Given that artificial moral agents—such as autonomous vehicles, lethal autonomous weapons, and automated trading systems—are now part of the socio-ethical equation, we should morally evaluate their behavior. How should artificial moral agents make decisions? Is one moral theory better suited than others for machine ethics? After briefly overviewing the dominant ethical approaches for building morality into machines, this paper discusses a recent proposal, put forward by Don Howard and Ioan Muntean (2016, 2017), for an artificial moral agent based on virtue theory. While the virtuous artificial moral agent has various strengths, this paper argues that a rule-based utilitarian approach (in contrast to a strict act utilitarian approach) is superior, because it can capture the most important features of the virtue-theoretic approach while realizing additional significant benefits. Specifically, a two-level utilitarian artificial moral agent incorporating both established moral rules and a utility calculator is especially well suited for machine ethics.
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There are approaches to ethics that eschew the big three theoretical traditions; but I would argue that such approaches cannot entirely avoid the concerns and lessons that study of these three traditions has revealed.
There are ways of developing AMAs that may not explicitly reference any standard moral theories, but rely upon a set of moral standards derived from common human interests or scientific surveys of preferences.
H&M (2017: 136–138) have a fourth analogy, that the moral cognition of an AMA can be modeled on machine learning—but this is logically implied by the others, a sort of conclusion to their set of analogies.
Their intention is that the virtuous AMA is geared towards specific moral domains such as patient care or autonomous vehicles (H&M 2017: 221), but it seems that with enough computational power it could be extended to handle multiple moral domains simultaneously.
One key difference between the two is that Bentham thinks there is only one kind of pleasure (any differences in pleasures are merely conceptual), whereas Mill makes a real distinction between intellectual and bodily pleasures, holding that the intellectual pleasures are superior.
Here, neural nets and deep learning could be incorporated towards identifying and establishing new rules or patterns of behavior (but this is a question for software engineers to decide).
Hooker (2000: 89) argues that conflicts between rules should not be resolved by applying Act U, partly because people would lose confidence in the system of rules; however, this is not a worry for machine ethics since confidence does not enter the equation.
Thanks to an anonymous reviewer for raising this objection.
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Thanks to participants at the Brain-Based and Artificial Intelligence workshop held at the Center for the Study of Ethics in the Professions, Illinois Institute of Technology (May 10–11, 2018), for helpful questions and discussion. Thanks also to two anonymous reviewers for helpful comments. Images of the stop and yield signs are courtesy of http://www.pixabay.com.
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Bauer, W.A. Virtuous vs. utilitarian artificial moral agents. AI & Soc 35, 263–271 (2020). https://doi.org/10.1007/s00146-018-0871-3
- Machine ethics
- Artificial moral agent
- Machine learning
- Virtue theory
- Two-level utilitarianism