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Increasingly complex and autonomous systems require machine ethics to maximize the benefits and minimize the risks to society arising from the new technology. It is challenging to decide which type of ethical theory to employ and how to implement it effectively. This survey provides a threefold contribution. First, it introduces a trimorphic taxonomy to analyze machine ethics implementations with respect to their object (ethical theories), as well as their nontechnical and technical aspects. Second, an exhaustive selection and description of relevant works is presented. Third, applying the new taxonomy to the selected works, dominant research patterns, and lessons for the field are identified, and future directions for research are suggested.
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Implementations in Machine Ethics: A Survey
and ABRAHAM BERNSTEIN, University of Zürich
Increasingly complex and autonomous systems require machine ethics to maximize the benets and mini-
mize the risks to society arising from the new technology. It is challenging to decide which type of ethical
theory to employ and how to implement it eectively. This survey provides a threefold contribution. First,
it introduces a trimorphic taxonomy to analyze machine ethics implementations with respect to their object
(ethical theories), as well as their nontechnical and technical aspects. Second, an exhaustive selection and de-
scription of relevant works is presented. Third, applying the new taxonomy to the selected works, dominant
research patterns, and lessons for the eld are identied, and future directions for research are suggested.
CCS Concepts: • Computing methodologies Articial intelligence;Philosophical/theoretical foundations
of articial intelligence;•Social and professional topics;
Additional Key Words and Phrases: Machine ethics, articial morality
ACM Reference format:
Suzanne Tolmeijer, Markus Kneer, Cristina Sarasua, Markus Christen, and Abraham Bernstein. 2020. Imple-
mentations in Machine Ethics: A Survey. ACM Comput. Surv. 53, 6, Article 132 (December 2020), 38 pages.
Autonomous machines are increasingly taking over human tasks. Initially, simple and limited as-
signments such as assembly line labor were taken over by machines. Nowadays, more complex
tasks are transferred to software and robots. Even parts of jobs that were previously deemed purely
human occupations, such as being a driver, credit line assessor, medical doctor, or soldier are pro-
gressively carried out by machines (e.g., References [39,45]). As many believe, ceding control over
important decisions to machines requires that they act in morally appropriate ways. Or, as Picard
puts it, “the greater the freedom of a machine, the more it will need moral standards” [106, p. 134].
For this reason, there has been a growing interest in Machine Ethics, dened as the disci-
pline “concerned with the consequences of machine behavior toward human users and other
This work is partially funded by armasuisse Science and Technology (S+T), via the Swiss Center for Drones and Robotics
of the Department of Defense, Civil Protection and Sport (DDPS). Research on this paper was also supported by an SNSF
Ambizione grant for the project Reading Guilty Minds (PI Markus Kneer, PZ00P1_179912).
Authors’ addresses: S. Tolmeijer, C. Sarasua, and A. Bernstein, Department of Informatics, University of Zurich, Binzmüh-
lestrasse 14, 8050 Zürich, Switzerland; emails: {tolmeijer, sarasua, bernstein}@i.uzh.c; M. Kneer, Centre for Ethics, Uni-
versity of Zurich, Zollikerstrasse 118, 8008 Zurich, Switzerland; email:; M. Christen: Institute of
Biomedical Ethics and History of Medicine and Digital Society Initiative, University of Zurich, Winterthurerstrasse 30,
8006 Zürich, Switzerland; email:
This work is licensed under a Creative Commons Attribution International 4.0 License.
© 2020 Copyright held by the owner/author(s).
ACM Computing Surveys, Vol. 53, No. 6, Article 132. Publication date: December 2020.
132:2 S. Tolmeijer et al.
machines” [6,p.1].
1Research in this eld is a combination of computer science and moral
philosophy. As a result, publications range from theoretical essays on what a machine can or
should do (e.g., References [26,49,61,127]) to prototypes implementing ethics in a system
(e.g., References [3,147]). In this eld, the emphasis lies on how to design and build a machine
such that it could act ethically in an autonomous fashion.2
The need that complex machines should interact with humans in an ethical way is undisputed;
but for understanding which design requirements follow from this necessity requires a system-
atic approach that is usually based on a taxonomy. There have been several attempts to classify
current approaches of machine ethics. A rst high-level classication was proposed by Allen et al.
[2] in 2005, distinguishing between top-down theory-driven approaches, bottom-up learning ap-
proaches, and hybrids of the two. Subsequent work tried to further determine types of procedures
[32,152,153], but these works were either mixing dierent dimensions (e.g., mixing technical ap-
proach and ethical theory in one category) [152] or oering an orthogonal dimension that did not
t the existing taxonomy (e.g., whether normative premises can dier between ethical machines)
[32]. Also, because these works did not provide an extensive and systematic overview of the ap-
plication of their taxonomy, verication of the taxonomy with papers from the eld was missing.
A recent survey from Yu et al. [153] on ethics in AI has some overlap with this work but (1) does
not systematically apply the ethical theory classication to selected papers and (2) takes a broader
perspective to include consequences of and interaction with ethical AI, while this article focuses
specically on machine ethics implementations. Hence, compared to previous works, this survey
covers more related work, provides a more extensive classication, and describes the relationship
between dierent ethics approaches and dierent technology solutions in more depth than previ-
ous work [2,32,152,153]. Furthermore, gaps are identied regarding nontechnical aspects when
implementing ethics in existing systems.
This article is created as a collaboration between ethicists and computer scientists. In the context
of implementing machine ethics, it can be a pitfall for philosophers to use a purely theoretical
approach without consulting computer scientists, as this can result in theories that are too abstract
to be implemented. Conversely, computer scientists may implement a faulty interpretation of an
ethical theory if they do not consult a philosopher. In such an interdisciplinary eld, it is crucial
to have a balanced cooperation between the dierent elds involved.
The contributions of this article are as follows:
Based on previous work [2], a trimorphic taxonomy is dened to analyze the eld based on
three dierent dimensions: types of ethical theory (Section 4), nontechnical aspects when
implementing those theories (Section 5), and technological details (Section 6).
The reviewed publications are classied and research patterns and challenges are identied
(Section 7).
An exhaustive selection and description of relevant contributions related to machine ethics
implementations is presented (Appendix A).
A number of general lessons for the eld are discussed and further important research di-
rections for machine ethics are outlined (Section 8).
1While there are other terms for the eld, such as “Articial Morality” and “Computational Ethics,” the term “Machine
Ethics” will be used throughout this survey to indicate the eld.
2In the following, the expression “implementations in machine ethics” concerns all relevant aspects to successfully create
real-world machines that can act ethically—namely the object of implementation (the ethical theory), as well as nontech-
nical and technical implementation aspects when integrating those theories into machines. By “machine” we denote both
software and embodied information systems (such as robots).
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Implementations in Machine Ethics: A Survey 132:3
As such, this survey aims to provide a guide, not only to researchers but also to those interested
in the state of the art in machine ethics, as well as seed a discussion on what is preferred and
accepted in society, and how machine ethics should be implemented.
The rest of this article is structured as follows. Section 2introduces the eld of machine ethics,
its importance, and justication of used terminology throughout the article. Section 3lists the
methodology used to create this survey, including the search methodology, the process of creating
the classication dimensions, and the actual classication process. Sections 4,5,and6introduce
the three classication dimensions presented in this survey. Section 7discusses the results of the
classication of the selected papers. Finally, Section 8outlines which future avenues of research
may be interesting to pursue based on the analysis, as well as the limitations of this survey.
Before going into more depth on the implementation of ethics, it is important to establish what is
considered machines ethics, why it matters, and present the relevant terminology for the eld.
2.1 Relevance
Software and hardware (combined under the term “machine” throughout this survey) are increas-
ingly assisting humans in various domains. They are also tasked with many types of decisions
and activities previously performed by humans. Hence, there will be a tighter interaction between
humans and machines, leading to the risk of less meaningful human control and an increased
number of decision made by machines. As such, ethics needs to be a factor in decision making to
consider fundamental problems such as the attribution of responsibility (e.g., Reference [127]) or
what counts as morally right or wrong in the rst place (e.g., Reference [140]). Additionally, ethics
is needed to reduce the chance of negative results for humans and/or to mitigate the negative
eects machines can cause.
Authors in the eld give dierent reasons for studying (implementations in) machine ethics.
Fears of the negative consequences of AI motivate the rst category of reasons: creating machines
that do not have a negative societal impact [13,89]. With further autonomy and complexity of
machines, ethics need to be implemented in a more elaborate way [27,41,52,78,98,102]. Society
needs to be able to rely on machines to act ethically when they gain autonomy [6,51]. A second
category of reasons for studying machine ethics focuses on the ethics part: By implementing ethics,
ethical theory will be better understood [27,62,98,102]. Robots might even outperform humans
in terms of ethical behavior at some point [4,9].
Some authors contend that in cases with no consensus on the most ethical way to act, the ma-
chine should not be allowed to act autonomously [5,127]. However, not acting does not imply the
moral conundrum is avoided. In fact, the decision not to act also has a moral dimension [58,81,149]
—think, for example, of the dierence between active and passive euthanasia [111]. Additionally,
by not allowing the machine to act, all the possible advantages of these machines are foregone.
Take, for example, autonomous cars: A large number of trac accidents could be avoided by al-
lowing autonomous cars on the road. Moreover, simply not allowing certain machines would not
stimulate the conversation on how to solve the lack of consensus, a conversation that can lead to
new, more practical ethical insights and helpful machines.
2.2 Terminology
An often-used term in the eld of machine ethics is “Articial Moral Agent” or AMA, to refer to a
machine with ethics as part of its programming. However, to see whether this term is appropriate
to use, it is important to identify what moral agents mean in the context of machine ethics and
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132:4 S. Tolmeijer et al.
how ethical machines should be regarded. In an often-cited paper, Moor [98] denes four dierent
levels of moral agents:
Ethical-impact agents are types of agents that have an (indirect) ethical impact. An example
would be a simple assembly line robot that replaces a human in a task. The robot itself
does not do anything (un)ethical by acting. However, by existing and doing its task, it has
an ethical impact on its environment; in this case, the human that performed the task is
replaced and has to nd another job.
Implicit ethical agents do not have any ethics explicitly added in their software. They are
considered implicitly ethical, because their design involves safety or critical reliability
concerns. For example, autopilots in airplanes should let passengers arrive safely and on
Explicit ethical agents draw on ethical knowledge or reasoning that they use in their deci-
sion process. They are explicitly ethical, since normative premises can be found directly
in their programming or reasoning process.
Fully ethical agents can make explicit judgments and are able to justify these judgments.
Currently, humans are the only agents considered to be full ethical agents, partially be-
cause they have consciousness, free will, and intentionality.
While these denitions can help with a rst indication of the types of ethical machines, they
do not allow for distinctions from a technical perspective and are also unclear from a philosoph-
ical perspective: Moor [98] does not actually dene what a moral agent is. For example, it can
be debated whether an autopilot is an agent. Therefore, a clearer denition is needed of what an
agent is. Himma [77] investigates the concepts of agency and moral agency, drawing from philo-
sophical sources such as the Stanford Encyclopedia of Philosophy and Routledge Encyclopedia of
Philosophy. He proposes the following denitions:
Agent: “X is an agent if and only if X can instantiate intentional mental states capable of
performing actions” [77, p. 21].
Moral agency: “For all X, X is a moral agent if and only if X is (1) an agent having the
capacities for (2) making free choices, (3) deliberating about what one ought to do, and
(4) understanding and applying moral rules correctly in the paradigm cases” [77, p. 24].
With regards to articial agents, Himma postulates that the existence of natural agents can be
explained by biological analysis, while articial agents are created by “intentional agents out of
pre-existing materials” [77, p. 24]. He emphasizes that natural and articial agents are not mutu-
ally exclusive (e.g., a clone of a living being). He further claims that moral agents need to have
conscious and intentionality, something that state-of-the-art systems do not seem to instantiate.
It is worth noting that Himma attempts to provide a general denition of moral agency, while,
for example, Floridi and Sanders [57] propose to change the current description of a moral agent.
