May Artificial Intelligence take health and sustainability on a honeymoon? Towards green technologies for multidimensional health and environmental justice.

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. Highlights Using AI in medicine and epidemiology has some benefits in the short term.AI usage may cause social inequalities and environmental damage in the long term.Health justice should be rethought from the One Health perspective.Going beyond anthropocentric and myopic cost-benefit analysis would expand health justice to include an environmental dimension.Greening AI would help to reconcile public and global health measures.

"Just" accuracy? Procedural fairness demands explainability in AI-based medical resource allocations.

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.

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.

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

Objetivo: Elaborar una herramienta de apoyo que ayude a tomar decisiones en el marco de la pandemia de COVID19. Método: Se parte de una búsqueda de diferentes recomendaciones éticas surgidas en España sobre priorización de recursos sanitarios escasos en la pandemia de COVID19, así como de una revisión narrativa de modelos teóricos sobre distribución en pandemias para definir una fundamentación ética. Finalmente, se extraen recomendaciones para su posible aplicación en distintos ámbitos asistenciales. Resultados: Se identifican tres principios, igualdad estricta, equidad y eficiencia, que se sustancian en criterios de distribución específicos. Conclusiones: Se propone un modelo de distribución de recursos sanitarios escasos en situación de pandemia que parte de un procedimiento de toma de decisiones y adapta los criterios de distribución a los escenarios de la atención sanitaria: atención primaria, residencias sociosanitarias y atención hospitalaria. (AU)

Objective: To develop a support tool to decision-making in the framework of the COVID19 pandemic. Method: Different ethical recommendations that emerged in Spain on prioritizing scarce health resources in the COVID19 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. (AU)

[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.

Traducir el pensamiento en acción: Interfaces cerebro-máquina y el problema ético de la agencia / Translating thought into action: brain-computer interfaces and the ethical problem of agency / Traduir el pensament en acció: Interfícies cervell-màquina i el problema ètic de l'agència

En este artículo, nos proponemos dos objetivos: el primero, describir la teoría clásica de la agencia intencional y cómo la neurotecnología de las interfaces cerebro-máquina desafía los requisitos de la teoría clásica de la agencia y de la consciencia corporal. La neurotecnología de las interfaces cerebro-máquina funciona implantando electrodos directamente en el área de la corteza motora del cerebro que controla el movimiento, y está diseñada para detectar las señales neuronales asociadas con la intención de moverse, que son después decodificadas por un algoritmo en un computador en tiempo real. Así, una persona podría pensar en mover su pierna o su brazo y la máquina recibiría la información de su pensamiento para traducir el pensamiento en acción, mediante prótesis internas o exoesqueletos. Esto es posible y sus aplicaciones se proyectan tanto sobre la rehabilitación de la funcionalidad motora, como sobre la posibilidad de mejoramiento (enhancement) de las capacidades humanas. Ambas aplicaciones dan lugar a numerosas implicaciones éticas, pero destacamos principalmente una, que denominamos: el problema ético de la agencia. El segundo objetivo del artículo es explorar brevemente la ética algorítmica en el contexto de las interfaces cerebro-máquina y cómo se entienden en este ámbito la autonomía, la responsabilidad y la privacidad informacional. Finalmente, abogamos por la necesidad de un marco ético de principios que regule la neurotecnología, y en tal sentido apelamos a los nuevos neuroderechos

The aim of this article is twofold: Firstly, we intend to describe the classical theory of intentional agency and to analyze how the neuro-technology of brain-machine interfaces (BCI) challenges the demands of that classical theory of agency and body consciousness. BCI neuro-technology works by implanting electrodes directly into the motor brain cortex that controls movement and detect neuronal signals associated with the intention to move, what is decoded by an algorithm on a computer in real time. Thus, someone could simply think about moving a leg or an arm and the tool (a prosthesis or exoskeleton) would receive the information to translate thought into action. This is yet feasible and its applications could involve rehabilitation of motor function and the possibility of enhancing human abilities. Both applications give rise to various several ethical implications but mainly to one that we call "the ethical problem of agency". Secondly, we briefly explore the ethics of algorithms in the context of BCI neuro-technology and the way autonomy, responsibility, and informational privacy are understood. Finally, we advocate the need for an ethical framework of principles governing neuro-technology, such as the new neuro-rights

En aquest article, ens proposem dos objectius: el primer, descriure la teoria clàssica de l'agència intencional i com la neurotecnologia de les interfícies cervell-màquina desafia els requisits de la teoria clàssica de l'agència i de la consciència corporal. La neurotecnologia de les interfícies cervell-màquina funciona implantant elèctrodes directament en l'àrea de l'escorça motora del cervell que controla el moviment, i està dissenyada per a detectar els senyals neuronals associades amb la intenció de moure's, que són després decodificades per un algoritme en un computador en temps real. Així, una persona podria pensar a moure la seva cama o el seu braç i la màquina rebria la informació del seu pensament per a traduir el pensament en acció, mitjançant pròtesis internes o exoesquelets. Això és possible i les seves aplicacions es projecten tant sobre la rehabilitació de la funcionalitat motora, com sobre la possibilitat de millorament (enhancement) de les capacitats humanes. Totes dues aplicacions donen lloc a nombroses implicacions ètiques, però destaquem principalment una, que denominem: el problema ètic de l'agència. El segon objectiu de l'article és explorar breument l'ètica algorítmica en el context de les interfícies cervell-màquina i com s'entenen en aquest àmbit l'autonomia, la responsabilitat i la privacitat informacional. Finalment, advoquem per la necessitat d'un marc ètic de principis que reguli la neurotecnologia, i en tal sentit apel·lem als nous neuro-drets