Bioethics and artificial intelligence: between deliberation on values and rational choice theory

The present work revisits how artificial intelligence, as technology and ideology, is based on the rational choice theory and the techno-liberal discourse, supported by large corporations and investment funds. Those that promote using different algorithmic processes (such as filter bubbles or echo chambers) create homogeneous and polarized spaces that reinforces people’s ethical, ideological, and political narratives. These mechanisms validate bubbles of choices as statements of fact and contravene the prerequisites for exercising deliberation in pluralistic societies, such as the distinction between data and values, the affirmation of reasonable dissent, and the relevance of diversity as a condition indispensable for democratic deliberation

Corrigendum: Bioethics and Artificial Intelligence: Between Value Deliberation and Rational Choice Theory (Frontiers in Robotics and AI, (2023), 10, (1140901), 10.3389/frobt.2023.1140901)

En el artículo publicado, hubo un error en la afiliación 1. En lugar de "Departamento de Bioética, Universidad ElBosque, Bogotá, Colombia", debería ser "Departamento de Bioética, Bioética, Grupo de Investigación en Ciencias de la Vida, Universidad ElBosque, Bogotá, Colombia". En el artículo publicado, había un error en la afiliación 2. En lugar de "Facultad de Medicina y Ciencias de la Salud, Universidad del Rosario, Bogotá, Colombia", debe ser "Facultad de Medicina y Ciencias de la Salud, Universidad del Rosario, Grupo de Investigación en Ciencias Médicas y de la Salud, Bogotá, Colombia". En el artículo publicado, había un error en la afiliación 5. En lugar de "Departamento de Bioética, Universidad El Bosque, Bogotá, Colombia", debe ser "Departamento de Bioética, Grupo de Investigación en Bioética, Ciencias de la Vida, Universidad El Bosque, Bogotá, Colombia". En el artículo publicado, había un error en la afiliación 7. En lugar de "Medical, El BosqueUniversity,Bogotá,Colombia", debería ser "Medical,UniversidadElBosque,Bogotá, Colombia". 8. En el artículo publicado había un error. error en la traducción de una frase: "En este sentido, los algoritmos pueden formar parte de grupos de reglas de decisión incontrovertibles (carácter acrítico/normativo)". Se ha introducido una corrección en2AIyelRCT:laconversióndevaloresendatos, párrafo 12. Esta frase decía anteriormente: "En este sentido, los algoritmos pueden formar parte de grupos de reglas de toma de decisiones incontrovertibles (carácter acrítico/normativo)". La frase correcta aparece a continuación: "En este sentido, los algoritmos representan un conjunto de reglas de decisión incontrovertibles (carácter acrítico/normativo)". Los autores piden disculpas por este error y afirman que no cambia en nada las conclusiones científicas del artículo. El artículo original ha sido actualizado. Nota del editor Todas las afirmaciones expresadas en este artículo son exclusivamente las de los autores y no representan necesariamente las de sus organizaciones afiliadas, o las del editor, los editores y los revisores. Cualquier producto que pueda ser evaluado en este artículo, o afirmación que pueda ser hecha por su fabricante, no está garantizada ni avalada por el editor.Inthepublishedarticle,therewasanerrorinaffiliation1.Insteadof“BioethicsDepartment, ElBosqueUniversity,Bogotá,Colombia”,itshouldbe“BioethicsDepartment,Bioethics,Life SciencesResearchGroup,UniversidadElBosque,Bogotá,Colombia”. In the published article, there was an error in affiliation 2. Instead of “School of Medicine and Health Sciences, Del Rosario University, Bogotá, Colombia”, it should be “School of Medicine and Health Sciences, Universidad del Rosario, Medical and Health SciencesEducationResearchGroup,Bogotá,Colombia”. In the published article, there was an error in affiliation 5. Instead of “Department of Bioethics, El Bosque University, Bogotá, Colombia”, it should be “Bioethics Department, Bioethics,LifeSciencesResearchGroup,UniversidadElBosque,Bogotá,Colombia”. In the published article, there was an error in affiliation 7. Instead of “Medical, El BosqueUniversity,Bogotá,Colombia”,itshouldbe“Medical,UniversidadElBosque,Bogotá, Colombia”. In the published article, there was an error. error in the translation of a sentence: “In this sense, algorithms can be a part of groups of incontrovertible decision-making rules (uncritical/normativecharacter)”. Acorrectionhasbeenmadeto2AIandtheRCT:theconversionofvaluesintodata, paragraph12.Thissentencepreviouslystated: “In this sense, algorithms can be a part of groups of incontrovertibledecision-makingrules(uncritical/normativecharacter.” Thecorrectedsentenceappearsbelow: “In this sense, algorithms depict a set of incontrovertible decision-makingrules(uncritical/normativecharacter).” The authors apologize for this error and state that this does not change the scientific conclusions of the article in any way. The originalarticlehasbeenupdated. Publisher’s note All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers.Anyproductthatmaybeevaluatedinthisarticle,orclaim thatmaybemadebyitsmanufacturer,isnotguaranteedorendorsed bythepublisher

