Cristian Moyano-Fernández

En este artículo se ofrecerán tres razones a fin de defender una fraternidad política con los animales. La primera comprenderá a los animales como miembros de la misma clase social oprimida que los humanos durante el desarrollo capitalista. La segunda analizará el concepto de ciudadanía matizando su rasgo de participación política y resaltando el rasgo de compartir un espacio común. La tercera introducirá el dinamismo orgánico y ecológico en el espacio de convivencia. Finalmente, mediante una relativización del concepto de vulnerabilidad, se concluirá que nuestra relación política con los animales no ha de ser siempre paternalista, hospitalaria o solidaria, sino también fraternal.

The context of accelerated climate change, environmental pollution, ecosystems depletion, loss of biodiversity and growing undernutrition has led human societies to a crossroads where food systems require transformation. New agricultural practices are being advocated in order to achieve food security and face environmental challenges. Cultivated meat has recently been considered one of the most desired alternatives by animal rights advocates because it promises to ensure nutrition for all people while dramatically reducing ecological impacts and animal suffering. It is therefore presented as one of the fairest means of food production for the coming decades, according to utilitarian arguments. However, food security, environmental concerns and animal welfarism guided by a short-term utilitarianism could have techno-optimism bias and could result in some forms of oppression such as anthropocentrism. I argue that there are still deep-rooted moral issues in food systems that are not addressed primarily by lab-grown meat, mainly derived from a loss of sovereignty. Food practices developed in high-tech labs with artificial interventionism constrain the ability of living entities (that are used as food) to flourish on their own terms. This paper aims to explore how sovereignty entitlements for humans and nonhumans are often overlooked by advocates of cultivated meat and the moral challenges it may pose. Accordingly, a more than utilitarian approach framed by ecological and republican justice is proposed here to shed light on some pitfalls of food chains based on cellular agriculture.

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.

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.

Solidarity within bioethics is increasingly being recognized as an important means of improving health for all. Its contribution seems particularly relevant when there are injustices or inequalities in health and different individuals or groups are disadvantaged. But the current context of ecological collapse, characterized mainly by a loss of biodiversity and ecosystem decline, affects global health in a different way to other factors. This scenario creates new challenges, risks and problems that require new insights from a bioethical perspective. I, therefore, propose an argument in favor of ecological solidarity. The aim of this article is to re-define this concept, outlining which causes should incite action through ecological solidarity and who should be the main recipient of it. To this end, I discuss what the background for practicing ecological solidarity might be: an intrinsically altruistic motivation to attempt to be a better person or a forced response to a political obligation. Finally, by way of example, I argue for rewilding as an effective, practical strategy through which ecological solidarity can be applied in the belief that building ecological solidarity supports a number of key interdependencies and ensures ethical care for the health of the planet.

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.

Artificial Intelligence (AI) developments are proliferating at an astonishing rate. Unsurprisingly, the number of meaningful studies addressing the social impacts of AI applications in several fields has been remarkable. More recently, several contributions have started exploring the ecological impacts of AI. Machine learning systems do not have a neutral environmental cost, so it is important to unravel the ecological footprint of these techno-scientific developments. In this chapter, we discuss the sustainability of AI from environmental ethics approaches. We examine the moral trade-offs that AI may cause in different moral dimensions and analyse prominent conflicts that may arise from human and more-than-human-centred concerns.

The capabilities approach has largely addressed individual capabilities via a liberal framework common in its literature. However, a growing number of scholars concerned with sustainable human development are analyzing theories and methodologies that are both suitable for human flourishing and display a respect for nature. This paper explores several forms of considering the value of non-animal and non-individual natural entities, such as ecosystems. I first expose some instrumental reasons why we may care about the flourishing of ecosystems and then other reasons based on the assumption that they have integrity and their own capabilities and, therefore, deserve moral consideration. I argue that despite the possible moral conflicts that may emerge between human and ecosystemic autonomy, they could be avoided by adopting an ecological justice virtue. I present this ecological justice characterized by some contributions of decolonial thought and environmental virtue ethics. I propose that if the capabilities approach was not anchored only in an individualistic ontology, it could better assume a multi-level axiology from which the inherent and instrumental value of ecosystems would be interconnected. And, to this end, I find the concept of synergetic flourishing helpful to accept an interdependent and non-human-centered recognition of the capabilities.

Given the pervasiveness of AI systems and their potential negative effects on people’s lives (especially among already marginalised groups), it becomes imperative to comprehend what goes on when an AI system generates a result, and based on what reasons, it is achieved. There are consistent technical efforts for making systems more “explainable” by reducing their opaqueness and increasing their interpretability and explainability. In this paper, we explore an alternative non-technical approach towards explainability that complement existing ones. Leaving aside technical, statistical, or data-related issues, we focus on the very conceptual underpinnings of the design decisions made by developers and other stakeholders during the lifecycle of a machine learning project. For instance, the design and development of an app to track snoring to detect possible health risks presuppose some picture or another of “health”, which is a key notion that conceptually underpins the project. We take it as a premise that these key concepts are necessarily present during design and development, albeit perhaps tacitly. We argue that by providing “justificatory explanations” about how the team understands the relevant key concepts behind its design decisions, interested parties could gain valuable insights and make better sense of the workings and outcomes of systems. Using the concept of “health”, we illustrate how a particular understanding of it might influence decisions during the design and development stages of a machine learning project, and how making this explicit by incorporating it into ex-post explanations might increase the explanatory and justificatory power of these explanations. We posit that a greater conceptual awareness of the key concepts that underpin design and development decisions may be beneficial to any attempt to develop explainability methods. We recommend that “justificatory explanations” are provided as technical documentation. These are declarative statements that contain at its simplest: (1) a high-level account of the understanding of the relevant key concepts a team possess related to a project’s main domain, (2) how these understandings drive decision-making during the life-cycle stages, and (3) it gives reasons (which could be implicit in the account) that the person or persons doing the explanation consider to have plausible justificatory power for the decisions that were made during the project.

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.