The ontology of social objects and facts remains a field of continued controversy. This situation complicates the life of social scientists who seek to make predictive models of social phenomena. For the purposes of modelling a social phenomenon, we would like to avoid having to make any controversial ontological commitments. The overwhelming majority of models in the social sciences, including statistical models, are built upon ontological assumptions that can be questioned. Recently, however, artificial neural networks have made their way into (...) the social sciences, raising the question whether they can avoid controversial ontological assumptions. ANNs are largely distinguished from other statistical and machine learning techniques by being a representation-learning technique. That is, researchers can let the neural networks select which features of the data to use for internal representation instead of imposing their preconceptions. On this basis, I argue that neural networks can avoid ontological assumptions to a greater degree than common statistical models in the social sciences. I then go on, however, to establish that ANNs are not ontologically innocent either. The use of ANNs in the social sciences introduces ontological assumptions typically in at least two ways, via the input and via the architecture. (shrink)

Desde la crítica feminista a John Rawls y la teoría del reconocimiento de Axel Honneth, se propone ampliar la noción liberal de justicia social para que las relaciones de amor y cuidado recuperen su papel vital. Se examina en qué medida y sentido la vida familiar constituye una esfera de justicia para mostrar cómo, además de la regulación externa, es necesaria una mirada valorativa de sus relaciones internas, sin olvidar que en la familia intervienen virtudes que exceden a la justicia.