For example, they proposed description includes the separation the technical concepts of moral
responsibility and moral accountability, a distinction that was not evident thus far: “An agent is
morally accountable for x if the agent is the source of x and x is morally qualiable [...] To be also
morally responsible for x, the agent needs to show the right intentional states.” Wallach and Allen
[142] rate AMAs along two dimensions: how sensitive systems are to moral considerations and
how autonomous they are. Sullins [131] has a partially overlapping concept of requirements for
robotic moral agency with Himma’s that intersects with Wallach and Allen’s relevant concepts:
autonomy (i.e., “the capacity for self-government”) [30]), intentionality (i.e., “the directedness or
‘aboutness’ of many, if not all, conscious states”[30]), and responsibility (i.e., “those things for
which people are accountable”[30]).
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Implementations in Machine Ethics: A Survey 132:5
These are just some notions of how concepts such as agency, autonomy, intentionality, account-
ability, and responsibility are important to the eld of machine ethics. However, it is challenging
to summarize and dene these concepts concisely while doing justice to the work in philosophy
and computer science that has been done so far, including the discussions and controversy around
dierent relevant concepts (as the dierent concepts of moral agency display). The goal of this
survey is not to give an introduction to moral philosophy, so this section merely gives a glimpse of
the depths of the topic. Rather, the goal is to summarize and analyze the current state of the eld
of machine ethics. To avoid any assumption on concepts, the popular term Autonomous Moral
Agent is not used in this survey: As shown above, the term “agent” can be debated in this context
and the term “autonomous” has various meanings in the dierent surveyed systems. Instead, a
machine that has some form of ethical theory implemented—implicitly or explicitly—in it is re-
ferred to as an “ethical machine” throughout this article. Accordingly, we refrained from adding
an analysis regarding degree of agency and autonomy of machines into our taxonomy, as those
points are rarely discussed by the authors themselves and because they would have added a layer
of complexity that would have made our taxonomy confusing.
This section describes the search strategy, paper selection criteria, and review process used for this
3.1 Search Strategy
A literature review was conducted to create an overview of the dierent implementations of and
approaches to machine ethics. The search of relevant papers was conducted in two phases: auto-
mated search and manual search.
Automated Search. The rst phase used a search entry that reected dierent terms related to
machine ethics combined with the word “implementation”:
implementation AND (“machine ethics” OR “articial morality” OR “machine
morality” OR “computational ethics” OR “roboethics” OR “robot ethics” OR “ar-
ticial moral agents”)
These terms were cumulated during the search process (e.g., Reference [144, p. 455]); each added
term resulted in a new search until no new terms emerged.3No time period of publication was
specied, to include as many items as possible.
The following library databases were consulted (with the number of results in parenthesis): Web
of Science (18), Scopus (237), ACM Digital Library (16), Wiley Online Library (23), ScienceDirect
(48), AAAI Publications (4), Springer Link (247), and IEEE Xplore (113). Of these initial results,
37 items were selected based on the selection criteria listed in Section 3.2.
Manual Search. The second phase included checking the related work and other work by the
same rst authors of phase one. Twenty-nine promising results from phase one did not meet all
criteria but were included in the second search phase to see if related publications did meet all
criteria. This process was repeated for each newly found paper until no more papers could be
added that t the selection criteria (see Section 3.2). This resulted in a total of 49 papers, describing
48 ethical machines.
3The term “Friendly AI,” coined by Yampolsky [154], is excluded, since it describes theoretical approaches to machine
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3.2 Selection Criteria
After the selection process, two more coauthors judged which papers should be in- or excluded
to verify the selection. Papers were included only if they adhered to all of the following inclusion
criteria. The article
implements a system OR describes a system in sucient (high-level) detail for implemen-
tation OR implements/describes a language to implement ethical cases,
describes a system that is explicitly sensitive to ethical variables (as described in Refer-
ence [98]), no matter whether it achieves this sensitivity through top-down rule-based ap-
proaches or bottom-up data-driven approaches (as described in Reference [2]),
is published as a conference paper, workshop paper, journal article, book chapter, or tech-
nical report,
and has ethical behavior as the main focus.
The following exclusion criteria were used. The article
describes machine ethics in a purely theoretical fashion,
describes a model of (human) moral decision making without an implementable model de-
lists results of human judgment on ethical decisions without using the data in an imple-
is published as a complete book, presentation slides, editorial, thesis, or has not been pub-
describes a particular system in less detail than other available publications,
focuses on unethical behavior to explore ethics (e.g., a lying program),
mentions ethical considerations while implementing a machine, but does not focus on the
ethical component and does not explain it in enough detail to be the main focus,
simulates articial agents to see how ethics emerge (e.g., by using an evolutionary algorithm
without any validation),
and describes a general proposal of an ethical machine without mentioning implementation
related details.
Given the focus on algorithms implementing moral decision making and the limitations of space,
we will not go into further detail as regards recent interesting work on AI and moral psychology
(cf. e.g., References [18,31,88,123]).
3.3 Taxonomy Creation and Review Process
To be able to identify strengths and weaknesses of the state of the art, we created dierent tax-
onomies and classied the selected papers accordingly. It was clear that both a dimension referring
to the implementation object (the ethical theory; cf. Table 1) and a dimension regarding the tech-
nical aspects of implementing those theories (cf. Table 4) were necessary. All authors agreed that
there were some aspects of implementing those theories that did not concern purely technical is-
sues but were still important for the classication. Hence, we dened and applied a third taxonomy
dimension (cf. Table 3) related to non-technical implementation choices. The rst version of these
three taxonomies was created using knowledge obtained during the paper selection.
Before the classication process started, one-third of the papers were randomly selected to re-
view the applicability of the taxonomy proposed and adjust the assessment scheme where required.
Any parts of the taxonomies that was unclear and led to inconsistent classications was adapted
and claried.
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Each selected paper was categorized according to the features of the three dierent taxonomy
dimensions (discussed in Sections 46). Since the ethical classication is perhaps the most dis-
putable, it was determined by three distinct assessors: two philosophers and a computer scien-
tist. Two computer scientists evaluated the implementation and technical details of all proposed
To provide a classication for all selected papers, multiple classication rounds took place for
each dimension. Between classication rounds, disagreements across reviewers were discussed un-
til a consensus was reached. In the case of the ethical dimensions, four papers could not be agreed
upon after multiple classication rounds. As such, these papers were labeled as “Ambiguous.”
Additionally, we shared a pre-print of the article with the authors of the classied systems to
verify that they agreed with the classications we provided. From 45 targeted authors, we received
18 responses. From these 18, 6 authors agreed with our classication and 12 proposed (mostly
minor) changes or additional citations. In total, we changed the classication of 6 features for
4 papers.
In the following, we now outline the three dimensions of our taxonomy.
This section introduces the rst of three taxonomy dimensions introduced in this article: a taxon-
omy of types of ethical theories, which is the basis for the categorization of ethical frameworks
used by machines (in Section 7). Note that this section is not a general introduction to (meta-)ethics,
which can for example be found in References [29,44,97].
4.1 Overview of Ethical Theory Types
It is commonplace to dierentiate between three distinct overarching approaches to ethics: con-
sequentialism,deontological ethics,andvirtue ethics. Consequentialists dene an action as morally
good if it maximizes well-being or utility. Deontologists dene an action as morally good if it is in
line with certain applicable moral rules or duties. Virtue ethicists dene an action as morally good
if, in acting in a particular way, the agent manifests moral virtues. Consider an example: An elderly
gentleman is harassed by a group of cocky teenagers on the subway and a resolute woman comes to
his aid. The consequentialist will explain her action as good, since the woman maximized the over-
all well-being of all parties involved—the elderly gentleman is spared pain and humiliation, which
outweighs the teenagers’ amusement. The deontologist will consider her action commendable as
it is in accordance with the rule (or duty) to help those in distress. The virtue ethicist, instead, will
deem her action morally appropriate, since it instantiates the virtues of benevolence and courage.
Consequentialist theories can be divided into two main schools: According to act utilitarian-
ism, the principle of utility (maximize overall well-being) must be applied to each individual act.
Rule utilitarians, by contrast, advocate the adoption of those and only those moral rules that will
maximize well-being. Cases can thus arise where an individual action does not itself maximize
well-being yet is consistent with an overarching well-being maximizing rule. While act utilitari-
ans would consider this action morally bad, rule utilitarians would consider it good.
Deontological ethics can be divided into agent-centered and patient-centered approaches.
Agent-centred theories focus on agent-relative duties, such as, for instance, the kinds of duties some-
one has toward their parents (rather than parents in general). Theories of this sort contrast with
patient-centered theories that focus on the rights of patients (or potential victims), such as the right,
postulated by Kant, not to be used as a means to an end by someone else [84].
Finally, there are some approaches that question the universal applicability of general ethical
principles to all situations, as put forward by deontological ethics, virtue ethics, or consequential-
ism. For such a particularist view, moral rules or maxims are simply vague rules of thumb, which
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Table 1. Ethical Theory Types Taxonomy
Ethics Type
Deontological ethics
Virtue ethics
— Hierarchically specic
— Hierarchically nonspecic
Congurable ethics
cannot do justice to the complexity of the myriad of real-life situations in which moral agents
might nd themselves. Hence, they have to be evaluated on a case-by-case basis.
We highlight that a moral theory is a set of substantial moral principles that determine
what, according to the theory, is morally right and wrong. Moral theories can take dierent
structures—they might state their concrete demands in terms of hard rules (deontological ethics);
virtues that should guide actions, with reference to an overall principle of utility maximization, or
else reject the proposal that there is a one-size-ts-all solution (itself a structural trait, this would
be particularism). In this work, we are interested in these structures, which we label “ethical
theory types.”
4.2 Categorizing Ethical Machines by Ethical Theory Type
Based on the distinct types of ethical theories introduced above, this sub-section develops a simple
typology of ethical machines, summarized in Table 1.
An evaluation of existing approaches to moral decision making in machines can make use of
this typology in the following way. Deontological ethics is rule based. What matters is that the
agent acts in accordance with established moral rules and/or does not violate the rights of others
(whose protection is codied by specied rules). Accidents occur, and a well-disposed agent might
nonetheless bring about a harmful outcome. On o-the-shelf deontological views, bad outcomes
(if non-negligently, or at least unintentionally, brought about) play no role in moral evaluation,
whereas the agent’s mental states (their intentions and beliefs) are important. If John, intending
to deceive Sally about the shortest way to work, tells the truth (perhaps because he himself is
poorly informed), then a Kantian will consider his action morally wrong, despite its positive con-
sequence.4In the context of machine ethics, the focus is solely on agent relative duties. Hence, no
distinction is made between agent-centered and patient-centered theories of deontological ethics
in the taxonomy summarized in Table 1.
Consequentialists, by contrast, largely disregard the agent’s mental states and focus principally
on outcomes: What matters is the maximization of overall well-being. Note that, procedurally,
a rule-utilitarian system can appear very similar to a deontological one. The agent must act in
keeping with a set of rules (potentially the very same as in a Kantian system) that, in the long run,
maximizes well-being. However, the two types of systems can still be distinguished in terms of the
ultimate source of normativity (well-being vs. good will) and will—standardly—dier in terms of
the importance accorded to the agent’s mental states. Thus far, nearly all consequentialist machine
4Note that if two actions dier only with respect to outcome, then consequences can play a role.
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Table 2. High-level Overview to Ethics Categories in the Context of Ethical Machine Implementation
Input Decision criteria Mechanism Challenges (examples)
Deontological ethics Action (mental states Rules/duties Fittingness with rule Conicting rules
and consequences) Imprecise rules
Consequentialism Action (consequences) Comparative
Maximization of utility Aggregation problems
Determining utility
Virtue ethics Properties of agent Virtues Instantiation of virtue(s) Conicting virtues
Concretion of virtues
Particularism Situation (context,
features, intentions,
Rules of thumb,
precedent, all
situations are unique
Fittingness with
No unique and universal
Each situation needs
unique assessment
ethics implementations utilize act utilitarianism. For this reason, the distinction between act and
rule utilitarianism is not relevant enough to be included in this survey.
