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Ethics of Artificial Intelligence

February 2021

Authors:

Ludwig-Maximilians-Universität in Munich

This article provides a comprehensive overview of the main ethical issues related to the impact of Artificial Intelligence (AI) on human society. AI is the use of machines to do things that would normally require human intelligence. In many areas of human life, AI has rapidly and significantly affected human society and the ways we interact with each other. It will continue to do so. Along the way, AI has presented substantial ethical and socio-political challenges that call for a thorough philosophical and ethical analysis. Its social impact should be studied so as to avoid any negative repercussions. AI systems are becoming more and more autonomous, apparently rational, and intelligent. This comprehensive development gives rise to numerous issues. In addition to the potential harm and impact of AI technologies on our privacy, other concerns include their moral and legal status (including moral and legal rights), their possible moral agency and patienthood, and issues related to their possible personhood and even dignity. It is common , however, to distinguish the following issues as of utmost significance with respect to AI and its relation to human society, according to three different time periods: (1) short-term (early 21 century): autonomous systems (transportation, weapons), machine bias in law, privacy and surveillance, the black box problem and AI decision-making; (2) mid-term (from the 2040s to the end of the century): AI governance, confirming the moral and legal status of intelligent machines (artificial moral agents), human-machine interaction, mass automation; (3) long-term (starting with the 2100s): technological singularity, mass unemployment, space colonisation.

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Ethics of Artificial Intelligence

This article provides a comprehensive overview of the main ethical issues related to the impact

of Artificial Intelligence (AI) on human society. AI is the use of machines to do things that would

normally require human intelligence. In many areas of human life, AI has rapidly and signifi-

cantly affected human society and the ways we interact with each other. It will continue to do so.

Along the way, AI has presented substantial ethical and socio-political challenges that call for a

thorough philosophical and ethical analysis. Its social impact should be studied so as to avoid

any negative repercussions. AI systems are becoming more and more autonomous, apparently

rational, and intelligent. This comprehensive development gives rise to numerous issues. In ad-

dition to the potential harm and impact of AI technologies on our privacy, other concerns in-

clude their moral and legal status (including moral and legal rights), their possible moral agency

and patienthood, and issues related to their possible personhood and even dignity. It is com-

mon, however, to distinguish the following issues as of utmost significance with respect to AI

and its relation to human society, according to three different time periods: (1) short-term (early

century): autonomous systems (transportation, weapons), machine bias in law, privacy and

surveillance, the black box problem and AI decision-making; (2) mid-term (from the 2040s to

the end of the century): AI governance, confirming the moral and legal status of intelligent ma-

chines (artificial moral agents), human-machine interaction, mass automation; (3) long-term

(starting with the 2100s): technological singularity, mass unemployment, space colonisation.

Table of Contents

The Relevance of AI for Ethics

What is AI?

Its Ethical Relevance

Main Debates

Machine Ethics

Bottom-up Approaches: Casuistry

ii.

Top-down Approaches: The MoralDM Approach

iii.

Mixed Approaches: The Hybrid Approach

Autonomous Systems

Machine Bias

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The Problem of Opacity

Machine Consciousness

The Moral Status of Artificial Intelligent Machines

The Autonomy Approach

ii.

The Indirect Duties Approach

iii.

The Relational Approach

iv.

The Upshot

Singularity and Value Alignment

Other Debates

AI as a form of Moral Enhancement or a Moral Advisor

ii.

AI and the Future of Work

iii.

AI and the Future of Personal Relationships

iv.

AI and the Concern About Human ‘Enfeeblement’

Anthropomorphism

Ethical Guidelines for AI

Conclusion

References and Further Reading

1. The Relevance of AI for Ethics

This section discusses why AI is of utmost importance for our systems of ethics and morality,

given the increasing human-machine interaction.

a. What is AI?

AI may mean several different things and it is defined in many different ways. When Alan

Turing introduced the so-called Turing test (which he called an ‘imitation game’) in his famous

1950 essay about whether machines can think, the term ‘artificial intelligence’ had not yet been

introduced. Turing considered whether machines can think, and suggested that it would be

clearer to replace that question with the question of whether it might be possible to build ma-

chines that could imitate humans so convincingly that people would find it difficult to tell

whether, for example, a written message comes from a computer or from a human (Turing

1950).

The term ‘AI’ was coined in 1955 by a group of researchers—John McCarthy, Marvin L. Minsky,

Nathaniel Rochester and Claude E. Shannon—who organised a famous two-month summer

workshop at Dartmouth College on the ‘Study of Artificial Intelligence’ in 1956. This event is

widely recognised as the very beginning of the study of AI. The organisers described the work-

shop as follows:

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We propose that a 2-month, 10-man study of artificial intelligence be carried out during

the summer of 1956 at Dartmouth College in Hanover, New Hampshire. The study is to

proceed on the basis of the conjecture that every aspect of learning or any other feature of

intelligence can in principle be so precisely described that a machine can be made to simu-

late it. An attempt will be made to find how to make machines use language, form abstrac-

tions and concepts, solve kinds of problems now reserved for humans, and improve them-

selves. We think that a significant advance can be made in one or more of these problems

if a carefully selected group of scientists work on it together for a summer. (Proposal 1955:

Another, later scholarly definition describes AI as:

the ability of a digital computer or computer-controlled robot to perform tasks commonly

associated with intelligent beings. The term is frequently applied to the project of develop-

ing systems endowed with the intellectual processes characteristic of humans, such as the

ability to reason, discover meaning, generalize, or learn from past experience. (Copeland

2020)