Social and moral psychology of COVID-19 across 69 countries

The COVID-19 pandemic has affected all domains of human life, including the economic and social fabric of societies. One of the central strategies for managing public health throughout the pandemic has been through persuasive messaging and collective behaviour change. To help scholars better understand the social and moral psychology behind public health behaviour, we present a dataset comprising of 51,404 individuals from 69 countries. This dataset was collected for the International Collaboration on Social & Moral Psychology of COVID-19 project (ICSMP COVID-19). This social science survey invited participants around the world to complete a series of moral and psychological measures and public health attitudes about COVID-19 during an early phase of the COVID-19 pandemic (between April and June 2020). The survey included seven broad categories of questions: COVID-19 beliefs and compliance behaviours; identity and social attitudes; ideology; health and well-being; moral beliefs and motivation; personality traits; and demographic variables. We report both raw and cleaned data, along with all survey materials, data visualisations, and psychometric evaluations of key variables

Psicología social y moral de COVID-19 en 69 países

La pandemia de COVID-19 ha afectado a todos los ámbitos de la vida humana, incluido el tejido económico y social de las sociedades. Una de las estrategias centrales para gestionar la salud pública a lo largo de la pandemia ha sido el envío de mensajes persuasivos y el cambio de comportamiento colectivo. Para ayudar a los estudiosos a comprender mejor la psicología social y moral que subyace al comportamiento en materia de salud pública, presentamos un conjunto de datos compuesto por 51.404 individuos de 69 países. Este conjunto de datos se recopiló para el proyecto de la Colaboración Internacional en Psicología Social y Moral de COVID-19 (ICSMP COVID-19). Esta encuesta de ciencias sociales invitó a participantes de todo el mundo a completar una serie de medidas morales y psicológicas y actitudes de salud pública sobre COVID-19 durante una fase temprana de la pandemia de COVID-19 (entre abril y junio de 2020). La encuesta incluía siete grandes categorías de preguntas: Creencias sobre COVID-19 y conductas de cumplimiento; identidad y actitudes sociales; ideología; salud y bienestar; creencias morales y motivación; rasgos de personalidad; y variables demográficas. Presentamos los datos brutos y depurados, junto con todos los materiales de la encuesta, las visualizaciones de los datos y las evaluaciones psicométricas de las variables clave.The COVID-19 pandemic has affected all domains of human life, including the economic and social fabric of societies. One of the central strategies for managing public health throughout the pandemic has been through persuasive messaging and collective behaviour change. To help scholars better understand the social and moral psychology behind public health behaviour, we present a dataset comprising of 51,404 individuals from 69 countries. This dataset was collected for the International Collaboration on Social & Moral Psychology of COVID-19 project (ICSMP COVID-19). This social science survey invited participants around the world to complete a series of moral and psychological measures and public health attitudes about COVID-19 during an early phase of the COVID-19 pandemic (between April and June 2020). The survey included seven broad categories of questions: COVID-19 beliefs and compliance behaviours; identity and social attitudes; ideology; health and well-being; moral beliefs and motivation; personality traits; and demographic variables. We report both raw and cleaned data, along with all survey materials, data visualisations, and psychometric evaluations of key variables

