on those developed for triage and risk prediction. A secondary aim is to review assessment tools that have been developed to prevent biases in AI systems. In addition, we provide a conceptual clarification for some terms related to biases in this particular context. We focus mainly on nonracial biases (...) that may be less considered when addressing biases in AI systems in the existing literature. In the manuscript, we found that the existence of bias in AI systems used for COVID-19 can result in algorithmic justice and that the legal frameworks and strategies developed to prevent the apparition of bias have failed to adequately consider social determinants of health. Finally, we make some recommendations on how to include more diverse professional profiles in order to develop AI systems that increase the epistemic diversity needed to tackle AI biases during the COVID-19 pandemic and beyond. (shrink)

Conservation biology has embraced the development and application of artificial intelligence to optimize its work. The efficiency with which machine learning processes data helps to identify wild species, repair anthropogenic impacts, and intervene in ecosystems, offering supposedly good results for conservation. Thus, artificial intelligence can here be proposed as an ally of environmental justice. However, I will dispute this thesis, arguing that since conservation biology does not start from absolute parameters and environmental justice is not free from moral plurality, then (...) artificial intelligence could reproduce and increase epistemological and ethical biases. La biología de la conservación se ha sumado al uso de la inteligencia artificial para optimizar su trabajo. La eficiencia con que esta procesa los datos ayuda a identificar especies salvajes, reparar los impactos antropogénicos e intervenir en ecosistemas, ofreciendo resultados supuestamente buenos para la conservación. Así, la inteligencia artificial puede proponerse como una aliada de la justicia ambiental. Pero discutiré esta tesis, argumentando que como la biología de la conservación no parte de parámetros absolutos y la justicia ambiental no está exenta de una pluralidad moral, entonces la inteligencia artificial puede reproducir y aumentar los sesgos epistemológicos y éticos. (shrink)