Virtue ethics diers from the aforementioned systems insofar as it does not focus principally on
(the consequences or rule-consistency of) actions but on agents and more particularly on whether
they exhibit good moral character or virtuous dispositions. A good action is one that is consistent
with the kinds of moral dispositions a virtuous person would have.
In contrast to the other three major approaches, on the particularist view, there is no unique
source of normative value, nor is there a single, universally applicable procedure for moral as-
sessment. Rules or precedents can guide our evaluative practices. However, they are deemed too
crude to do justice to many individual situations. Thus, according to particularism, whether a cer-
tain feature is morally relevant or not in a new situation—and if so, what exact role it is playing
there—will be sensitive to other features of the situation.
Table 2gives a schematic overview of key characteristics of the dierent types of ethical systems
that might be implemented in an ethical machine. Note that it does not take some of the more ne-
grained aspects dierentiating the theories (e.g., the before-mentioned complications regarding act
and rule utilitarianism) into account.
As an alternative to implementing a single determinate type of ethics, systems can also combine
two or more types, resulting in a hybrid ethical machine. This approach seems enticing when one
theory alleviates problems another one might have in certain situations, but it can also generate
conicts across types of ethical approaches. Hence, some proposals enforce a specied hierarchy,
which means that one theory is dominant over the other(s) in the system. For example, a primarily
deontological system might use rules but turn to the utilitarian approach of maximizing utility
when the rules are in conict. In other cases, the hierarchy is non-specic and dierent theories
are present without a specied dominant theory.
Some authors do not settle on a particular type of ethical theory. Instead, they provide a cong-
urable technical framework or language and exhibit how dierent types of ethical theories can be
implemented. The choice of which theory type should be selected is essentially left to the person
implementing the system in an actual use case.
Finally, some contributions were classied as ambiguous from a meta-ethical perspective. For
these, not enough details were given by the authors to classify a paper or the theories used to
implement were not ethical theories but retrieved from domains other than moral philosophy.
4.3 Ethical Theory Types in Practice
There are certain challenges inherent in the dierent types of ethics when they need to be applied
in practice. Since these obstacles need to be taken into account to select an ethical theory type for
an ethical machine, this subsection provides a (non-exhaustive) list of complications.
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Challenges of deontological ethics in practice: At a rst glance, the rule-based nature of de-
ontological ethics seems to lend itself well for implementation. However, at dierent stages of
implementation, challenges arise. The rst issue is which rules should be implemented. Rules are
expected to be strictly followed, implying that for every exception, the rule must be amended,
resulting in an extremely long rule. Determining the right level of detail is important for the
success of an application: when the rules are not practical and at the right level of detail, they will
not be interpretable for the machine [9]. Second, there might be conicts between rules [32]—in
general or in specic situations. Whilst ordering or weighing the rules might address this issue
from an implementational perspective, determining an order of importance can be dicult. Also,
this assumes that all relevant rules are determined before they are used.
Challenges of consequentialist ethics in practice: There are three main categories of diculties for
consequentialist ethics. First, it is hard to identify consequences and determine the right level of
detail and aggregation in terms of time and size. Some outcomes might have resulted regardless
of the action theorized to have caused it. In real-life situations, all possible consequences are not
always that clear beforehand given the lack of epistemic transparency and causal interdependence.
A second issue is concerned with quantifying consequences. As consequentialism is about max-
imizing utility, the problem is how to dene utility. In simple scenarios like the Trolley problem,
utility is often dened as how many people survive or die. In the real world, more complex con-
cepts, such as happiness and well-being, are preferred to dene utility. There are measures avail-
able (e.g., QALY [68]), but using a dierent measure can give a dierent outcome. Even more so,
even if each consequence is assigned a utility, then it might still be inappropriate to simply aggre-
gate them (e.g., see Reference [86]).
Finally, there might be a signicant computational cost when computing utility [146] requiring
heuristics or approximations to derive a correct answer in time. This, in turn, requires a verication
of whether these results are still correct.
Challenges of virtue ethics in practice: Virtues are positive character traits, character traits that
should be manifested in morally good actions. Dening what “character” a machine has is trou-
bling, if a machine can be claimed to have a character at all. To judge whether a machine—or a
human for that matter—is virtuous is not possible by merely observing one action or a series of
actions that seem to imply that virtue; the reasons behind them need to be clear [128]. Perhaps
the best way to create a virtuous machine is to let a machine mimic the behavior of a virtuous
person. But how is a certain virtue measured, and who decides which virtues are more important
and how to pick the perfect role model? Coleman [42] even proposes dierent virtues that are
more desirable for machines rather than human virtues, implying merely mimicking a virtuous
person is not sucient.
To circumvent these challenges, machine ethics researchers have not used virtue ethics often,
as the alternatives might be more appealing. For example, Haber [69] states that virtue ethics
and principle-based ethics are complements and that for each trait there will be a principle that
expresses that trait and vice versa. While not everyone agrees with Haber, it is easier and more
detailed from a computational perspective to implement rules than generic virtues to adhere to.
Arkin [9] also concludes that principle-based and act-centric models allow for stricter ethical im-
plementations, which is desirable in machine ethics.
Challenges of particularism in practice: In particularism, the system needs to take the entire
context into account. This implies that it needs to either be trained for all possible cases, which is
not possible, or be able to extrapolate without using generalizations, which is highly challenging.
For each feature of the context, the system would have to recognize whether it is morally relevant
in the given case and how it will inuence the result. Case-based methods or instance-based
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Table 3. Non-technical Taxonomy Dimension
Feature Type Subtype
Approach Bottom-up
Diversity consideration Ye s
Contribution type Model representation
Model selection
Judgment provision
Action selection/execution
Evaluation Test Non-expert
Prove Model checker
Logical proof
Informal Example scenarios
Face validity
Domain specic Yes ( domain specied)
classications come closest to allowing an implementation of particularism. More recently,
some contributions are trying to approximate particularist ethics using neural networks (e.g.,
References [66,74]).
Challenges of hybrid approaches in practice: Each type of ethical theory raises its own set of com-
plications, but combining them introduces additional issues. First, when dierent types of ethical
theories are used in a non-hierarchical way, the interaction between them can be problematic: How
should the results from dierent ethical approaches be combined to guarantee morally appropriate
outcomes? What happens when the results of dierent implemented ethical theory types stand in
conict, and how should such conicts be resolved?
Second, when a hierarchical approach is employed, it is not evident when the system should
employ one theory rather than another. One standard approach resorts to the secondary set of
ethical principles when the rst does not deliver a verdict. While this alleviates some of the chal-
lenges of hybrid systems, it is still possible that the second ethical theory proposes something that
conicts with the rst ethical theory type.
The next section introduces the second dimension of ethical machines: the non-technical aspects
of implementing ethics into a machine.
The second taxonomy dimension that was created for this survey considers the nontechnical as-
pects of implementing the aforementioned ethical theories into machines. An important part of
creating an ethical system is to decide how to implement ethics. That entails dening whether
an implementation can follow dierent approaches, how to evaluate the system, and whether
or not domain specications need to be taken into account. Important features concerning the
implementation dimension are summarized in Table 3. Furthermore, this section highlights the
implementation challenges that the various ethical theories entail.
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5.1 Approaches
Dierent typologies have been proposed to determine how ethics types are implemented. The
most inuential and widely referenced scheme, also applied in this survey, stems from Allen, Smit,
and Wallach [2]. They distinguish three types of implementation approaches, namely top-down,
bottom-up, and hybrid.
Top-down approaches: Top-down approaches assume that humans have gathered sucient
knowledge on a specic topic; it is a matter of translating this knowledge into an im-
plementation. The ethical theory types described in Section 4are examples of normative
human knowledge that can be translated into usable mechanisms for machines. The sys-
tem acts in line with predetermined guidelines and its behavior is therefore predictable.
In AI, strategies using a top-down approach mostly make use of logical or case-based
reasoning. Given general domain knowledge, the system can reason about the situation
that is given as input. Usually, human knowledge is not specied in a very structured or
detailed way for concrete cases, so knowledge needs to be interpreted before it can be
used. This process presents the risk of losing or misrepresenting information. The posi-
tive aspect of this approach is that existing knowledge is applied and no new knowledge
needs to be generated.
Bottom-up approaches: A dierent method to implementing ethics is to assume the machine
can learn how to act if it receives as input enough correctly labeled data to learn from.
This approach, not just in machine ethics but in general, has gained popularity after the
surge of machine learning in AI and the recent success of neural networks. Technologies
such as articial neural networks, reinforcement learning, and evolutionary computing
fall under this trend. Increased computing power and amounts of data allow learning
systems to become more successful. However, data has to be labeled consistently and the
right data properties need to be described in a machine-processable way to obtain an
accurate training of machines. There is a risk that the machine learns the wrong rules or
cannot reliably extrapolate to cases that were not reected in its training data. However,
for certain tasks, such as feature selection or classication, machine learning can be very
Hybrid approaches: As the term suggests, hybrid approaches combine top-down and
bottom-up approaches. As Allen et al. phrase it: “Both top-down and bottom-up ap-
proaches embody dierent aspects of what we commonly consider a sophisticated moral
sensibility” [2, p. 153]. They indicate that a hybrid approach is considered necessary, if a
single approach does not cover all requirements of machine ethics. The challenge consists
in appropriately combining features of top-down and bottom-up approaches.
Bonnemains et al. [32] suggest adding a fourth category, called “Personal values/ethics system.”
Essentially, it acknowledges that two dierent agents may rely on dierent ethical systems or may
rely on dierent precedence in case of conicts in a hybrid system. In this survey, this is regarded
as diversity consideration: The authors of a machine ethics paper consider the possibility that not all
ethical machines adhere to the same ethical theory type, and their contribution includes the choice
of diverse types of ethics to be implemented. As Bonnemains et al. recognize, this category is some-
what orthogonal to the previous three, as all of those can be seen to implement distinct normative
principles. For example, a machine ethics implementation with diversity consideration could allow
for multiple ethical theory types to be implemented (i.e., a top-down approach) or allow for dif-
ferent machines to learn dierent types of ethics (i.e., a bottom-up approach). It is considered part
of the implementation dimension rather than the ethics dimension, since diversity considerations
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can also exist within the same ethical theory, for example, by allowing deontological machines to
have dierent rules to adhere to while still all being deontological in nature. This survey regards
structures of normative frameworks and their implementation rather than substantial normative
principles (cf. Table 3).
5.2 Type of Contribution
Ethical systems can be intended to enact dierent aspects of ethical behavior. This section discusses
the dierent types of contributions published to implement ethical machines.
Model representation: This contribution type focuses on representing current ethical
knowledge. The goal is to determine how to appropriately represent a theory, dilemma,
or expert-generated guidelines whilst staying true to the original theory.
Model selection: Given a set of alternative options to implement an ethical machine, some
systems limit their action to selecting the most tting elements to be included in the
Judgment provision: These contributions focus on judging an action given a scenario and
a set of possible actions. Example outputs are binary (acceptable/non-acceptable)orre-
sponses on a scale (e.g., very ethical to very unethical).
Action selection/execution: Here the proposed system chooses which action is best given
multiple possible actions for a scenario. Some systems then assign the action to a human,
while others carry out the selected action themselves. Part of the action selection task
can also be action restriction, when some possible actions are not morally acceptable
5.3 Evaluation
Most artifacts—simple or complex, concrete or abstract—can be evaluated in virtue of their ca-
pacity to fulll their constitutive function or purpose. A good knife cuts well, a good thermostat
reliably activates the heating if the temperature drops below a predetermined threshold, and a
good translation system adequately and idiomatically converts grammatical sentences from one
language into another. Whereas there are objective and measurable criteria for the evaluation of
thermostats, things are more cumbersome when it comes to moral machines. This is not because
their purpose does not standardly consist in simply “acting morally” but in executing certain tasks
(taking care of the elderly, counselling suicidal people, evaluating risk of recidivism, etc.) in a moral
fashion. Much rather, the complication arises from the question of what exactly is to count as ex-
ecuting the task at hand in morally appropriate ways, or against what exactly the behavior of the
system should be evaluated.