In the early twenty-first century, the ultimate goal of many computer specialists and engineers

has been to build a robust AI system which would not differ from human intelligence in any as-

pect other than its machine origin. Whether this is at all possible has been a matter of lively de-

bate for several decades. The prominent American philosopher John Searle (1980) introduced

the so-called Chinese room argument to contend that strong or general AI (AGI)—that is, build-

ing AI systems which could deal with many different and complex tasks that require human-like

intelligence—is in principle impossible. In doing so, he sparked a long-standing general debate

on the possibility of AGI. Current AI systems are narrowly focused (that is, weak AI) and can

only solve one particular task, such as playing chess or the Chinese game of Go. Searle’s general

thesis was that no matter how complex and sophisticated a machine is, it will nonetheless have

no ‘consciousness’ or ‘mind’, which is a prerequisite for the ability to

understand

, in contrast to

the capability to

compute

(see section 2.e.).

Searle’s argument has been critically evaluated against the counterclaims of functionalism and

computationalism. It is generally argued that intelligence does not require a particular substra-

tum, such as carbon-based beings, but that it will also evolve in silicon-based environments, if

the system is complex enough (for example, Chalmers 1996, chapter 9).

In the early years of the twenty-first century, many researchers working on AI development as-

sociated AI primarily with different forms of the so-called machine learning—that is, technolo-

gies that identify patterns in data. Simpler forms of such systems are said to engage in ‘super-

vised learning’—which nonetheless still requires considerable human input and supervision—

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but the aim of many researchers, perhaps most prominently Yann LeCun, had been set to de-

velop the so-called self-supervised learning systems. These days, some researchers began to dis-

cuss AI in a way that seems to equate the concept with machine learning. This article, however,

uses the term ‘AI’ in a wider sense that includes—but is not limited to—machine learning tech-

nologies.

b. Its Ethical Relevance

The major ethical challenges for human societies AI poses are presented well in the excellent in-

troductions by Vincent Müller (2020), Mark Coeckelbergh (2020), Janina Loh (2019), Catrin

Misselhorn (2018) and David Gunkel (2012). Regardless of the possibility of construing AGI, au-

tonomous AI systems already raise substantial ethical issues: for example, the machine bias in

law, making hiring decisions by means of smart algorithms, racist and sexist chatbots, or non-

gender-neutral language translations (see section 2.c.). The very idea of a machine ‘imitating’

human intelligence—which is one common definition of AI—gives rise to worries about decep-

tion, especially if the AI is built into robots designed to look or act like human beings (Boden et

al. 2017; Nyholm and Frank 2019). Moreover, Rosalind Picard rightly claims that ‘the greater

the freedom of a machine, the more it will need moral standards’ (1997: 19). This substantiates

the claim that all interactions between AI systems and human beings necessarily entail an

ethi-

cal dimension

, for example, in the context of autonomous transportation (see section 2.d.).

The idea of implementing ethics within a machine is one of the main research goals in the field

of machine ethics (for example, Lin et al. 2012; Anderson and Anderson 2011; Wallach and

Allen 2009). More and more responsibility has been shifted from human beings to autonomous

AI systems which are able to work much faster than human beings without taking any breaks

and with no need for constant supervision, as illustrated by the excellent performance of many

systems (once they have successfully passed the debugging phase).

It has been suggested that humanity’s future existence may depend on the implementation of

solid moral standards in AI systems, given the possibility that these systems may, at some point,

either match or supersede human capabilities (see section 2.g.). This point in time was called

‘technological singularity’ by Vernon Vinge in 1983 (see also: Vinge 1993; Kurzweil 2005;

Chalmers 2010). The famous playwright Karl Čapek (1920), the renowned astrophysicist

Stephen Hawking and the influential philosopher Nick Bostrom (2016, 2018) have all warned

about the possible dangers of technological singularity should intelligent machines turn against

their creators, that is, human beings. Therefore, according to Nick Bostrom, it is of utmost im-

portance to build friendly AI (see the alignment problem, discussed in section 2.g.).

In conclusion, the implementation of ethics is crucial for AI systems for multiple reasons: to

provide safety guidelines that can prevent existential risks for humanity, to solve any issues re-

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lated to bias, to build friendly AI systems that will adopt our ethical standards, and to help hu-

manity flourish.

2. Main Debates

The following debates are of utmost significance in the context of AI and ethics. They are not the

only important debates in the field, but they provide a good overview of topics that will likely re-

main of great importance for many decades (for a similar list, see Müller 2020).

a. Machine Ethics

Susan Anderson, a pioneer of machine ethics, defines the goal of machine ethics as:

to create a machine that follows an ideal ethical principle or set of principles in guiding its

behaviour; in other words, it is guided by this principle, or these principles, in the deci-

sions it makes about possible courses of action it could take. We can say, more simply, that

this involves “adding an ethical dimension” to the machine. (2011: 22)

In addition, the study of machine ethics examines issues regarding the moral status of intelli-

gent machines and asks whether they should be entitled to moral and legal rights (Gordon

2020a, 2020b; Richardson 2019; Gunkel and Bryson 2014; Gunkel 2012; Anderson and

Anderson 2011; Wallach and Allen 2010). In general, machine ethics is an interdisciplinary sub-

discipline of the ethics of technology, which is in turn a discipline within applied ethics. The

ethics of technology also contains the sub-disciplines of robot ethics (see, for example, Lin et al.

2011, 2017; Gunkel 2018; Nyholm 2020), which is concerned with questions of how human be-

ings design, construct and use robots; and computer ethics (for example, Johnson 1985/2009;

Johnson and Nissenbaum 1995; Himma and Tavani 2008), which is concerned with =commer-

cial behaviour involving computers and information (for example, data security, privacy issues).

