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Moral Neurolearning by Machines: Artificial Values, Intelligences and Neural Networks

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Moral Neuroeducation for a Democratic and Pluralistic Society

Abstract

The recent success achieved by the companies and sectors whose products and services incorporate Artificial Intelligence (AI) has given rise to a striking and considerably disproportionate increase in expectations with regard to AI’s development and potential. Among other applications, AI is currently linked to the possibility of self-driving cars and algorithmic governance in the political and business worlds. However, considerable concern has been raised about the moral behaviour of the mathematical decision-making models incorporated into AI equipped products. However, just as AI’s outstanding technological issues are not easy to resolve, nor are the moral concerns it prompts. The aim of this chapter is to develop a critical-hermeneutic perspective on this issue and demonstrate the impossibility of developing morally intelligent technology in the present day.

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Notes

  1. 1.

    What keeps us connected is data, not language. In other words, in the era of digitalisation, communication is understood as data exchange, which makes it possible to stay connected with any user and/or element of the cyber-physical ecosystem.

  2. 2.

    Cyber-physical spaces consist of at least five basic characteristics: connection, generation, accumulation, prediction and decision.

  3. 3.

    As Effy Vayena and Urs Gasser (2016: 27) explain: “Our increasing interaction with digital technologies and devices, and their effects on us and our behavior, have given birth to new concept: the digital phenotype.” Since first being proposed in 2015 (Jain et al. 2015), this concept is closely linked to the idea of an extended phenotype suggested by Richard Dawkins (1982), who argued that the idea of the phenotype should not be limited to biological processes. Thereby, interactions with the environment and the way we alter it also come into play, generating a much broader phenotype. See, Vayena and Gasser, “Capturing and understanding these interactions allows greater understanding of how we function” (2016: 27).

  4. 4.

    This approach is also known as supervised learning, as the machine requires a degree of human support or help in order to make decisions, such as, for example, in knowing what is right and what is wrong.

  5. 5.

    For an in-depth study of the ethical aspects of neuromarketing, see Conill (2012) and Conill and García-Marzá (2012).

  6. 6.

    For a study of all these contributions, see Calvo (2018).

  7. 7.

    For a detailed study of moral neuroeducation, see Pallarés-Domíguez (2016, 2017)

  8. 8.

    For a detailed study of moral neuroeducation in business, see Medina-Vicent and Pallarés-Domínguez (2017).

  9. 9.

    For a detailed study of these and other contributions, see González-Esteban (2016).

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Authors and Affiliations

  1. Universitat Jaume I, Castellón de la Plana, Spain

    Patrici Calvo

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  1. Patrici Calvo
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Correspondence to Patrici Calvo .

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  1. Universitat Jaume I, Castellón de la Plana, Spain

    Patrici Calvo

  2. Universidad de Valencia, Valencia, Spain

    Javier Gracia-Calandín

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Calvo, P. (2019). Moral Neurolearning by Machines: Artificial Values, Intelligences and Neural Networks. In: Calvo, P., Gracia-Calandín, J. (eds) Moral Neuroeducation for a Democratic and Pluralistic Society. Springer, Cham. https://doi.org/10.1007/978-3-030-22562-9_13

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