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        Chapter 13 The Role of Neuroscience in Precise, Precautionary, and Probabilistic Accounts of Sentience

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        Collection
        Wellcome
        Language
        English
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        Abstract
        Given that there is currently no consensus as to exactly which animals are sentient, how should we make moral decisions when we are uncertain as to which of the animals influenced by that decision are sentient? And how relevant is evidence from the neurosciences for making these decisions? In this chapter, I outline three different approaches toward incorporating uncertainty about sentience into moral decision-making: what I call precise, precautionary, and probabilistic approaches to sentience. I suggest that neuroscientific evidence has different relevance for each of these accounts. Precautionary approaches should be adopted to provide basic protections for animals even when we are uncertain about their sentience, but probabilistic accounts are more relevant for decisions where we need to carefully weigh positive and negative consequences of different possible decisions. Precise accounts can be useful for providing guidance but are not directly relevant for making decisions or guiding policy.
        Book
        Neuroethics and Nonhuman Animals
        URI
        https://library.oapen.org/handle/20.500.12657/47111
        Keywords
        Sentience, Consciousness, Arguments by analogy, Precautionary principle
        Publisher
        Springer Nature
        Publisher website
        https://www.springernature.com/gp/products/books
        Publication date and place
        2020
        Grantor
        • Wellcome Trust - 203132/Z/16/Z
        Classification
        Neurosciences
        Pages
        13
        Rights
        https://creativecommons.org/licenses/by/4.0/
        • Imported or submitted locally

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        License

        • If not noted otherwise all contents are available under Attribution 4.0 International (CC BY 4.0)

        Credits

        • logo EU
        • This project received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 683680, 810640, 871069 and 964352.

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