The first ethical code for AI systems was introduced by the famed science fiction writer Isaac

Asimov, who presented his Three Laws of Robotics in

Runaround

(Asimov 1942). These three

were later supplemented by a fourth law, called the Zeroth Law of Robotics, in

Robots and

Empire

(Asimov 1986). The four laws are as follows:

A robot may not injure a human being or, through inaction, allow a human being to be

harmed;

A robot must obey the orders given it by human beings except where such orders would

conflict with the first law;

A robot must protect its own existence as long as such protection does not conflict with

the first or second law;

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A robot may not harm humanity or, by inaction, allow humanity to suffer harm.

Asimov’s four laws have played a major role in machine ethics for many decades and have been

widely discussed by experts. The standard view regarding the four laws is that they are impor-

tant but insufficient to deal with all the complexities related to moral machines. This seems to

be a fair evaluation, since Asimov never claimed that his laws could cope with all issues. If that

was really the case, then Asimov would perhaps not have written his fascinating stories about

problems caused partly by the four laws.

The early years of the twenty-first century saw the proposal of numerous approaches to imple-

menting ethics within machines, to provide AI systems with ethical principles that the machines

could use in making moral decisions (Gordon 2020a). We can distinguish at least three types of

approaches: bottom-up, top-down, and mixed. An example of each type is provided below (see

also Gordon 2020a: 147).

i. Bottom-up Approaches: Casuistry

Guarini’s (2006) system is an example of a bottom-up approach. It uses a neural network which

bases its ethical decisions on a learning process in which the neural network is presented with

known correct answers to ethical dilemmas. After the initial learning process, the system is sup-

posed to be able to solve new ethical dilemmas on its own. However, Guarini’s system generates

problems concerning the reclassification of cases, caused by the lack of adequate reflection and

exact representation of the situation. Guarini himself admits that casuistry alone is insufficient

for machine ethics.

ii. Top-down Approaches: The MoralDM Approach

The system conceived by Dehghani et al. (2011) combines two main ethical theories, utilitarian-

ism and deontology, along with analogical reasoning. Utilitarian reasoning applies until ‘sacred

values’ are concerned, at which point the system operates in a deontological mode and becomes

less sensitive to the utility of actions and consequences. To align the system with human moral

decisions, Dehghani et al. evaluate it against psychological studies of how the majority of human

beings decide particular cases.

The MoralDM approach is particularly successful in that it pays proper respect to the two main

ethical theories (deontology and utilitarianism) and combines them in a fruitful and promising

way. However, their additional strategy of using empirical studies to mirror human moral deci-

sions by considering as correct only those decisions that align with the majority view is mislead-

ing and seriously flawed. Rather, their system should be seen as a model of a descriptive study of

ethical behaviour but not a model for normative ethics.

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iii. Mixed Approaches: The Hybrid Approach

The hybrid model of human cognition (Wallach et al. 2010; Wallach and Allen 2010) combines a

top-down component (theory-driven reasoning) and a bottom-up (shaped by evolution and

learning) component that are considered the basis of both moral reasoning and decision-

making. The result thus far is LIDA, an AGI software offering a comprehensive conceptual and

computational model that models a large portion of human cognition. The hybrid model of

moral reasoning attempts to re-create human decision-making by appealing to a complex com-

bination of top-down and bottom-up approaches leading eventually to a descriptive but not a

normative model of ethics. In addition, its somewhat idiosyncratic understanding of both ap-

proaches from moral philosophy does not in fact match how moral philosophers understand

and use them in normative ethics. The model presented by Wallach et al. is not necessarily inac-

curate with respect to how moral decision-making works in an empirical sense, but their ap-

proach is descriptive rather than normative in nature. Therefore, their empirical model does not

solve the normative problem of how moral machines should act. Descriptive ethics and norma-

tive ethics are two different things. The former tells us how human beings make moral deci-

sions; the latter is concerned with how we should act.

b. Autonomous Systems

The proposals for a system of machine ethics discussed in section 2.a. are increasingly being dis-

cussed in relation to autonomous systems the operation of which poses a risk of harm to human

life. The two most-often discussed examples—which are at times discussed together and con-

trasted and compared with each other—are autonomous vehicles (also known as self-driving

cars) and autonomous weapons systems (sometimes dubbed ‘killer robots’) (Purves et al. 2015;

Danaher 2016; Nyholm 2018a).

Some authors think that autonomous weapons might be a good replacement for human soldiers

(Müller and Simpson 2014). For example, Arkin (2009, 2010) argues that having machines fight

our wars for us instead of human soldiers could lead to a decrease in war crimes if the machines

were equipped with an ‘ethical governor’ system that would consistently follow the rules of war

and engagement. However, others worry about the widespread availability of AI-driven autono-

mous weapons systems, because they think the availability of such systems might tempt people

to go to war more often, or because they are sceptical about the possibility of an AI system that

could interpret and apply the ethical and legal principles of war (see, for example, Royakkers

and van Est 2015; Strawser 2010). There are also worries that ‘killer robots’ might be hacked

(Klincewicz 2015).

Similarly, while acknowledging the possible benefits of self-driving cars—such as increased traf-

fic safety, more efficient use of fuel and better-coordinated traffic—many authors have also

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noted the possible accidents that could occur (Goodall 2014; Lin 2015; Gurney 2016; Nyholm

2018b, 2018c; Keeling 2020). The underlying idea is that autonomous vehicles should be

equipped with ‘ethics settings’ that would help to determine how they should react to accident

scenarios where people’s lives and safety are at stake (Gogoll and Müller 2017). This is consid-

ered another real-life application of machine ethics that society urgently needs to grapple with.

