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The synergies between understanding belief formation and artificial intelligence

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IIT-22-046R (272.8Kb)
IIT-22-046R_preview (2.467Kb)
Date
2022-12-31
Author
Lumbreras Sancho, Sara
Director/Coordinador
Morente López, Jesús
Estado
info:eu-repo/semantics/publishedVersion
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Abstract
Understanding artificial intelligence (AI) and belief formation have interesting bidirectional synergies. From explaining the logical derivation of beliefs and their internal consistency, to giving a quantitative account of mightiness, AI still has plenty of unexploited metaphors that can illuminate belief formation. In addition, acknowledging that AI should integrate itself with our belief processes (mainly, the capacity to reflect, rationalize, and communicate that is allowed by semantic coding) makes it possible to focus on more promising lines such as Interpretable Machine Learning.
 
Understanding artificial intelligence (AI) and belief formation have interesting bidirectional synergies. From explaining the logical derivation of beliefs and their internal consistency, to giving a quantitative account of mightiness, AI still has plenty of unexploited metaphors that can illuminate belief formation. In addition, acknowledging that AI should integrate itself with our belief processes (mainly, the capacity to reflect, rationalize, and communicate that is allowed by semantic coding) makes it possible to focus on more promising lines such as Interpretable Machine Learning.
 
URI
https:doi.org10.3389fpsyg.2022.868903
The synergies between understanding belief formation and artificial intelligence
Tipo de Actividad
Artículos en revistas
ISSN
1664-1078
Materias/ UNESCO
UNESCO::32 Medicina::3201 Ciencias clínicas::320199 Otras especialidades (Enfermería)
UNESCO::58 Pedagogía::5802 Organización y planificación de la educación
Materias/ categorías / ODS
Instituto de Investigación Tecnológica (IIT)
Palabras Clave
artificial intelligence, Belief, Machine bias, Complexity, reinforcement learning
artificial intelligence, Belief, Machine bias, Complexity, reinforcement learning
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