Txetxu Ausin

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.

The increasing application of artificial intelligence (AI) to healthcare raises both hope and ethical concerns. Some advanced machine learning methods provide accurate clinical predictions at the expense of a significant lack of explainability. Alex John London has defended that accuracy is a more important value than explainability in AI medicine. In this article, we locate the trade-off between accurate performance and explainable algorithms in the context of distributive justice. We acknowledge that accuracy is cardinal from outcome-oriented justice because it helps to maximize patients’ benefits and optimizes limited resources. However, we claim that the opaqueness of the algorithmic black box and its absence of explainability threatens core commitments of procedural fairness such as accountability, avoidance of bias, and transparency. To illustrate this, we discuss liver transplantation as a case of critical medical resources in which the lack of explainability in AI-based allocation algorithms is procedurally unfair. Finally, we provide a number of ethical recommendations for when considering the use of unexplainable algorithms in the distribution of health-related resources.

Interview with Philip Brey in which he clarifies and exemplifies the concept of ‘socially disruptive technology’, offering a series of key aspects for its present and future analysis from the disciplinary perspective of technology ethics. Philip Brey is Professor of Philosophy of Technology at the Department of Philosophy at the University of Twente. He has been a keynote speaker of the International Workshop on Controversies and Polarization on Disruptive Technologies, that took place virtually and in Granada, on October 5th and 6th 2020. His key-talk was titled “Disruptive Technologies: Social, Moral and Ontological Consequences”.

The application of Artificial Intelligence (AI) in healthcare and epidemiology undoubtedly has many benefits for the population. However, due to its environmental impact, the use of AI can produce social inequalities and long-term environmental damages that may not be thoroughly contemplated. In this paper, we propose to consider the impacts of AI applications in medical care from the One Health paradigm and long-term global health. From health and environmental justice, rather than settling for a short and fleeting green honeymoon between health and sustainability caused by AI, it should aim for a lasting marriage. To this end, we conclude by proposing that, in the upcoming years, it could be valuable and necessary to promote more interconnected health, call for environmental cost transparency, and increase green responsibility.

To assess—from a qualitative perspective—the perceptions and attitudes of Spanish rehabilitation professionals (e.g. rehabilitation doctors, speech therapists, physical therapists) about Brain–Computer Interface (BCI) technology. A qualitative, exploratory and descriptive study was carried out by means of interviews and analysis of textual content with mixed generation of categories and segmentation into frequency of topics. We present the results of three in-depth interviews that were conducted with Spanish speaking individuals who had previously completed a survey as part of a larger, 3-country/language, survey on BCI perceptions. 11 out of 15 of these Spanish respondents (survey) either strongly or somewhat accept the use of BCI in rehabilitation therapy. However, the results of our three in-depth interviews show how, due to a strong inertia of attitudes and perceptions about BCI technology, most professionals feel reluctant to use BCI technology in their daily practice (interview).

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.

Various research groups with ongoing projects in the areas of personal data protection, reuse of public sector information and open data organised a seminar on October 29, 2020, on reuse and big data in the field of health in which researchers, managers and policymakers from different disciplines participated. Starting from the premise of the special importance that access to health data has as a resource for research, management and development of health products, treatments and interventions and, at the same time, its consideration as a special category of data that is particularly sensitive, private and protected, this document seeks to systematise the main conclusions reached.

Hay dos tipos de bienes y derechos: de bienestar y de libertad. Los primeros, irrenunciables, acarrean deberes positivos, de dar o hacer. Los derechos de libertad, en cambio, sólo imponen a otros deberes negativos, dejando a su titular libre para ejercerlos o no según quiera. Sostenemos que el derecho a la vida es un derecho de libertad. De ahí se sigue que no es obligatorio aceptar el don de la vida salvo cuando el ordenamiento permite a los progenitores presumir el futuro consentimiento retroactivo del nuevo ser humano por nacer. Cuando las circunstancias hagan irrazonable tal presunción, es ilícita toda acción causal que contribuya a ese nacimiento salvo únicamente la acción voluntaria y libre de la madre de no abortar —por ser ése un derecho suyo. Matizamos nuestra teoría y la articulamos en un enfoque gradualista que da cuenta de la existencia de conflictos jurídicos

In diesem Aufsatz möchten wir Leibniz' abwägende und gewichtende Methoden als fruchtbare Annäherung an das Kontingente, das durch Debatten, Kontroversen und letztlich normative Konflikte bestimmte Universum der menschlichen Praxis betrachten. Dieser Ansatz ergänzt Leibniz' bekanntes algorithmisches Verfahren und ist der Jurisprudenz verpflichtet – Leibniz wandte ihn selbst auf viele theoretische wie praktische Unternehmen an (Rechtsfragen, Religionsstreitigkeiten, Theodizee, ethische und politische Fragestellungen etc.). Leibniz' Inanspruchnahme einer Balance der Gründe und sein hiermit verbundenes metaphysisches Prinzip der Kontinuität nehmen zudem in mancher Hinsicht die , Ausweitung' der Logik im gradualistischen Sinne voraus