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  • 1
    Online Resource
    Online Resource
    Berlin : Humboldt-Universität zu Berlin
    In:  2023
    ISSN: 1572-8676 , 1572-8676
    Language: English
    Pages: 1 Online-Ressource (20 Seiten)
    Publ. der Quelle: Dordrecht : Springer Science + Business Media B.V.
    Angaben zur Quelle: 2023
    DDC: 300
    Keywords: Phenomenology of substance addiction ; Lived body ; Cure and care ; Illness and disease ; Sozialwissenschaften
    Abstract: Substance addiction has been historically conceived and widely researched as a brain disease. There have been ample criticisms of brain-centred approaches to addiction, and this paper aims to align with one such criticism by applying insights from phenomenology of psychiatry. More precisely, this work will apply Merleau-Ponty’s insightful distinction between the biological and lived body. In this light, the disease model emerges as an incomplete account of substance addiction because it captures only its biological aspects. When considering addiction as a brain disorder, it will be shown that research fails to account for the contextual, functional, and emotional aspects inherent to subjective health. It is concluded that, while the disease model is fundamental to our understanding of what happens in the brain, its brain-centred approach is cure-oriented. Instead, we suggest a care-orientated approach, which understands and treats the psychological feel as bodily experience situated in an environment, allowing for a more encompassing therapeutic perspective.
    Abstract: Peer Reviewed
    URL: Volltext  (kostenfrei)
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  • 2
    Online Resource
    Online Resource
    Berlin : Humboldt-Universität zu Berlin
    In:  17
    Language: English
    Pages: 1 Online-Ressource (11 Seiten)
    Publ. der Quelle: Lausanne : Frontiers Research Foundation
    Angaben zur Quelle: 17
    DDC: 004
    Keywords: artificial intelligence ; human-robot interaction ; feminist technoscience ; enactivism ; sociocultural practices ; Informatik ; Sozialwissenschaften
    Abstract: Introduction: This paper presents Enactive Artificial Intelligence (eAI) as a gender-inclusive approach to AI, emphasizing the need to address social marginalization resulting from unrepresentative AI design. Methods: The study employs a multidisciplinary framework to explore the intersectionality of gender and technoscience, focusing on the subversion of gender norms within Robot-Human Interaction in AI. Results: The results reveal the development of four ethical vectors, namely explainability, fairness, transparency, and auditability, as essential components for adopting an inclusive stance and promoting gender-inclusive AI. Discussion: By considering these vectors, we can ensure that AI aligns with societal values, promotes equity and justice, and facilitates the creation of a more just and equitable society.
    Abstract: Peer Reviewed
    URL: Volltext  (kostenfrei)
    Library Location Call Number Volume/Issue/Year Availability
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