The concern for self-driving cars being involved in deadly accidents for which the AI system may

not have been adequately prepared has already been realised, tragically, as some people have

died in such accidents (Nyholm 2018b). The first instance of death while riding in an autono-

mous vehicle—a Tesla Model S car in ‘autopilot’ mode—occurred in May 2016. The first pedes-

trian was hit and killed by an experimental self-driving car, operated by the ride-hailing com-

pany Uber, in March 2018. In the latter case, part of the problem was that the AI system in the

car had difficulty classifying the object that suddenly appeared in its path. It initially classified

the victim as ‘unknown’, then as a ‘vehicle’, and finally as a ‘bicycle’. Just moments before the

crash, the system decided to apply the brakes, but by then it was too late (Keeling 2020: 146).

Whether the AI system in the car functions properly can thus be a matter of life and death.

Philosophers discussing such cases may propose that, even when it cannot brake in time, the car

might swerve to one side (for example, Goodall 2014; Lin 2015). But what if five people were on

the only side of the road the car could swerve onto? Or what if five people appeared on the road

and one person was on the curb where the car might swerve? These scenarios are similar to the

much-discussed ‘trolley problem’: the choice would involve killing one person to save five, and

the question would become under what sorts of circumstances that decision would or would not

be permissible. Several papers have discussed relevant similarities and differences between the

ethics of crashes involving self-driving cars, on the one hand, and the philosophy of the trolley

problem, on the other (Lin 2015; Nyholm and Smids 2016; Goodall 2016; Himmelreich 2018;

Keeling 2020; Kamm 2020).

One question that has occupied ethicists discussing autonomous systems is what ethical princi-

ples should govern their decision-making process in situations that might involve harm to hu-

man beings. A related issue is whether it is ever acceptable for autonomous machines to kill or

harm human beings, particularly if they do so in a manner governed by certain principles that

have been programmed into or made part of the machines in another way. Here, a distinction is

made between deaths caused by self-driving cars—which are generally considered a deeply re-

grettable but foreseeable side effect of their use—and killing by autonomous weapons systems,

which some consider always morally unacceptable (Purves et al. 2015). Even a campaign has

been launched to ‘

stop killer robots

’, backed by many AI ethicists such as Noel Sharkey and

Peter Asaro.

One reason for arguing that autonomous weapons systems should be banned the campaign puts

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forward is that what they call ‘meaningful human control’ must be retained. This concept is also

discussed in relation to self-driving cars (Santoni de Sio and van den Hoven 2018). Many au-

thors have worried about the risk of creating ‘responsibility gaps’, or cases in which it is unclear

who should be held responsible for harm that has occurred due to the decisions made by an au-

tonomous AI system (Matthias 2004; Sparrow 2007; Danaher 2016). The key challenge here is

to come up with a way of understanding moral responsibility in the context of autonomous sys-

tems that would allow us to secure the benefits of such systems and at the same time appropri-

ately attribute responsibility for any undesirable consequences. If a machine causes harm, the

human beings involved in the machine’s action may try to evade responsibility; indeed, in some

cases it might seem unfair to blame people for what a machine has done. Of course, if an auton-

omous system produces a good outcome, which some human beings, if any, claim to deserve

praise for, the result might be equally unclear. In general, people may be more willing to take re-

sponsibility for good outcomes produced by autonomous systems than for bad ones. But in both

situations, responsibility gaps can arise. Accordingly, philosophers need to formulate a theory of

how to allocate responsibility for outcomes produced by functionally autonomous AI technolo-

gies, whether good or bad (Nyholm 2018a; Dignum 2019; Danaher 2019a; Tigard 2020a).

c. Machine Bias

Many people believe that the use of smart technologies would put an end to human bias because

of the supposed ‘neutrality’ of machines. However, we have come to realise that machines may

maintain and even substantiate human bias towards women, different ethnicities, the elderly,

people with medical impairments, or other groups (Kraemer et al. 2011; Mittelstadt et al. 2016).

As a consequence, one of the most urgent questions in the context of machine learning is how to

avoid machine bias (Daniels et al. 2019). The idea of using AI systems to support human

decision-making is, in general, an excellent objective in view of AI’s ‘increased efficiency, accu-

racy, scale and speed in making decisions and finding the best answers’ (World Economic

Forum 2018: 6). However, machine bias can undermine this seemingly positive situation in var-

ious ways. Some striking cases of machine bias are as follows:

Gender bias in hiring (Dastin 2018);

Racial bias, in that certain racial groups are offered only particular types of jobs

(Sweeney 2013);

Racial bias in decisions on the creditworthiness of loan applicants (Ludwig 2015);

Racial bias in decisions whether to release prisoners on parole (Angwin et al. 2016);

Racial bias in predicting criminal activities in urban areas (O’Neil 2016);

Sexual bias when identifying a person’s sexual orientation (Wang and Kosinski 2018);

Racial bias in facial recognition systems that prefer lighter skin colours (Buolamwini and

Gebru 2018);

Racial and social bias in using the geographic location of a person’s residence as a proxy

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for ethnicity or socio-economic status (Veale and Binns 2017).

We can recognise at least three reasons for machine bias: (1) data bias, (2) computational/algo-

rithmic bias and (3) outcome bias (Springer et al. 2018: 451). First, a machine learning system

that is trained using data that contain implicit or explicit imbalances reinforces the distortion in

the data with respect to any future decision-making, thereby making the bias systematic.

