Alejandro Sánchez

Este artículo analiza la evolución de la política exterior rusa hacia Occidente entre 2000 y 2008 mediante un diseño cualitativo comparativo basado en _process tracing_. Se comparan dos discursos de Vladimir Putin —el del Bundestag (2001) y el de la Conferencia de Seguridad de Múnich (2007)— como indicadores empíricos del tránsito desde una lógica de integración condicionada hacia una postura neorrevisionista. La investigación sostiene, desde el realismo neoclásico, que este cambio responde a presiones sistémicas proyectadas a través de su política exterior y mediadas por su política doméstica. El giro se consolida en la guerra ruso-georgiana de 2008, cuyas prácticas de contestación violenta reaparecerían en Ucrania. Rusia no busca un orden internacional democrático, sino reivindicar su estatus de gran potencia y asegurar sus zonas de influencia.

Medical advice and recommendations should always consider the patient as a person, considering their dignity in particular. Analyzing the decision-making process is important to understand that the patient must make the decision freely, and the doctor must strive to consider their reasons, feelings, and context to create a therapeutic alliance, with respect and compassion, avoiding the dominant and authoritarian paternalism of the past. Thaler and Sunstein’s proposal of “libertarian paternalism” to change the environment and facilitate good decisions without coercion may be appropriate, provided that the doctor-patient relationship is capable of achieving that human bond of friendship, so that the patient trusts and accepts what the doctor proposes when they have been informed and educated about what is best for their health, promoting a more humanized, ethical medicine committed to the overall well-being of the human being.

In Artificial Intelligence, Artificial Neural Networks are very accurate models in tasks such as classification and regression in the study of natural phenomena, but they are considered “black boxes” because they do not allow direct explanation of what they address. This paper reviews the possibility of scientific explanation from these models and concludes that other efforts are required to understand their inner workings. This poses challenges to access scientific explanation through their use, since the nature of Artificial Neural Networks makes it difficult at first instance the scientific understanding that can be extracted from them.