Predicting attitudinal and behavioral responses to COVID-19 pandemic using machine learning

At the beginning of 2020, COVID-19 became a global problem. Despite all the efforts to emphasize the relevance of preventive measures, not everyone adhered to them. Thus, learning more about the characteristics determining attitudinal and behavioral responses to the pandemic is crucial to improving future interventions. In this study, we applied machine learning on the multinational data collected by the International Collaboration on the Social and Moral Psychology of COVID-19 (N = 51,404) to test the predictive efficacy of constructs from social, moral, cognitive, and personality psychology, as well as socio-demographic factors, in the attitudinal and behavioral responses to the pandemic. The results point to several valuable insights. Internalized moral identity provided the most consistent predictive contribution—individuals perceiving moral traits as central to their self-concept reported higher adherence to preventive measures. Similar results were found for morality as cooperation, symbolized moral identity, self-control, open-mindedness, and collective narcissism, while the inverse relationship was evident for the endorsement of conspiracy theories. However, we also found a non-neglible variability in the explained variance and predictive contributions with respect to macro-level factors such as the pandemic stage or cultural region. Overall, the results underscore the importance of morality-related and contextual factors in understanding adherence to public health recommendations during the pandemic.Peer reviewe

National identity predicts public health support during a global pandemic (vol 13, 517, 2022) : National identity predicts public health support during a global pandemic (Nature Communications, (2022), 13, 1, (517), 10.1038/s41467-021-27668-9)

Publisher Copyright: © The Author(s) 2022.In this article the author name ‘Agustin Ibanez’ was incorrectly written as ‘Augustin Ibanez’. The original article has been corrected.Peer reviewe

National identity predicts public health support during a global pandemic.

Changing collective behaviour and supporting non-pharmaceutical interventions is an important component in mitigating virus transmission during a pandemic. In a large international collaboration (Study 1, N = 49,968 across 67 countries), we investigated self-reported factors associated with public health behaviours (e.g., spatial distancing and stricter hygiene) and endorsed public policy interventions (e.g., closing bars and restaurants) during the early stage of the COVID-19 pandemic (April-May 2020). Respondents who reported identifying more strongly with their nation consistently reported greater engagement in public health behaviours and support for public health policies. Results were similar for representative and non-representative national samples. Study 2 (N = 42 countries) conceptually replicated the central finding using aggregate indices of national identity (obtained using the World Values Survey) and a measure of actual behaviour change during the pandemic (obtained from Google mobility reports). Higher levels of national identification prior to the pandemic predicted lower mobility during the early stage of the pandemic (r = -0.40). We discuss the potential implications of links between national identity, leadership, and public health for managing COVID-19 and future pandemics

Predicting attitudinal and behavioral responses to COVID-19 pandemic using machine learning

At the beginning of 2020, COVID-19 became a global problem. Despite all the efforts to emphasize the relevance of preventive measures, not everyone adhered to them. Thus, learning more about the characteristics determining attitudinal and behavioral responses to the pandemic is crucial to improving future interventions. In this study, we applied machine learning on the multinational data collected by the International Collaboration on the Social and Moral Psychology of COVID-19 (N = 51,404) to test the predictive efficacy of constructs from social, moral, cognitive, and personality psychology, as well as socio-demographic factors, in the attitudinal and behavioral responses to the pandemic. The results point to several valuable insights. Internalized moral identity provided the most consistent predictive contribution—individuals perceiving moral traits as central to their self-concept reported higher adherence to preventive measures. Similar results were found for morality as cooperation, symbolized moral identity, self-control, open-mindedness, and collective narcissism, while the inverse relationship was evident for the endorsement of conspiracy theories. However, we also found a non-neglible variability in the explained variance and predictive contributions with respect to macro-level factors such as the pandemic stage or cultural region. Overall, the results underscore the importance of morality-related and contextual factors in understanding adherence to public health recommendations during the pandemic

Author Correction: National identity predicts public health support during a global pandemic

Correction to: Nature Communications https://doi.org/10.1038/s41467-021-27668-9, published online 26 January 2022