Second, a programme may suffer from algorithmic bias due to the developer’s implicit or ex-

plicit biases. The design of a programme relies on the developer’s understanding of the norma-

tive and non-normative values of other people, including the users and stakeholders affected by

it (Dobbe et al. 2018). Third, outcome bias could be based on the use of historical records, for

example, to predict criminal activities in certain particular urban areas; the system may allocate

more police to a particular area, resulting in an increase in reported cases which would have

been unnoticed before. This logic would substantiate the AI system’s decision to allocate the po-

lice to this area, even though other urban areas may have similar or even greater numbers of

crimes, more of which would go unreported due to the lack of policing (O’Neil 2016).

Most AI researchers, programmers and developers as well as scholars working in the field of

technology believe that we will never be able to design a fully unbiased system. Therefore, the

focus is on

reducing

machine bias and minimising its detrimental effects on human beings.

Nevertheless, various questions remain. What type of bias cannot be filtered out and when

should we be satisfied with the remaining bias? What does it mean for a person in court to be

subject not only to human bias but also to machine bias, with both forms of injustice potentially

helping to determine the person’s sentence? Is one type of bias not enough? Should we not

rather aim to eliminate human bias instead of introducing a new one?

d. The Problem of Opacity

AI systems are used to make many sorts of decisions that significantly impact people’s lives. AI

can be used to make decisions about who gets a loan, who is admitted to a university, who gets

an advertised job, who is likely to reoffend, and so on. Since these decisions have major impacts

on people, we must be able to understand the underlying reasons for them. In other words, AI

and its decision-making need to be explainable. In fact, many authors discussing the ethics of AI

propose explainability (also referred to as explicability) as a basic ethical criterion, among oth-

ers, for the acceptability of AI decision-making (Floridi et al. 2018). However, many decisions

made by an autonomous AI system are not readily explainable to people. This came to be called

the problem of opacity.

The opacity of AI decision-making can be of different kinds, depending on relevant factors.

Some AI decisions are opaque to those who are affected by them because the algorithms behind

the decisions, though quite easy to understand, are protected trade secrets which the companies

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using them do not want to share with anyone outside the company. Another reason for AI opac-

ity is that most people lack the technical expertise to understand how an AI-based system works,

even if there is nothing intrinsically opaque about the technology in question. With some forms

of AI, not even the experts can understand the decision-making processes used. This has been

dubbed the ‘black box’ problem (Wachter, Mittelstadt and Russell 2018).

On the individual level, it can seem to be an affront to a person’s dignity and autonomy when

decisions about important aspects of their lives are made by machines if it is unclear—or per-

haps even impossible to know—why machines made these decisions. On the societal level, the

increasing prominence of algorithmic decision-making could become a threat to our democratic

processes. Henry Kissinger, the former U.S. Secretary of State, once stated, ‘We may have cre-

ated a dominating technology in search of a guiding philosophy’ (Kissinger 2018; quoted in

Müller 2020). John Danaher, commenting on this idea, worries that people might be led to act

in superstitious and irrational ways, like those in earlier times who believed that they could af-

fect natural phenomena through rain dances or similar behaviour. Danaher has called this situa-

tion ‘the threat of algocracy’—that is, of rule by algorithms that we do not understand but have

to obey (Danaher 2016b, 2019b).

But is AI opacity always, and necessarily, a problem? Is it equally problematic across all con-

texts? Should there be an absolute requirement that AI must in all cases be explainable? Scott

Robbins (2019) has provided some interesting and noteworthy arguments in opposition to this

idea. Robbins argues, among other things, that a hard requirement for explicability could pre-

vent us from reaping all the possible benefits of AI. For example, he points out that if an AI sys-

tem could reliably detect or predict some form of cancer in a way that we cannot explain or un-

derstand, the value of knowing the information would outweigh any concerns about not know-

ing how the AI system would have reached this conclusion. In general, it is also possible to dis-

tinguish between contexts where the procedure behind a decision matters in itself and those

where only the quality of the outcome matters (Danaher and Robbins 2020).

Another promising response to the problem of opacity is to try to construct alternative modes of

explaining AI decisions that would take into account their opacity but would nevertheless offer

some form of explanation that people could act on. Sandra Wachter, Brent Mittelstadt, and

Chris Russell (2019) have developed the idea of a ‘counterfactual explanation’ of such decisions,

one designed to offer practical guidance for people wishing to respond rationally to AI decisions

they do not understand. They state that ‘counterfactual explanations do not attempt to clarify

how [AI] decisions are made internally. Instead, they provide insight into which external facts

could be different in order to arrive at a desired outcome’ (Wachter et al. 2018: 880). Such an

external, counterfactual way of explaining AI decisions might be a promising alternative in cases

where AI decision-making is highly valuable but functions according to an internal logic that is

opaque to most or all people.

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e. Machine Consciousness

Some researchers think that when machines become more and more sophisticated and intelli-

gent, they might at some point become spontaneously conscious as well (compare Russell 2019).

This would be a sort of puzzling—but potentially highly significant from an ethical standpoint—

side effect of the development of advanced AI. Some people are intentionally seeking to create

machines with artificial consciousness. Kunihiro Asada, a successful engineer, set his goal as to

create a robot that can experience pleasure and pain, on the basis that such a robot could engage

in the kind of pre-linguistic learning that a human baby is capable of before it acquires language

(Marchese 2020). Another example is Sophia the robot, whose developers at

Hanson Robotics

say that they wish to create a ‘super-intelligent benevolent being’ that will eventually become a

‘conscious, living machine’.