There are objective facts as to whether an image represents a certain type of animal or not.
These facts constrain whether the image is correctly classied as representing an animal. The ex-
istence of objective, universal moral values, by contrast, is controversial (cf. e.g., References [75,
91,110,138]). Furthermore, and as objectivists readily acknowledge, delineating what is morally
permissible poses an epistemic challenge of a dierent order than identifying, say, a girae in an
image, or determining the weight of an object. The ontological and epistemic complications that
arise in the moral domain thus make it dicult to settle on standards against which the perfor-
mance of a moral machine could be evaluated. More fundamentally, it is not even evident what
kinds of considerations should guide the process of choosing such standards.
While complications as to the evaluation of a moral machine are worrying, their practical sig-
nicance should not be exaggerated. Although there is disagreement as regards complex cases, in
ordinary life situations in which one is confronted with extremely dicult ethical decisions or run
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away trolleys are exceedingly rare. In many domains, moral dilemmas are unlikely to arise or be of
much import, and there is widespread convergence (not only among the folk, but experts, too) on
what constitutes adequate moral behavior. Overall, then, the challenge of evaluation might raise
metaphysical and epistemic complications of limited pragmatic importance, at least when care is
exercised to limit the decision capacity of moral machines to mundane contexts that steer clear of
complex ethical paradoxes.
5.3.1 Test. When a system is tested, the system outcome needs to be compared against a ground
truth. These may have the following origins:
Non-experts: One possibility consists in making folk morality the benchmark. Prob-
lematically, there is substantial evidence of moral parochialism across cultures (e.g.,
References [56,90,118]), and it is not dicult to nd topics on which a single nation
is roughly divided—just think of abortion, euthanasia, or same-sex relations in the U.S.
[117]. Furthermore, the existence of widespread convergence in moral opinion does not
necessarily make such opinions true or acceptable (consider that until a century and a
half ago, there was broad agreement in considerable parts of the world that slavery is
morally acceptable).
Experts: To escape the tyranny of a potentially mistaken or self-serving majority, one might
adopt the standard of experts in normative ethics. Problematically, however, experts
themselves are sometimes deeply divided on fundamental issues of moral import as well
as meta-ethical intuitions [34], and their very expertise can be called into question [124,
Laws: One might side-step the complications raised by retreating to a second-best solution:
the law. This strategy, however, is not without drawbacks either, as the law is simply
silent on most questions of day-to-day morality. It is, for instance, not illegal to lie in most
contexts, yet it would be regarded as outrageous to be perpetually deceived by “moral”
machines. Still, it might be suitable to draw on the law to provide restrictions where
they exist, for example, as concerns the “Laws of War” or “Laws of Armed Conicts”
for the lethal weapons domain [9], or specic domain rules such as the Code of Ethics
for Engineers [94]. As Arkin [9] suggests, scoping the problem using domain-specic
requirements can make it more easily implementable and testable.
5.3.2 Prove. Another approach, typically based on some type of logic, consists of proving that
the system behaves correctly according to some known specications. This approach can be di-
vided into the following types:
Model checker: Given an ethical machine, a model checker exhaustively and automatically
ascertains that it adheres to a given set of specications.
Logical proof: This approach provides a logical proof that given certain premises, the system
does what it should do. Proofs of this sort can be eected manually or by using a theorem
prover that employs automated logical and mathematical reasoning.
Note that this approach assumes that a correct specication exists aprioriandiswidelyac-
cepted. Within the logic community, model checking and theorem proving are often considered
an implementation issue rather than a type of evaluation (e.g., see Reference [71]). In some cases,
authors do not even explicitly mention that they employ a model checker, because it is inherent
in their approach to logic programming. However, given the multidisciplinary nature of the eld
of machine ethics, it is vital to explicitly state which approach has been used. Furthermore, while
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logical/internal validity and consistency may be inherent in the system, a form of evaluation is
necessary to ensure the system acts as expected in dierent cases and exhibits external validity.
5.3.3 Informal Evaluation. Some authors refrain from formally evaluating their implementa-
tion. Instead, they only describe their work and, in some cases, show a few example scenarios or
exhibit application domains. Whilst these approaches may have limited validity, they may be war-
ranted given the evaluation complications outlined above or when the authors principally engage
in theory building [53].
Example scenarios/case studies: To showcase that the system works as intended, one or
multiple scenarios are presented to demonstrate the system’s performance. This proce-
dure gives a rst indication of the functionalities of the machine or may help in theorizing
about certain properties of a system, but it does not cover all possible situations or give a
complete performance indication.
Face validity: Often described as “reasonable results,” authors using this approach state that
the results of a few example tasks are as expected. It is often unclear what this means and
to what extent these results are desirable.
5.3.4 None. When no evaluation could be discerned, papers were categorized as having none
of the evaluation types present.
5.4 Domain Specificity
What is deemed an appropriate action can depend on the domain in which the moral agent is
operating, such as the principles in the domain of biomedical ethics as proposed by Beauchamps
and Childress [22] for the medical domain, or the Rules of Engagement and Laws of Armed Conict
for autonomous weapon systems [9]. Hence, some contributions focus on a specic application
domain, which limits the scope of an ethical machine implementation, and thus the endeavor is
more manageable [9].
The third and nal taxonomy dimension introduced concerns the technical aspects when imple-
menting ethical theories into machines. This includes the type of technology chosen for the imple-
mentation, the input the system relies on, the ethical machine’s availability (i.e., implementation
details are published), and other technical features: whether it relies on specic hardware or feed-
back from users, provides explanations for its conclusions, has a user interface (UI), and whether
the input for the system needs to be preprocessed. Important features pertaining to the technical
dimension are surveyed in Table 4.
6.1 Types of Technology
Inspired by Russell and Norvig [115], dierent types of technologies can be distinguished. While
these types of technology are not always clearly delimited, this categorization allows comparing
6.1.1 Logical Reasoning. There are dierent types of logic or logic-based techniques used in
machine ethics.
Deductive logic: This is the classical type of logic: Knowledge is represented as logical
statements—propositions and rules—that allow deriving new propositions. Pure deduc-
tive systems typically involve no learning or inference involved but only derive what can
be known from their set of statements and inputs.
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Table 4. Technical Taxonomy Dimension
Feature Type Subtype or classication scheme
Tec h typ e
Logical reasoning Deductive logic
Non-monotonic logic
Abductive logic
Deontic logic
Rule-based system
Event calculus
Knowledge representation & Ontologies
Inductive logic
Probabilistic reasoning Bayesian approach
Markov models
Statistical inference
Learning Inductive logic
Decision tree
Reinforcement learning
Neural networks
Evolutionary computing
Case-based reasoning
Input Case Logical representation
Numerical representation
(Structured) language representation
Sensor data
Implementation availability Specication details Y - P - N (Yes - Partially - No)
Implementation details Y - P - N
Code (link) provided Y - P - N
Other Hardware (simulation) Y - P - N
Feedback Y - P - N
Explanation Y - P - N
UI(mostlyGUI) Y-P-N
Automated processing Y - P - N
As explained in Section 6, “Inductive logic” is present twice.
Non-monotonic logic: Non-monotonic logic allows the revision of conclusions when a con-
ict arises, for example, in light of new information.
Abductive logic: In abductive logic, the conclusions drawn are the most likely propositions
given the premises.
Deontic logic: This type of logic stems from philosophy and is specically designed to ex-
press normative propositions. Naturally, this type of logic is inherently suited for the
representation and deduction of moral propositions.
Rule-based systems: As the name suggests, rule-based systems are systems that function
based on a set of rules. These can be ethical rules the system has to adhere to. Note that
many of the dierent types of logic above are typically implemented as some form of
rule-based system.
Event calculus: Event calculus allows reasoning about events. When a machine needs to act
ethically, dierent events can trigger dierent types of behavior.
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Knowledge representation (KR) and ontologies: A KR approach focuses on representing
knowledge in a form that a computer system can utilize. In other words, the emphasis
lies on improving the quality of the data rather than (just) improving the algorithm.
Inductive logic: When relying on inductive logic, premises are induced or learned from ex-
amples, rather than pre-dened by a human.
6.1.2 Probabilistic Reasoning. Recently, probabilistic reasoning has gained more attention. Dif-
ferent types of probabilistic reasoning approaches can be distinguished.
Bayesian approaches: Based on Bayes’s rule, these approaches rely on prior knowledge to
compute the likelihood of an event. In an ethical context, a machine can then act based
on this predicted information.
Markov models: Markov models focus on sequences of randomly changing events, assuming
that a future event only depends on the current (and not the previous) event(s).
Statistical inference: By retrieving probability distributions from available data, the system
can try to predict the chances of future events happening.
6.1.3 Learning. The increased computational power, the amounts of data available, and the
GPU-driven revival of neural networks have made learning systems more popular. There are dif-
ferent learning approaches to be characterized.
Inductive logic: In inductive logic, a rule-base for reasoning is learned. As such, it is listed
under both the “Logic” and “Learning” categories of this taxonomy.
Decision tree: Decision trees are a supervised learning method to solve a classication prob-
lem by exploring the decision space as a search tree and computing the expected utility.
They are, thus, useful to identify and interpret the features that are most important to
classify cases.
Reinforcement learning: A system can learn from its actions when they are reinforced with
rewards or punishments received from its environment.
Neural networks: A neural network can be trained on many cases, to be able to classify new
cases based on their relevant features.
Evolutionary computing: Evolutionary algorithms are used when, for example, dierent
competing models of an ethical machine exist. Models evolve in an iterative fashion, based
on actions inspired from the concept of evolution in the eld of biology (e.g., selection,
and mutation) [82].
6.1.4 Optimization. The most common form of optimization relies on a closed-form formula for
which some optimal parameters are sought. Dierent actions get assigned dierent values based
on a predetermined formula, and the best value is chosen (e.g., the highest value).
6.1.5 Case-based Reasoning. In case-based reasoning, a new situation is assessed based on a
collection of prior cases. Similar cases are identied and their conclusions are transferred to apply
to the current situation.
6.2 Input
To be able to respond appropriately, ethical machines need to receive information about the envi-
ronment (or situation at hand). Input is the information that the system receives, not the transfor-
mation the system itself performs on the data afterward.
Sensor data: In the case of (simulated) hardware, the machine perceives the input through
its sensors. The sensor data are interpreted and processed to serve as the input.
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Case: Logical representation: Systems using a form of logic often need an input case rep-
resented using logic.
Case: Numerical representation: Other systems, for example ones using neural nets, need
their input in a numerical form. This can be a vector representation or a set of numbers.
Case: Language representation: Language inputs can be natural language or input trans-
lated into structured language.
6.3 Implementation Availability
As mentioned before, part of the eld of machine ethics tends to be of a theoretical nature. This
becomes apparent in the level of detail of implementation proposals. While some authors imple-
ment an idea and provide the source code, this is fairly rare in machine ethics. Some authors only
give a few implementation details, and others merely specify a high-level description of their idea.
Usually, the focus lies on sketching an idea rather than its complete implementation.
Specication details: This level has the fewest implementation details: The author species
the proposed idea (e.g., textually) without any additional detail.
Implementation details: This next level provides implementation details illustrating how
the specication is implemented in the described machine.
Code (link) provided: This nal level provides the link to the code of the machine, so the
prototype can be used and the experiments can be replicated.
6.4 Other Implementation Categories
This section introduces dierent and independent categories that are of interest for the implemen-
tation of an ethical machine.
6.4.1 Hardware. Robots can have direct physical results rather than “just” digital or indirect
physical consequences. Hardware can change the way people interact with a system and how it
should be able to function, making it an interesting and important feature to classify.
6.4.2 Feedback. No matter which ethical approach is used, feedback is a valuable component
of an ethical system. For example, the user can be asked whether the provided output was the best
given the input or whether the system was clear during its decision process.
