Article
Version 1
Preserved in Portico This version is not peer-reviewed
Implementing Pro-social Rule Bending in an Elder-care Robot Environment
Version 1
: Received: 12 October 2023 / Approved: 12 October 2023 / Online: 13 October 2023 (03:00:35 CEST)
Version 2 : Received: 23 October 2023 / Approved: 23 October 2023 / Online: 23 October 2023 (10:04:14 CEST)
Version 2 : Received: 23 October 2023 / Approved: 23 October 2023 / Online: 23 October 2023 (10:04:14 CEST)
How to cite: Ramanayake, R.; Nallur, V. Implementing Pro-social Rule Bending in an Elder-care Robot Environment. Preprints 2023, 2023100788. https://doi.org/10.20944/preprints202310.0788.v1 Ramanayake, R.; Nallur, V. Implementing Pro-social Rule Bending in an Elder-care Robot Environment. Preprints 2023, 2023100788. https://doi.org/10.20944/preprints202310.0788.v1
Abstract
Many ethical issues arise when robots are introduced into elder-care settings. When ethically charged situations occur, robots ought to be able to handle them appropriately. Some experimental approaches use (top-down) moral generalist approaches, like Deontology and Utilitarianism, to implement ethical decision-making. Others have advocated the use of bottom-up approaches, such as learning algorithms, to learn ethical patterns from human behaviour. Both approaches have their shortcomings when it comes to real-world implementations. Human beings have been observed to use a hybrid form of ethical reasoning called Pro-Social Rule Bending, where top-down rules and constraints broadly apply, but in particular situations, certain rules are temporarily bent. This paper reports on implementing such a hybrid ethical reasoning approach in elder-care robots. We show through simulation studies that it leads to better upholding of human values such as autonomy, whilst not sacrificing beneficence.
Keywords
elder-care robots; machine ethics; ethical decision making; ethical governor; rule bending
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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