Others, such as Joanna Bryson, note that depending on how we define consciousness, some ma-

chines might already have some form of consciousness. Bryson argues that if we take conscious-

ness to mean the presence of internal states and the ability to report on these states to other

agents, then some machines might fulfil these criteria even now (Bryson 2012). In addition,

Aïda Elamrani-Raoult and Roman Yampolskiy (2018) have identified as many as twenty-one

different possible tests of machine consciousness.

Moreover, similar claims could be made about the issue of whether machines can have minds. If

mind is defined, at least in part, in a functional way, as the internal processing of inputs from

the external environment that generates seemingly intelligent responses to that environment,

then machines could possess minds (Nyholm 2020: 145–46). Of course, even if machines can be

said to have minds or consciousness in some sense, they would still not necessarily be anything

like human minds. After all, the particular consciousness and subjectivity of any being will de-

pend on what kinds of ‘hardware’ (such as brains, sense organs, and nervous systems) the being

in question has (Nagel 1974).

Whether or not we think some AI machines are already conscious or that they could (either by

accident or by design) become conscious, this issue is a key source of ethical controversy.

Thomas Metzinger (2013), for example, argues that society should adopt, as a basic principle of

AI ethics, a rule against creating machines that are capable of suffering. His argument is simple:

suffering is bad, it is immoral to cause suffering, and therefore it would be immoral to create

machines that suffer. Joanna Bryson contends similarly that although it is possible to create ma-

chines that would have a significant moral status, it is best to avoid doing so; in her view, we are

morally obligated not to create machines to which we would have obligations (Bryson 2010,

2019). Again, this might all depend on what we understand by consciousness. Accordingly, Eric

Schwitzgebel and Mara Garza (2015: 114–15) comment, ‘If society continues on the path to-

wards developing more sophisticated artificial intelligence, developing a good theory of con-

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sciousness is a moral imperative’.

Another interesting perspective is provided by Nicholas Agar (2019), who suggests that if there

are arguments both in favour of and against the possibility that certain advanced machines have

minds and consciousness, we should err on the side of caution and proceed on the assumption

that machines do have minds. On this basis, we should then avoid any actions that might con-

ceivably cause them to suffer. In contrast, John Danaher (2020) states that we can never be sure

as to whether a machine has conscious experience, but that this uncertainty does not matter; if a

machine behaves similarly to how conscious beings with moral status behave, this is sufficient

moral reason, according to Danaher’s ‘ethical behaviourism’, to treat the machine with the same

moral considerations with which we would treat a conscious being. The standard approach con-

siders whether machines do actually have conscious minds and then how this answer should in-

fluence the question of whether to grant machines moral status (see, for example, Schwitzgebel

and Garza 2015; Mosakas 2020; Nyholm 2020: 115–16).

f. The Moral Status of Artificial Intelligent Machines

Traditionally, the concept of moral status has been of utmost importance in ethics and moral

philosophy because entities that have a moral status are considered part of the moral commu-

nity and are entitled to moral protection. Not all members of a moral community have the same

moral status, and therefore they differ with respect to their claims to moral protection. For ex-

ample, dogs and cats are part of our moral community, but they do not enjoy the same moral

status as a typical adult human being. If a being has a moral status, then it has certain moral

(and legal) rights as well. The twentieth century saw a growth in the recognition of the rights of

ethnic minorities, women, and the LGBTQ+ community, and even the rights of animals and the

environment. This expanding moral circle may eventually grow further to include artificial intel-

ligent machines once they exist (as advocated by the robot rights movement).

The notion of personhood (whatever that may mean) has become relevant in determining

whether an entity has full moral status and whether, depending on its moral status, it should en-

joy the full set of moral rights. One prominent definition of moral status has been provided by

Frances Kamm (2007: 229):

So, we see that within the class of entities that count in their own right, there are those en-

tities that

in their own right and for their own sake

could give us reason to act. I think

that it is this that people have in mind when they ordinarily attribute moral status to an

entity. So, henceforth, I shall distinguish between an entity’s counting morally in its own

right and its having moral status. I shall say that

an entity has moral status when, in its

own right and for its own sake, it can give us reason to do things such as not destroy it or

help it

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Things can be done for X’s own sake, according to Kamm, if X is either conscious and/or able to

feel pain. This definition usually includes human beings and most animals, whereas non-living

parts of nature are mainly excluded on the basis of their lack of consciousness and inability to

feel pain. However, there are good reasons why one should broaden their moral reasoning and

decision-making to encompass the environment as well (Stone 1972, 2010; Atapattu 2015). For

example, the Grand Canyon could be taken into moral account in human decision-making, given

its unique form and great aesthetic value, even though it lacks personhood and therefore moral

status. Furthermore, some experts have treated sentient animals such as great apes and ele-

phants as persons even though they are not human (for example, Singer 1975; Cavalieri 2001;

Francione 2009).

In addition, we can raise the important question of whether (a) current robots used in social sit-

uations or (b) artificial intelligent machines, once they are created, might have a moral status

and be entitled to moral rights as well, comparable to the moral status and rights of human be-

ings. The following three main approaches provide a brief overview of the discussion.

i. The Autonomy Approach

Kant and his followers place great emphasis on the notion of autonomy in the context of moral

status and rights. A moral person is defined as a rational and autonomous being. Against this

background, it has been suggested that one might be able to ascribe personhood to artificial in-

telligent machines once they have reached a certain level of autonomy in making moral deci-

sions. Current machines are becoming increasingly autonomous, so it seems only a matter of

time until they meet this moral threshold. A Kantian line of argument in support of granting

moral status to machines based on autonomy could be framed as follows:

Rational agents have the capability to decide whether to act (or not act) in accordance

with the demands of morality.