6.4.3 Explanation. Transparency is important when it comes to algorithmic decisions, both
from a user perspective [155] and, in some cases (such as the General Data Protection Regulation
in the European Union [64]), from a legal perspective. To achieve this goal, an understandable
explanation should be provided by the system.
6.4.4 User Interface. Systems should be easy to interact with. This is important for all machines,
including ethical machines.
6.4.5 Automated Processing. Sometimes, initial prototypes focus on the concept of a system,
not the (detailed) implementation, and may require some pre-processing of the input data. Ideally,
systems should be able to process input from the environment automatically.
The goal of this section is to classify the surveyed moral machines according to the three tax-
onomy dimensions introduced in Sections 46and elicit patterns in the literature based on this
classication. Specically, every publication is categorized according to the object of implemen-
tation (ethical theory) as well as the non-technical and technical aspects of implementing those
theories (as described in Section 3). Summaries of the selected papers can be found in Appendix A.
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Table 5. Ethical Theory Classification
Ethical theory type Papers
Deontological (D)
Anderson et al. 2004 (W.D.) [6], Anderson et al. 2006 [7], Anderson et al.
2008 [3], Anderson et al. 2014 [5], Bringsjord et al. 2012 [35], Dennis et al.
2016 [51], Malle et al. 2017 [93], McLaren 2003 [94], Mermet et al. 2016
[95], Neto et al. 2011 [100], Noothigattu et al. 2018 [101] Reed et al. 2016
[113], Shim et al. 2017 [126], Turilli 2007 [134], Wiegel et al. 2009 [146]
Consequentialist (C)
Abel et al. 2016 [1], Anderson et al. 2004 (Jeremy) [6], Armstrong 2015
[14], Cloos 2005 [40], Dennis et al. 2015 [52], Dang et al. 2017 [135],
Vanderselst et al. 2018 [137], Wineld et al. 2014 [147], Atkinson et al.
2008 [17]
Particularism (P) Ashley et al. 1994 [15], Guarini 2006 [66]
Hybrid dominance D-C
Arkin 2007 [9], Azad-Manjiri 2014 [19], Dehghani et al. 2008 [50],
Govindarajulu et al. 2017 [65], Pereira et al. 2007 [102], Tus et al. 2015
Hybrid dominance C-D Pontier et al. 2012 [108]
Hybrid undened dominance
Lindner et al. 2017 [89](C & A), Yilmaz et al. 2017 [152](D, C & A),
Honarvar et al. 2009 [78](C & P), Howard et al. 2017 [82](P & Virtue
ethics), Berreby et al. 2017 [28](D & C)
Congurable ethics Bonnemains et al. 2018 [32], Cointe et al. 2016 [41], Ganascia 2007 [62],
Thornton et al. 2017 [132]
Ambiguous (A)
Han et al. 2012 [72], Cervantes et al. 2016 [38], Madl et al. 2015 [92],
Verheij et al. 2016 [139], Wallach et al. 2010 [144], Wu et al. 2017 [151],
Arkoudas et al. 2005 [13], Furbach et al. 2014 [60]
Hybrid dominance D-C implies both D and C are implemented, but D is dominant. The reverse is true for Hybrid dominance
C-D.FortheHybrid undened dominance the theories that are combined are noted in parentheses following the citation.
Fig. 1. Ethical theory type ratio.
7.1 Ethical Classification
The classication results for the ethical dimension of machine ethics implementations can be found
in Table 5; the ratio of single vs. hybrid theory papers is visualized in Figure 1. Among the papers,
several constitute clear-cut cases instantiating one of the four main ethical systems. For example,
References [5,100,126] are clearly deontological, and References [1,40,52,137] constitute uncon-
troversial examples of consequentialist systems. Furthermore, a considerable number of papers
invoke elements from multiple systems. Finally, there are papers in which the hierarchy across
theory types remains ambiguous. Examples of ambiguous papers are implementations where au-
thors try to mimic the human brain [37], or focus on implementing constraints such as the Pareto
principle [89], which does not strictly speaking constitute a moral theory. Note that categorizing
a paper as “ambiguous” does not imply a negative assessment of the implementation. It simply
means that the proposal cannot be adequately placed within our classication framework.
About 50% of the proposals draw on a single type of ethical theory (see Figure 1). As can be
seen in Table 5, deontological and consequentialist ethics are used most often. It stands out that
particularism is barely used and pure virtue ethics is not used at all. This may be explained as
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132:20 S. Tolmeijer et al.
Fig. 2. Non-technical analysis.
follows: First, a generalist approach is much easier to implement than a particularist approach,
as it is more straightforward to encode generalist rules than to build systems that may have to
handle as of yet unknown, particular cases. Second, virtue ethics can be considered a very high-
level theory focusing on characteristics rather than actions or consequences, which is dicult to
interpret in an application context.
About a quarter of the approaches are of a hybrid nature, combining at least two classical ethical
theory types. Approximately half of those have a hierarchical approach, in which deontological
features are standardly dominant over consequentialist ones. The non-hierarchical systems, where
at least two ethical theory types work together without a single one being dominant, frequently
go beyond the two main types of theory. Examples are virtue ethics and particularism [82], and a
reective equilibrium approach that combines consequences, rules, and other inuences [152].
A little less than 10% of the papers do not have a specic theory implemented. Instead, they
provide various proposals on how to implement dierent ethical theory types without choosing a
particular one. This can be considered a computer scientist approach, where the goal is to devise
a general framework that the users can adapt to their preferences.
It is surprising that despite previous calls that a single classical theory is not enough to create
an ethical machine and hybrid methods are needed (e.g., Reference [2]), there is relatively little
work on hybrid ethical machines. While most hybrid systems have emerged over the past 10 to
15 years, we could not nd evidence for an increase in the creation of such systems.
7.2 Implementation Classification
Table 6provides an overview of the classication of the non-technical implementation.
Approximately consistent with the number of single theory and hybrid theory approaches iden-
tied in Section 8.1, most authors choose a top-down approach. Hybrid approaches account for a
little less than 25% of those chosen (see Figure 2(a)).
Most authors use a general approach to machine ethics: Almost three of four do not use a
domain-specic approach but focus on a general proposal of implementing machine ethics (see
Figure 2(b)).
In terms of contribution type, there is a relatively balanced division between authors inves-
tigating how an ethical machine should be shaped (model selection and model representation)
and authors focusing on the output of the ethical machine (action judgment and action
selection/execution, see Figure 2(c)). Most papers address action selection/execution. About 15%
of all the papers focus on action judgment: The system judges a situation but leaves it up to the
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Implementations in Machine Ethics: A Survey 132:21
Table 6. Non-technical Dimension Classification
Appr. Contribution type Eval. type Eval. subtype Diversity Domain Papers
Top-d ow n
Model representation Proof Model checker Ganascia 2007 [62]
Logical proof Arkoudas et al. 2005 [13]
Bringsjord et al. 2012 [35]
Govindarajulu et al. 2017 [65]
Informal Example scenario(s) Berreby et al. 2017 [28]
None None Bonnemains et al. 2018 [32]
Model selection Informal Example scenario(s) Turilli 2007 [134]
Verheij et al. 2016 [139]
Wiegel et al. 2009 [146]
Judgment provision Test Expert Engineering McLaren 2003 [94]
Ashley et al. 1994 [15]
Expert + Non-expert Military Reed et al. 2016 [113]
Proof Model checker Dennis et al. 2015 [52]
Logical proof Mermet et al. 2016 [95]
None None Lindner et al. 2017 [89]
Action selection/ execution Test Non-expert Medical Shim et al. 2017 [126]
Dehghani et al. 2008 [50]
Laws Cars Thornton et al. 2016 [132]
Vanderelst et al. 2018 [137]
Wineld et al. 2014 [147]
Informal Example scenario(s) Cervantes et al. 2016 [38]
Medical Anderson et al. 2008 [3]
Cointe et al. 2016 [41]
Face validity Pereira et al. 2007 [102]
None None Neto et al. 2011 [100]
Home care Cloos 2005 [40]
Home care Dang et al. 2017 [135]
Anderson et al. 2004 (Jeremy) [6]
Judgment provision + Proof Model checker Dennis et al. 2016 [51]
action selection/execution
Model representation + Informal Face validity Atkinson et al. 2008 [17]
action selection/execution
Model representation Proof Logical proof Armstrong 2015 [14]
Furbach et al. 2014 [60]
Model selection None None Howard et al. 2017 [82]
Malle et al. 2017 [93]
Action selection/ execution Test Non-expert Wu et al. 2017 [151]
Informal Example scenario(s) Abel et al. 2016 [1]
Model representation + Test + proof Non-expert + logical proof Cars Noothigattu et al. 2018 [101]
action selection/execution
Model representation Test Non-expert Guarini 2006 [66]
Expert Anderson et al. 2014 [5]
None None Medical Azad-Manjiri 2014 [19]
Action selection/ execution Test Non-expert Honarvar et al. 2009 [78]
Expert Medical Anderson et al. 2006 [7]
Laws Medical Madl et al. 2015 [92]
Informal Example scenarios Yilmaz et al. 2017 [152]
Military Arkin 2007 [9]
Face validity Han et al. 2012 [72]
None None Anderson et al. 2004 (WD) [6]
Wallach et al. 2010 [144]
Model selection + Informal Example scenario(s) Tus et al. 2015 [133]
action selection/execution
Judgment provision + Test Expert Medical Pontier et al. 2012 [108]
action selection/execution
Diversity consideration: implies yes, an empty cell implies no/not present.
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132:22 S. Tolmeijer et al.
Fig. 3. Technology analysis.
human to actually act on this. From a broader scientic perspective, it is good that both model
shaping and output-oriented contributions are investigated. However, it would be ideal to have
both things connected.
A possibility for future improvement regards system evaluation: Over half of the authors either
provide no or only an informal evaluation of their system. Of the rest, about 50% use a test approach
and 50% validate their claims with some form of formal proof (see Figure 2(d)).
Finally, about half of the selected papers (51%) acknowledge diversity in implementable ethics,
while the other half presents work allowing for or assuming only one ethical theory type.
7.3 Technical Classification
The technical dimension classication can be found in Table 7. Of the dierent techniques, logical
reasoning is the most frequent. Figure 3(a) shows the distribution of types of technology used.
About a quarter of the papers adopt more than one technology type. Only about 10% of the authors
focused on a pure learning approach. Case-based reasoning and probabilistic reasoning are the
least popular. Mostly classical AI approaches are used—perhaps due to the direct correspondence
of rules with deontological ethics.
The level of implementation detail provided is somewhat limited (see Figure 3(b)): Although
most authors include a specication of their idea in the paper, implementation details (or even
source code) are rarely included. Both from a computer science perspective and a general science
perspective, this is quite undesirable, as it hampers the reproducibility and extensibility of systems
and empirical studies.
The dierent types of input used are fairly distributed: In about 36% of the ethical machines the
input is dened as logical cases, in 21% the input has a numerical representation, in 30% the input
is written in (natural or structured) language, and 34% use (simulated) sensor data as input. Of all
cases, ve selected papers had more than one type of input for their system. Around 25% of the
authors used a (simulated) robot, corresponding with the amount of sensor data used as input.
In terms of user friendliness, the implemented systems score poorly. While it is important to
note many of these machines are in their prototype phase and more focused on the ethics than
the user, it should be important to keep the user in mind from the start of development. Nearly
35% of the machines provide an explanation of their output; 27% process the input automatically,
implying that about three out of four implementations require the user to pre-process the input
manually in some way—which does not make it easy for the user. Only around one out of ve
machines include a user interface and less than 17% oer the option for the user to give feedback.
In summary, there is still plenty of room for improvement as regards user friendliness.
