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Normative Ethical Theory and AI Ethics

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Abstract

Normative ethical theories propose frameworks for determining value and for guiding judgment and action. We examine three dominant varieties: consequentialism, focusing on outcomes of actions; deontology, focusing on the nature of actions themselves; and virtue ethics, focusing on the moral character of the agent. There are significantly different varieties of each. Here, we outline the main characteristics, drawing attention to those features that lend themselves most readily to application in the context of AI, as well as giving an overview of the particular difficulties of each approach. We discuss different conceptions of the end goals of ethics, including different conceptions of happiness, pleasure, preference fulfilment, and the question of measuring outcomes. These all have implications for the ethical assessment of the development and deployment of AI and for implementing ethics within AI. The application of ethical theories to situations of rapidly changing technology presents many challenges. Likewise, accounts of agency and its moral relevance within different normative ethical theories are critical when considering the use of machine agency to enhance or replace human agency. The interpretation and following of moral rules are addressed; questions include the development of global guidelines for AI and the implementation of ethical rules within AI.

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Further Reading

    General Reading on Normative Ethical Theory

    Normative Ethical Theories for AI

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    Acknowledgements

    This chapter was partially funded by the National Institute for Health Research, Health Services and Delivery Research Programme (project number 13/10/80). The views expressed are those of the author and not necessarily those of the NIHR or the Department of Health and Social Care.

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    Boddington, P. (2023). Normative Ethical Theory and AI Ethics. In: AI Ethics. Artificial Intelligence: Foundations, Theory, and Algorithms. Springer, Singapore. https://doi.org/10.1007/978-981-19-9382-4_6

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    • DOI: https://doi.org/10.1007/978-981-19-9382-4_6

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