The ability to make decisions and to determine what is good

has

absolute value.

The ability to make such decisions

gives

rational persons absolute value.

A rational agent can act autonomously, including acting with respect to moral principles.

Rational agents have dignity

insofar

as they act autonomously.

Acting autonomously makes persons morally responsible.

Such a being—that is, a rational agent—has moral personhood.

It might be objected that machines—no matter how autonomous and rational—are not human

beings and therefore should not be entitled to a moral status and the accompanying rights under

a Kantian line of reasoning. But this objection is misleading, since Kant himself clearly states in

his

Groundwork

(2009) that human beings should be considered as moral agents not because

they are human beings, but because they are autonomous agents (Altman 2011; Timmermann

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2020: 94). Kant has been criticised by his opponents for his logocentrism, even though this very

claim has helped him avoid the more severe objection of speciesism—of holding that a particular

species is morally superior simply because of the empirical features of the species itself (in the

case of human beings, the particular DNA). This has been widely viewed as the equivalent of

racism at the species level (Singer 2009).

ii. The Indirect Duties Approach

The indirect duties approach is based on Kant’s analysis of our behaviour towards animals. In

general, Kant argues in his

Lectures on Ethics

(1980: 239–41) that even though human beings

do not have direct duties towards animals (because they are not persons), they still have indirect

duties towards them. The underlying reason is that human beings may start to treat their fellow

humans badly if they develop bad habits by mistreating and abusing animals as they see fit. In

other words, abusing animals may have a detrimental, brutalising impact on human character.

Kate Darling (2016) has applied the Kantian line of reasoning to show that even current social

robots should be entitled to moral and legal protection. She argues that one should protect life-

like beings such as robots that interact with human beings when society cares deeply enough

about them, even though they do not have a right to life. Darling offers two arguments why one

should treat social robots in this way. Her first argument concerns people who witness cases of

abuse and mistreatment of robots, pointing out that they might become ‘traumatized’ and ‘de-

sensitized’. Second, she contends that abusing robots may have a detrimental impact on the

abuser’s character, causing her to start treating fellow humans poorly as well.

Indeed, current social robots may be best protected by the indirect duties approach, but the idea

that exactly the same arguments should also be applied to future robots of greater sophistication

that either match or supersede human capabilities is somewhat troublesome. Usually, one

would expect that these future robots—unlike Darling’s social robots of today—will be not only

moral patients but rather proper moral agents. In addition, the view that one should protect life-

like beings ‘when society cares deeply enough’ (2016: 230) about them opens the door to social

exclusion based purely on people’s unwillingness to accept them as members of the moral com-

munity. Morally speaking, this is not acceptable. The next approach attempts to deal with this

situation.

iii. The Relational Approach

Mark Coeckelbergh (2014) and David Gunkel (2012), the pioneers of the relational approach to

moral status, believe that robots have a moral status based on their social relation with human

beings. In other words, moral status or personhood emerges through social relations between

different entities, such as human beings and robots, instead of depending on criteria inherent in

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the being such as sentience and consciousness. The general idea behind this approach comes to

the fore in the following key passage (Coeckelbergh 2014: 69–70):

We may wonder if robots will remain “machines” or if they can become companions. Will

people start saying, as they tend to say of people who have “met their dog” … , that some-

one has “met her robot”? Would such a person, having that kind of relation with that ro-

bot, still feel shame at all in front of the robot? And is there, at that point of personal en-

gagement, still a need to talk about the “moral standing” of the robot? Is not moral quality

already implied in the very relation that has emerged here? For example, if an elderly per-

son is already very attached to her Paro robot and regards it as a pet or baby, then what

needs to be discussed is that relation, rather than the “moral standing” of the robot.

The personal experience with the

Other

, that is, the robot, is the key component of this rela-

tional and phenomenological approach. The relational concept of personhood can be fleshed out

in the following way:

A social model of autonomy, under which autonomy is not defined individually but

stands in the context of social relations;

Personhood is absolute and inherent in every entity as a social being; it does not come in

degrees;

An interactionist model of personhood, according to which personhood is relational by

nature (but not necessarily reciprocal) and defined in non-cognitivist terms.

The above claims are not intended as steps in a conclusive argument; rather, they portray the

general line of reasoning regarding the moral importance of social relations. The relational ap-

proach does not require the robot to be rational, intelligent or autonomous as an individual en-

tity; instead, the social encounter with the robot is morally decisive. The moral standing of the

robot is based on exactly this social encounter.

The problem with the relational approach is that the moral status of robots is thus based com-

pletely on human beings’ willingness to enter into social relations with a robot. In other words,

if human beings (for whatever reasons) do not want to enter into such relations, they could deny

robots a moral status to which the robots might be entitled on more objective criteria such as ra-

tionality and sentience. Thus, the relational approach does not actually provide a strong founda-

tion for robot rights; rather, it supports a pragmatic perspective that would make it easier to

welcome robots (who already have moral status) in the moral community (Gordon 2020c).

iv. The Upshot

The three approaches discussed in sections 2.f.i-iii. all attempt to show how one can make sense

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of the idea of ascribing moral status and rights to robots. The most important observation is,

however, that robots are entitled to moral status and rights independently of our opinion, once

they have fulfilled the relevant criteria. Whether human beings will actually

recognise

their sta-

tus and rights are a different matter.

g. Singularity and Value Alignment

Some of the theories of the potential moral status of artificial intelligent agents discussed in sec-

tion 2.f. have struck some authors as belonging to science fiction. The same can be said about

the next topic to be considered: singularity. The underlying argument regarding

technological

singularity

was introduced by statistician I. J. Good in ‘Speculations Concerning the First

Ultraintelligent Machine’ (1965):

Let an ultraintelligent machine be defined as a machine that can far surpass all the intel-

lectual activities of any man however clever. Since the design of machines is one of these

intellectual activities, an ultraintelligent machine could design even better machines; there

would then unquestionably be an “intelligence explosion”, and the intelligence of man

would be left far behind. Thus, the first ultraintelligent machine is the last invention that

man need ever make.