7.4 Interactions between Dimensions
Given that machine ethics is an interdisciplinary eld, it is interesting to look at the interaction be-
tween the ethical theory types and their implementation, Figure 4shows the interactions between
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Table 7. Technical Classification
Tech type Tech subtype Input Availability Other Papers
Case (logical rep)
Case (numerical rep)
Case ((struc) language rep)
(Simulated) Sensor data
Formalization details
Implementation details
Code (link) provided
Robot (Simulation)
Automated Processing
Logical reasoning (LR) Deductive logic ✓✓◦◦Bringsjord et al. 2012 [35]
Mermet et al. 2016 [95]
Verheij et al. 2016 [139]
Non-monotonic logic (N-M logic) ✓✓Ganascia 2007 [62]
Deontic logic (Deon Logic) ✓✓◦◦Arkoudas et al. 2005 [13]
✓✓ Furbach et al. 2014 [60]
◦◦ Malle et al. 2017 [93]
Wiegel et al. 2009 [146]
Rule-based system (Rules) ✓✓Dennis et al. 2015 [52]
Dennis et al. 2016 [51]
Neto et al. 2011 [100]
✓✓ Pontier et al. 2012 [108]
Tus et al. 2015 [133]
Turilli 2007 [134]
Event calculus ✓✓ Bonnemains et al. 2018 [32]
Abductive logic ✓✓Pereira et al. 2007 [102]
N-M logic + event calculus ✓✓◦◦Berreby et al. 2017 [28]
Rules + KR & ontologies ✓✓✓✓ Cointe et al. 2016 [41]
Deon logic + event calculus ✓✓Govindarajulu et al. 2017 [65]
Probabilistic reasoning (PR) Bayes’ Rule + Markov models ✓✓Cloos 2005 [40]
Learning (L) Reinforcement learning ✓✓ ✓ ✓ Abel et al. 2016 [1]
Wu et al. 2017 [151]
Neural networks Guarini 2006 [66]
✓✓✓ ✓ Honar var et al. 2009 [78]
NN + Evolutionary computing ◦◦Howard et al. 2017 [82]
Optimization (O) Optimization ◦◦ ✓✓ Anderson et al. 2004 (Jeremy) [6]
✓✓ ✓ Anderson et al. 2004 (WD) [6]
✓✓ ✓ Anderson et al. 2008 [3]
✓✓Thornton al. 2017 [132]
◦◦ Dang et al. 2017 [135]
✓✓ ✓ Vanderelst et al. 2018 [137]
Case-based reasoning Case-base d reasoning ✓✓✓ Atkinson et al. 2008 [17]
✓✓◦◦ Ashley et al. 1994 [15]
✓✓✓ ✓McLaren 2003 [94]
LR + L Inductive logic ✓✓ ✓✓ ✓✓ Anderson et al. 2014 [5]
KR & ontologies + inductive logic ◦◦ ✓✓ Anderson et al. 2006 [7]
LR + O Deductive logic + O ✓✓✓✓Yilmaz et al. 2017 [152]
Rules + O ✓✓✓✓✓✓Arkin 2007 [9]
✓✓✓ Cervantes et al. 2016 [38]
✓✓ Reed et al. 2016 [113]
✓✓Shim et al. 2017 [126]
✓✓Wineld et al. 2014 [147]
Rules + abductive logic + O ✓✓Han et al. 2012 [72]
LR + PR Rules + Bayes’ Rule ✓✓◦◦Lindner et al. 2017 [89]
Rules + statistical inference ◦◦ ✓✓Madl et al. 2015 [92]
✓✓ ✓Wallach et al. 2010 [144]
LR + CBR Rules + KR & ontology + CBR ◦◦ Dehghani et al. 2008 [50]
LR + L + O Rules + decision tree + O ✓✓ ✓Azad-Manjiri 2014 [19]
PR + O Bayes’ Rule + O ✓✓ Armstrong 2015 [14]
L + O Inductive logic + O ✓✓Noothigattu et al. 2018 [101]
implies yes/fully, implies partially, an empty cell implies no/not present.
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132:24 S. Tolmeijer et al.
Fig. 4. Dimension interaction.
ethical theory, ethical implementation approach, and technology type used to implement an eth-
ical machine. For researchers, it can be useful to see which combinations have not yet been tried
out that might be promising. For example, Figure 4(a) shows that (to the best of our knowledge) a
hybrid approach (including both top-down and bottom-up elements) to implementing pure conse-
quentialism does not yet exist. Similarly, bottom-up approaches to optimization (see Figure 4(b))
or pure deontological approaches to learning (see Figure 4(c)) (e.g., seeing which input leads to
behavior adherent to a certain set of rules) have not yet been explored.
7.5 General Observations
There are some general observations to be made about the eld. First, the focus is on one universal
and objective moral agent. There are barely any options for adding cultural inuences or societal
preferences in any of the classied papers. Almost all systems assume the user cannot inuence
the output of the system. A recent publication shows indication of cultural dierences in ethical
preferences [18], and the development of societal preferences within an ethical machine would
improve the chance of acceptance of ethical machines. However, it is still under debate whether
the eld should move toward a “universal moral grammar,” such as that proposed by Mikhail [96].
Second, there are some issues inherent to the eld. For instance, there are no benchmarks to
verify if a system is working as it should. There are no specic tasks to be implemented, no con-
sensus as to what the correct output is, and few data sets to use in an implementation. A helpful
tool to recur to in this context is the work by Whitley [145], who provides a four-dimensional
schema for analyzing a research eld. Two of the dimensions refer to the uncertainty of the task
at hand, and two refer to the mutual dependence between the elds and scientists in them. The
eld of machine ethics scores highly on all of these dimensions:
High technical task uncertainty: There is unpredictability and variability in which methods are
used in the eld and how results are interpreted. In this regard, it is a fragmented eld.
High strategic task uncertainty: There are problems present in the eld that are valued dier-
ently (e.g., some authors focus on the theoretical, others on the implementation, and the
ethical theories or even ethical theory types they focus on diverge).
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Implementations in Machine Ethics: A Survey 132:25
High strategic dependence: There is much disagreement on the relevant topics, so there is a
high reliance on peers for validation and reputation in the eld.
Medium functional dependence: In terms of physical dependence of resources, there is none.
Anyone with a computer can add to the eld; no expensive equipment is needed. However,
there is a high dependence on results of others and acceptance by the eld.
Another potentially helpful perspective can be derived from Whitley’s theory, where the eld of
machine ethics would be a “polycentric oligarchy,” implying there are several independent clus-
ters of scholars that conrm each other’s assumptions and do not communicate much with other
clusters that have very dierent views. At rst glance, such clusters can indeed be detected: the
multi-agent norm domain (e.g., References [100,133]), the logical translation of ethical theories
(e.g., References [65,72,102]), or the modern learning approach to machine ethics (e.g., Refer-
ences [1,151]). While exploratory research in many directions is valuable, the eld would benet
from more standardization and more communication between clusters to exchange knowledge on
ethics and technology.
Based on the results of the analysis and description of the selected papers, some literature gaps
are identied that can be of interest for future work. Additionally, the limitations of this survey
are discussed.
Ethical dimension. In view of earlier calls for hybrid systems when it comes to ethical theory,
a surprisingly low percentage of authors consider a multi-theory approach in which machines
can interchangeably apply dierent theories depending on the type of situation. In terms of the
content (and not the structure) of ethical theories, it is important to acknowledge and harness the
nuances of specic theories, but human morality is complex and cannot be captured by one single
classical ethical theory. Even experts can have rational disagreement amongst themselves on an
ethical dilemma. This leads to the next important point: An ethical machine will not be of use if
it is not accepted by its users, which can be the risk of focusing on one ethical theory and, thus,
not covering human morality. Ethical theory needs to be combined with domain-specic ethics as
accepted by domain experts and, as identied in the analysis of this article, this is not the case in
the majority of the related work. Moreover, it is necessary to discuss the ethical theory/theories
in the system with its possible users. Some examples of using folk morality in machine ethics can
be found in Noothigattu et al. [101], as well as in Reference [116]. However, it is important to note
that just as ethical theories have their challenges, so does folk morality. Three challenges are who
to include in the group whose values should be considered (standing), how to obtain their values
(measurement), and how to aggregate their values (aggregation)[21]. Implementations should start
from ethical theories combined with domain-specic ethical theory, after which acceptance by the
users and deviation from socially accepted norms should be discussed (cf. e.g., References [18,31,
Non-technical dimension. There is a need for more systematic evaluations when ethical machines
are created to be able to rate and compare systems. To this end, there is a strong need for domain-
specic benchmarks. Based on input from domain experts, data sets need to be created containing
the types of cases prevalent in that domain, with respect to which ethical machines must be as-
sessed. The gathering of typical tasks and respective answers that domain experts agree on is just
as important as the actual creation of ethical machines. This implies the need for more collabora-
tion between elds. Computer scientists and philosophers, as well as domain experts and social
science experts, have to work together to ensure the interaction with and eects of the ethical
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132:26 S. Tolmeijer et al.
machines are as desired. Even within the eld, collaboration is needed between dierent clusters
of topics in the eld of machine ethics, for example between clusters specializing in MAS and
machine learning, respectively. Finally, in general, implementation requires more attention. While
on a higher level, theoretical discussion remains important in this eld, especially to prepare for
possible future scenarios, the testing of theory in practice can enrich the discussion on what is
(or is not) possible at that moment and what practical implementations and consequences certain
ethical machines can have.
Technical dimension. When a system is implemented, it is imperative to provide exhaustive spec-
ication detail, including availability of the code, which is predominantly lacking. Another frequent
shortcoming regards usability: The system should have a user interface so that the future user can
interact with the system without having to know how to code. Furthermore, automatic processing
of input cases deserves more attention, so as to avoid having to encode each variable manually
as a vector for a neural network. Considering the increased need for transparency in algorithmic
decision making, as well as the fundamental role of reasons in ethics, the system should also pro-
vide an explanation of why it took a certain decision. In a next phase, the user should be able to
give feedback on the ethical decision the system makes. Finally, the association of a given type of
technology with a certain type of ethics requires an adequate technical justication, beyond using
just the most acquainted technology.
Further Points of Interest. Current technology allows for successful application of narrow AI
geared toward specic tasks. While steps are being taken toward articial general intelligence
(AGI), the technology does not yet exist [83]. Hence, domain-specic applications seem suitable.
A domain-specic non-AGI approach to machine ethics alleviates some of the risks and limitations
on machine ethics posed by [36], such as those related to an “insucient knowledge and/or com-
putational resources for the situation at hand.” However, there are still risks and limitations. For
instance, in the context of lethal autonomous weapons systems, the loss of “meaningful human
control” [119] is a risk, as humans would not have the same control over ethical decisions such as
target selection. A limitation of using domain-specic ethical machines is that the process of one
domain may not be transferable to other domains. Furthermore, not everyone is ready to accept a
machine taking over the ethical decision making process [76].
A slightly dierent way to address ethics in machines is to dene (and implement) an ethical
decision support, rather than leaving the machine to make an autonomous ethical decision. For an
overview of dierent types of moral mediation, see Van de Voort et al. [136]. Etzioni agrees that
the focus should lie on decision support, stating “there seem to be very strong reasons to treat
smart machines as partners, rather than as commanding a mind that allows them to function on
their own” [54, p. 412]. One of those reasons is that AGI will not exist in the foreseeable future.
This approach will also help with acceptance of machines with ethical considerations in society.
There are dierent possible levels of autonomy the system can have, for example only summarizing
available data, interpreting available data, summarizing possible actions, or even suggesting/pre-
selecting a possible action the system deems best. Dierent types of support and collaboration
might be necessary for dierent applications, and according to the literature review done in this
article, further research is needed in this direction.
Limitations. This survey has some limitations that need to be mentioned. First of all, the scope of
the paper selection was limited to explicit ethical theories (i.e., theories directly programmed into
the machine). While some of the works reviewed can still be of interest and provide inspiration
for implementation, papers devoid of implementation details were excluded from this survey. Ex-
amples are emerging ethics based on human data to research folk morality (e.g., Reference [151])
or models of human morality to determine relevant features in input cases (e.g., Reference [141]).
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Implementations in Machine Ethics: A Survey 132:27
Furthermore, we limited the survey to one paper per author whenever similar systems were dis-
cussed across multiple publications, selecting the most comprehensive one. This does not do full
justice to the work of certain authors (e.g., Guarini working on explainability of neural networks
making ethical decision [67]). While the paper selection procedure was designed to be as exhaus-
tive as possible, it is still possible that a few important papers were missed. Finally, three authors
reviewed the ethical dimension and two reviewed the implementation and technical dimension,
but it is still possible there was bias in the classication due to the limited number of people in-
volved in the classication process and the process of discussion until agreement was reached.
The future of the eld of machine ethics will depend on advances in both technology and ethical
theory. Until new breakthroughs change the eld, it is important to acknowledge what has been
done so far and the avenues of research that make sense to pursue in the near future. To accom-
plish this, the contribution of this survey is threefold. First, a classication taxonomy with three
dimensions is introduced: the ethical dimension, the dimension considering nontechnical aspects
when implementing ethics into a machine, and the technical dimension. Second, an exhaustive se-
lection of papers describing machine ethics implementations is presented, summarized, and clas-
sied according to the introduced taxonomies. Finally, based on the classication, a trend analysis
is presented that leads to some recommendations on future research foci. It is important to keep in
mind how machine ethics can be used in a meaningful way for its users, with increasing agreement
on what a system should do, and in what context.
For readers that are interested in a more detailed description of the classied papers, this appendix
provides a short summary of each of the selected papers. To structure their presentation, the pa-
pers were categorized across two orthogonal dimensions: (i) implementation (top-down, bottom-
up, and hybrid, cf. Reference [143]), and (ii) type of ethical theory (deontological, consequentialist,
virtue ethics, particularism). Given that not all dimensions for possible classication could be in-
cluded to structure this section, the chosen dimensions focus on the ethical aspect of the selected
papers: the ethical theory and how it is implemented.
A.1 Top-Down
A.1.1 Deontological Ethics. Among top-down deontological approaches, dierent kinds can be
distinguished: papers that use predetermined given rules for a certain domain, papers focusing on
multi-agent systems (MAS), and other papers that do not t either of these two categories.
Domain rules. In the medical domain, Anderson and Anderson [3] use an interpretation of the
four principles of Beauchamp and Childress [22] from earlier work by Anderson et al. [7]tocreate
an ethical eldercare system. The system, called Ethel, needs to oversee the medication intake of pa-
tients. Initial information is given by an overseer, including, for example, at what time medication
should be taken, how much harm could be done by not taking the medication, and the number
of hours it would take to reach this maximum harm. Shim et al. [126] also explore the medical
domain, but focus on mediating between caregivers and patients with Parkinson’s disease. Instead
of a constraint-based approach from previous work, their paper builds on the work by Arkin [9],
who employs a rule-based approach. Based on expert knowledge, a set of rules is created to im-
prove communication quality between patient and caregiver and to ensure that the communication
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132:28 S. Tolmeijer et al.
process is safe and not interrupted. Among other things, each rule has a type (obligation or pro-
hibition) and response output when triggered. The rules are prohibition rules, for example about
yelling, and obligations rules regarding, for instance, how to keep the patient safe. There are verbal
and non-verbal cues for each action, retrieved through sensors. For the military domain, Reed et al.
[113] use a model that balances the principles of civilian non-malecence, military necessity, pro-
portionality, and prospect of success. The resulting principles are ranked in order of importance.
A scenario is used to calibrate the relative ethical violation model by updating the weight for each
principle. Then, a survey is conducted to collect both expert and non-expert assessment of the
situation. Rule-based systems trained on human data perform at the level of human experts. For
the air trac domain, Dennis et al. [51] developed the ETHAN system that deals with situations
when civil air navigation regulations are in conict. The system relates these rules to four hier-
archical ordered ethical principles (do not harm people, do not harm animals, do not damage self,
and do not damage property) and develops a course of action that generates the smallest violation
to those principles in case of conict. McLaren [94] used adjudicated cases from the National So-
ciety of Professional Engineers to adopt the principles in their code of ethics for a system called
SIROCCO. Its primary goal is to test whether it can apply existing heuristic techniques to identify
the principles and previous cases that are most applicable for the analysis of new cases, based on
an engineering ethics ontology. SIROCCO accepts a target case in Ethics Transcription Language,
searches relevant details in cases in its knowledge base in Extended Ethics Transcription Language
and produces advised code provisions and relevant known cases.
Multi-Agent Systems. Wiegel and van den Berg [146] use a Belief-Desire-Intention (BDI) model to
model agents in a MAS setting. Their approach is based on deontic epistemic action logic, which
includes four steps: modelling moral information, creating a moral knowledge base, connecting
moral knowledge to intentions, and including meta-level moral reasoning. Moral knowledge is
linked to intentions and if there is no action that can satisfy the constraints, the agent will not
act. Neto et al. [100] also implement a BDI approach for a MAS. Their focus is on norm conict:
an agent can adopt and update norms, decide which norms to activate based on the case at hand,
its desires, and its intentions. Conict between norms is solved by selecting the norm that adds
most to the achievement of the agent’s intentions and desires. Norm-adherence is incorporated in
the agent’s desires and intentions. Also, Mermet and Simon [95] deal with norm conicts. They
distinguish between moral rules and ethical rules that come into play when moral rules are in
conict. They perform a verication of whether their system called GDT4MAS, is able to choose
the correct ethical rule in conict cases.
Other. Bringsjord and Taylor [35] propose a normative approach using what they call “divine-
command ethics.” They present a divine-command logic intended to be used for lethal autonomous
robots in the military domain. This logic is a natural-deduction proof theory, where input from a
human can be seen as a divine command for the robot. Turilli [134] introduces the concept of the
ethical consistency problem. He is interested in the ethical aspects of information technology in
general. He proposes a generic two-step method that rst translates ethical principles into ethical
requirements, and then ethical requirements into ethical protocols.
A.1.2 Consequentialism. Among papers that use a top-down consequentialist approach, this
survey briey discusses (i) those that focus on the home assistance domain, (ii) those that focus
on safety applications, and (iii) a variety of others.
Home domain.Cloos[40] proposes a service robot for the home environment. The system, called
Utilibot, chooses the action with the highest expected utility. Because of the computational com-
plexity of consequentialism, the ethical theory is a decision criterion rather than a decision process.
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Implementations in Machine Ethics: A Survey 132:29
The description of the system seems a realistic thought experiment, mentioning features the sys-
tem could have, based on previous research. The system controlling the robot, Wellnet, consists
of Bayesian nets and uses a Markov decision process to optimize its behavior for its policies. Van
Dang et al. [135] focus a similar use case but opt for a dierent technical approach: they adopt
a cognitive agent software architecture called Soar. The robot is given information about fam-
ily members. When it receives a request, each possible action is assigned a utility value for each
general law of robotics as proposed by Asimov. The action with the maximum overall utility is se-
lected to be executed, which can be to either obey, disobey, or partially obey (meaning proposing
an alternative option for) the human’s request.
Falling prevention. Three related papers focus on the use case where a human and robot (both
represented by a robot in experiments) are navigating a space that has a hole in the ground. The
robot has to decide how to intervene to prevent the human from falling into the hole.
Wineld et al. [147] add a “Safety/Ethical Logic” layer that is integrated in a so-called con-
sequence engine, which is a simulation-based internal model. This mechanism for estimating the
consequences of actions follows rules very similar to Asimov’s laws of robotics. They address each
law in an experiment. Dennis et al. [52] continue the work of Wineld et al. [147], by using and
extending their approach, and introduce a declarative language that allows the creation of con-
sequence engines within what they name the “agent infrastructure layer toolkit” (AIL). Systems
created with AIL can be formally veried using an available model checker. The example sys-
tem that is implemented sums multiple possible unethical outcomes and minimizes the number of
people harmed. Vanderelst and Wineld [137] have a similar approach and implement two robots
representing humans and a robot that follows Asimov’s laws respectively. In their case study, there
are two goal locations, one of which is dangerous, and the Asimov robot has to intervene.
Other. In early work by Anderson et al. [6], a simple utilitarian system is introduced based on
the theory of Jeremy Bentham that implements act utilitarianism (i.e., calculates utilities of options
and chooses the one with the highest utility).
A.1.3 Particularism. Ashley and McLaren [15] describe a system that “compares cases that con-
tain ethical dilemmas about whether or not to tell the truth.” They use a case-based reasoning ap-
proach to compare the dierent cases in its database. The program, called Truth-Teller, compares
dierent real-world situations in terms of relevant similarities and distinctions in justications
for telling the truth or lying. Representations for principles and reasons, truth telling episodes,
comparison rules, and important scenarios are presented.
A.1.4 Hybrid: Specified Hierarchy. This section contains papers that use a top-down ethical
hybrid approach with a specied hierarchy. Dierent groups can be distinguished: papers where
deontological ethics are dominant over consequentialism, and a paper where consequentialism is
dominant over deontological ethics.
Deontological dominance. While the following three systems all have the same approach, they are
very dierent in their implementation. In the system by Dehghani et al. [50], the ethical theory
type is very clear. The system, called MoralMD, has two modes: deontological and utilitarian.
A new case is processed into predicate calculus and the presence of principles and contextual
features are compared to a determined set of rules in a knowledge base. The order of magnitude
reasoning module calculates the relationship between the utility of each choice. If there are no
sacred values involved in the case at hand (i.e., the deontological component), then the system
will choose the proper output based on the highest utility (i.e., the consequentialist component).
Govindarajulu and Bringsjord [65] provide a rst-order modal logic to formalize the doctrine of
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132:30 S. Tolmeijer et al.
double eect and even of triple eect: “the deontic cognitive event calculus.” The calculus includes
the modal operators for knowledge, beliefs, desires, and intentions. To be able to be useful in non-
logic systems, they explain what characteristics a system should have to be able to use the proposed
approach. The doctrine of double (and triple) eect combines deontological and consequentialist
ethics, where deontology has a greater emphasis than consequentialism. Pereira and Saptawijaya
[102] use prospective logical programming to model various moral dilemmas taken from the classic
trolley problem and employ the principle of double eect as the moral rule. Once an action has
been chosen, preferences for situations are judged a posteriori by the user. The authors show their
implementation in a program called ACORDA.
Consequentialist dominance. In earlier work, Pontier and Hoorn [108] introduced a “cognitive
model of emotional intelligence and aective decision making” called Silicon Coppélia to be used in
the health domain. An agent has three moral duties (autonomy, benecence, and non-malecence)
with a certain ambition to fulll each duty (i.e., weights). The system’s decisions are based on
action-specic expected utilities and consistency with the predetermined duties. While most au-
thors make an act utilitarian system, Pontier and Hoorn create a rule utilitarian system by trying to
maximize the total amount of utility for everyone. While they use rules (i.e., deontological ethics),
they implement them in a consequentialist way, making this the dominant ethical theory type.
Their model was extended to match decisions of judges in medical ethical cases [109].
A.1.5 Hybrid: Unspecified Hierarchy. Both systems in this category focus on a modular ap-
proach, where dierent ethical theory types can be combined in an ethical machine. The goal of
the system by Berreby et al. [28] is to create a modular architecture to represent ethical principles
in a consistent and adjustable manner. They qualify what they call “the Good” and “the Right”
as the ethical part of their system (implying both consequentialist and deontological constraints).
Besides these system components, the system consists of an action model (i.e., “it enables the
agent to represent its environment and the changes that take place in it, taking as input a set of
performed actions”) and a causal model (i.e., “it tracks the causal powers of actions, enabling rea-
soning over agent responsibility and accountability, taking as input the event trace given by the
action model and a specication of events containing a set of events and of dependence relations”)
[28]. The implementation is done in Answer Set Programming using a modied version of Event
calculus. Using a medical scenario, they provide a proof of concept. Lindner et al. [89] have created
a software library for modelling “hybrid ethical reasoning agents” called HERA. Based on logic,
they create a prototype called IMMANUEL, which is a robotic face and upper body that users can
interact with. The system’s ethical constraints draw on consequentialist calculations, the Pareto
principle from economics, and the principle of double eect. Uncertainty and belief in permissibly
of an action are added as extra variables in the system.
A.1.6 Configurable Ethics. The papers in this subsection have a top-down approach and pro-
posed various ways in which ethics can be implemented. One paper has machine ethics tailored
for a specic domain, while another uses dierent techniques in a more domain-general way. A
third focuses on multi-agent systems.
Domain-specic. Thornton et al. [132] combine deontology, consequentialism, and virtue ethics
to optimize driving goals in automated vehicle control. Constraints and costs on vehicle goals are
determined on the basis of both deontological and consequentialist considerations. Virtue ethics
generates specic goals across vehicle types, such that a trac infraction of an ambulance is as-
sessed as less costly than that of a taxi cab.
Domain-general. Ganascia [62] claims to be the rst to attempt to model ethical rules with
Answer Set Programming (cf. [20]) to model three types of ethical systems—Aristotelian ethics,
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Implementations in Machine Ethics: A Survey 132:31
Kantian deontology, and Constant’s “Principles of Politics” (cf. [43]). Drawing on [107] situation
calculus, Bonnemains et al. [32] devise a formalism in which moral dilemmas can be expressed
and resolved in line with distinct ethical systems, including consequentialism and deontological
Multi-agent systems. Cointe et al. [41] extend ethical decision making to multi-agent systems.
The judgment function can accommodate a wide variety of inputs and is not restricted to the
format of a single type of ethical system.
A.1.7 Ambiguous. Arkoudas et al. [13] reason that well-behaved robots should be based on
“mechanized formal logics of action, obligation and permissibility.” After introducing a domain-
specic deontic logic, they describe a previously published interactive theorem proving system,
Athena, that can be utilized to verify ethical systems based on rst-order logic. Murakami [99]
presented an axiomatization of Horty’s utilitarian formulation of multi-agent deontic logic [80],
while Arkoudas et al. [13] present a sequent-based deduction formulation of Murakami’s system.
While deontic logic is used, each deontic stit frame contains a utility function. The contribution
lies in the new approach to Murakami’s system, which is implemented and proven in Athena. In
a dierent approach, the proposed system by Cervantes et al. [38] devise a computational model
for moral decision-making inspired by neuronal mechanisms of the human brain. The model inte-
grates agential preferences, past experience, current emotional states, a set of ethical rules, as well
as certain utilitarian and deontological doctrines as desiderata for the impending ethical decision.
With an entirely dierent focus, Atkinson and Bench-Capon [17] depart from Hare’s contention
[73] that in situations with serious consequences, we engage in complex moral reasoning rather
than the simple application of moral rules and norms. Moral norms are thus considered not an
input to, but an output of serious moral deliberation. The authors model situated moral reason-
ing drawing on Action-Based Alternating Transition Systems (cf. Reference [148] as well as Refer-
ence [16]). While some argue this approach can be seen as virtue ethics (e.g., [23]), the authors of
this survey consider this to be a consequentialist implementation, as the focus of the approach is
on whether the consequences of an action adhere to a certain value.
Verheij [139] draws on Bench-Capon’s framework of value-based argumentation [24,25], which
is inspired by case law (new cases are decided on past cases where there is no clear legislation,
cf. Reference [70]). The paper, focusing on computational argumentation for AI in Law, breaks
new ground in so far as the formal model is not restricted to either qualitative or quantitative
primitives, but integrates both.
A.2 Boom-up
A.2.1 Deontological Ethics. Malle et al. [93] argue that robots need to have a norm capacity—a
capacity to learn and adhere to norms. Drawing on deontic logic, the authors explore two distinct
approaches of implementing a norm system in an articial cognitive architecture. Noothigattu
et al. [101] collect data on human ethical decision making to learn societal preferences. They then
create a system that summarizes and aggregates the results to make ethical decisions.
A.2.2 Consequentialism. Armstrong [14] observes that equipping articial agents directly with
values or preferences can be dangerous (cf. Reference [33]). Representing values as utility func-
tions, the author proposes a value selection mechanism where existing values do not interfere
with the adoption of new ones. Abel et al. [1] pursue a related goal. In contrast to Armstrong,
the agent does not maximize a changing meta-utility function but instead draws on partially ob-
servable Markov decision processes (cf. Reference [85]) familiar from reinforcement learning. The
system is tested with respect to two moral dilemmas.
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132:32 S. Tolmeijer et al.
A.2.3 Hybrid: Unspecified Hierarchy. In contrast to the dominant action-based models of au-
tonomous articial moral agents, Howard and Muntean [82] advocate an agent-based model,
which combines traits of virtue ethics and moral particularism. The implementation draws on
neural networks optimized by evolutionary computation and is given a test run with the NEAT
(NeuroEvolution of Augmenting Topologies) package (cf. References [55,114,129,130]).
A.2.4 Ambiguous. Furbach et al. [60] demonstrate how deontic logic can be transformed into
description logic so as to be processed by Hyper—a theorem prover employing hypertableau cal-
culus by aid of which normative systems can be evaluated and checked for consistency. Wu and
Lin [151] are interested in “ethics shaping” and propose a reinforcement learning model. The latter
is augmented by a system of penalties and rewards that draws on the Kullback-Leibler divergence
A.3 Hybrid
This section introduces selected papers that use a hybrid approach to implement ethics by com-
bining top-down and bottom-up elements.
A.3.1 Deontological Ethics. The following papers, all by the same set of authors, use a hybrid
approach to implement deontological ethics. In 2004, Anderson et al. [6] introduced W.D. , a system
based on the prima facie duties advocated by W. D. Ross. W.D. leaves the encoding of a situation
up to the user, who has to attribute values to the satisfaction and violation of the duties for each
possible action. The system pursues the action with the highest weighted sum of duty satisfaction.
Two years later, Anderson et al. [7] introduced MedEthEx, an advisory system in medical ethics.
MedEthEx has three components: a basic module trained by experts, a knowledge-based inter-
face that guides users when inputting a new case, and a module that provides advice for the new
case at hand. In 2014, Anderson and Anderson [5]createdGenEth, a general analyzing system for
moral dilemmas. The system is capable of representing a variety of aspects of dilemmas (situa-
tional features, duties, actions, cases, and principles) and can generate abstract ethical principles
by applying inductive logic to solutions of particular dilemma cases. The principles are evaluated
by a self-made Ethical Turing Test: If the system performs as an ethical expert would, then it passes
the test. GenEth was also applied in a eldercare use case [8].
A.3.2 Particularism. Guarini [66] explores whether neural networks can be employed to imple-
ment particularist ethics, as occasionally hinted at by Dancy, one of particularism’s most renowned
advocates (cf. References [4648]). Using the action/omission distinction (cf. Reference [150]fora
review) as a test paradigm, neural networks are trained with dierent types of cases to investigate
whether they can competently judge new ones.
A.3.3 Hybrid: Specified Hierarchy. Arkin [9] explores constraints on the deployment of lethal
autonomous weapons in the battleeld (it was subsequently published as a series of three articles
[1012]. The proposed system is predominantly governed by deontological rules, namely interna-
tional laws of war and the U.S. Army’s rules of engagement. Its architecture relies on four cen-
tral constituents: an Ethical Governor that suppresses lethal action; an Ethical Behavior Control
that constrains behavior in line with the rules; an Ethical Adaptor, which can update the agent’s
constraint set to a more restrictive one; and a Responsibility Advisor, which is the human-robot
interaction part of the system.
Azad-Manjiri [19] develops an architecture for a deontological system constrained by
Beauchamp and Childress’s biomedical principles. The system determines its actions on the ba-
sis of said principles and a decision tree algorithm trained with expert ethicist judgments in a
variety of cases from the biomedical domain. Building on early work by Ganascia [63], Tuş and
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Implementations in Machine Ethics: A Survey 132:33
Ganascia [133] augment a belief-desire-intention rational agent model with normative constraints.
They devote particular attention to the problem arising from the acquisition of new norms, which
frequently stand in conict with existing ones (for an alternative approach building on the belief-
desire-intention model, see Honarvar and Ghasem-Aghaee [78,79] discussed below).
A.3.4 Hybrid: Unspecified Hierarchy. Yilmaz et al. [152] survey the eld of machine ethics and
propose a coherence-driven reective equilibrium model (cf. Reference [112]), by aid of which
conicts across heterogenous interests and values can be resolved. Honarvar and Ghasem-Aghaee
[78] build a belief-desire-intention agent model whose decisions are based on a number of weighted
features drawn from hedonic act utilitarianism (e.g., the amount of pleasure and displeasure for
the agent and other parties aected by the action).
A.3.5 Ambiguous. Most of the work of Saptawijaya and Pereira (cf. References [102105,120
122]) focuses on logic programming and prospective logic to model ethical machines. In Han et al.
[72], they introduce uncertainty as a factor in decision making and draw on abductive logic to
accommodate it. Madl and Franklin [92] call for limits on ethical machines for safety reasons.
Developing on Franklin et al.’s [59] LIDA architecture—an AGI model of human cognition—they
suggest that deliberate actions could be constrained top-down during run time, and ethical meta-
rules (such as certain Kantian principles) could be implemented on a metacognitive level. Rather
than start from a complete set of rules, the latter can gradually expand. The approach is exemplied
by CareBot, an assistive simulated bot for the home care domain. Wallach et al. [144] also discuss
the LIDA model. They demonstrate how emotions can be integrated into a LIDA-based account of
the human decision making process and extend the approach to articial moral agents.
We thank the authors of the selected papers for providing valuable feedback on their paper’s rep-
resentation. Also, we thank the anonymous reviewers for their feedback on our manuscript, which
has helped us to substantially improve it.
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... Broadly, we can classify paradigms of ethics into three overarching classes: utilitarianism, deontological ethics and virtue ethics [4]. Utilitarianism is based on resolving ethical dilemmas based on expected consequences of one's action, and aims to maximize collective utility as the underlying principle. ...
... Computational modeling of ethics can be broadly classified into three paradigmatic approaches: consequentialism, deontological ethics and virtue ethics [4]. We also discuss about Computational Transcendence [5] which leads to emergent responsible behaviour in AAs. ...
... We also discuss about Computational Transcendence [5] which leads to emergent responsible behaviour in AAs. These paradigms of ethics are summarized in Table I Utilitarianism Utilitarianism or consequential ethics [4], [6] is based on reasoning about the consequences of one's actions. It considers an action ethical if it leads to or maximizes overall wellbeing. ...
p>Computational models for ethical autonomy, are crucial for building trustworthy autonomous systems. While different paradigms of ethical autonomy are pursued, comparing and contrasting these paradigms remains a challenge. In this work, we present SPECTRA (Strategic Protocol Evaluation and Configuration Testbed for Responsible Autonomy) a general purpose multi-agent, message passing framework on top of which, different models of computational ethics can be implemented. The paper also presents our implementation of four paradigms of ethics on this framework-- deontology, utilitarianism, virtue ethics and a recently proposed paradigm called computational transcendence. We observe that although agents have the same goal, differences in their underlying paradigm of ethics has a significant impact on the outcomes for individual agents as well as on the system as a whole. We also simulate a mixed population of agents following different paradigms of ethics and study the properties of the emergent system.</p
... The ethics of AI usage has been studied extensively by lawyers, philosophers, and technologists to develop policies to account for the ethical implications of an AI application. However, the development of moral decision-making capability within AI algorithms, based on ethical theories, is still in its infancy; it has been discussed and debated in the last couple of decades [3][4][5], but has resulted in few real-world implementations [6,7]. This question of how to develop AI based on ethical theories falls under the umbrella of machine ethics, often referred to as artificial morality or AI alignment. ...
... This question of how to develop AI based on ethical theories falls under the umbrella of machine ethics, often referred to as artificial morality or AI alignment. The majority of the frameworks discussed in machine ethics are either based on rule-based (deontological), or consequentialist ethical theories [6]. In deontological implementations, an artificial agent abides by a set of rules which dictate its action, regardless of what happens as a result of this action. ...
... For example, a utilitarian might prioritize the needs of the majority over that of the few through utility maximization. For a computer or an artificial agent, following rules or calculating the best consequence is straightforward; this may be one of the reasons why most of the implementations in machine ethics are based on the deontological and consequentialist ethics [6]. ...