The idea of an intelligence explosion involving self-replicating, super-intelligent AI machines

seems inconceivable to many; some commentators dismiss such claims as a myth about the fu-

ture development of AI (for example, Floridi 2016). However, prominent voices both inside and

outside academia are taking this idea very seriously—in fact, so seriously that they fear the pos-

sible consequence of the so-called ‘existential risks’ such as the risk of human extinction. Among

those voicing such fears are philosophers like Nick Bostrom and Toby Ord, but also prominent

figures like Elon Musk and the late Stephen Hawking.

Authors discussing the idea of technological singularity differ in their views about what might

lead to it. The famous futurist Ray Kurzweil is well-known for advocating the idea of singularity

with exponentially increasing computing power, associated with ‘Moore’s law’, which points out

that the computing power of transistors, at the time of writing, had been doubling every two

years since the 1970s and could reasonably be expected to continue to do so in future (Kurzweil

2005). This approach sees the path to superintelligence as likely to proceed through a continu-

ing improvement of the hardware Another take on what might lead to superintelligence

—favoured by the well-known AI researcher Stuart Russell—focuses instead on algorithms.

From Russell’s (2019) point of view, what is needed for singularity to occur are conceptual

breakthroughs in such areas as the studies of language and common-sense processing as well as

learning processes.

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Researchers concerned with singularity approach the issue of what to do to guard humanity

against such existential risks in several different ways, depending in part on what they think

these existential risks depend on. Bostrom, for example, understands superintelligence as con-

sisting of a maximally powerful capacity to achieve whatever aims might be associated with arti-

ficial intelligent systems. In his much-discussed example (Bostrom 2014), a super-intelligent

machine threatens the future of human life by becoming optimally efficient at maximising the

number of paper clips in the world, a goal whose achievement might be facilitated by removing

human beings so as to make more space for paper clips. From this point of view, it is crucial to

equip super-intelligent AI machines with the right goals, so that when they pursue these goals in

maximally efficient ways, there is no risk that they will extinguish the human race along the way.

This is one way to think about how to create a beneficial super-intelligence.

Russell (2019) presents an alternative picture, formulating three rules for AI design, which

might perhaps be viewed as an updated version of or suggested replacement for Asimov’s fic-

tional laws of robotics (see section 2.a.):

The machine’s only objective is to maximise the realisation of human preferences.

The machine is initially uncertain about what those preferences are.

The ultimate source of information about human preferences is human behaviour.

The theories discussed in this section represent different ideas about what is sometimes called

‘value alignment’—that is, the concept that the goals and functioning of AI systems, especially

super-intelligent future AI systems, should be properly aligned with human values. AI should be

tracking human interests and values, and its functioning should benefit us and not lead to any

existential risks, according to the ideal of value alignment. As noted in the beginning of this sec-

tion, to some commentators, the idea that AI could become super-intelligent and pose existen-

tial threats is simply a myth that needs to be busted. But according to others, thinkers such as

Toby Ord, AI is among the main reasons why humanity is in a critical period where its very fu-

ture is at stake. According to such assessments, AI should be treated on a par with nuclear

weapons and other potentially highly destructive technologies that put us all at great risk unless

proper value alignment happens (Ord 2020).

A key problem concerning value alignment—especially if understood along the lines of Russell’s

three principles—is whose values or preferences AI should be aligned with. As Iason Gabriel

(2020) notes, reasonable people may disagree on what values and interests are the right ones

with which to align the functioning of AI (whether super-intelligent or not). Gabriel’s suggestion

for solving this problem is inspired by John Rawls’ (1999, 2001) work on ‘reasonable pluralism’.

Rawls proposes that society should seek to identify ‘fair principles’ that could generate an over-

lapping consensus or widespread agreement despite the existence of more specific, reasonable

disagreements about values among members of society. But how likely is it that this kind of con-

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vergence in general principles would find widespread support? (See section 3.)

h. Other Debates

In addition to the topics highlighted above, other issues that have not received as much atten-

tion are beginning to be discussed within AI ethics. Five such issues are discussed briefly below.

i. AI as a form of Moral Enhancement or a Moral Advisor

AI systems tend to be used as ‘recommender systems’ in online shopping, online entertainment

(for example, music and movie streaming), and other realms. Some ethicists have discussed the

advantages and disadvantages of AI systems whose recommendations could help us to make

better choices and ones more consistent with our basic values. Perhaps AI systems could even,

at some point, help us improve our values. Works on these and related questions include

Borenstein and Arkin (2016), Giubilini et al. (2015, 2018), Klincewicz (2016), and O’Neill et al.

(2021).

ii. AI and the Future of Work

Much discussion about AI and the future of work concerns the vital issue of whether AI and

other forms of automation will cause widespread ‘technological unemployment’ by eliminating

large numbers of human jobs that would be taken over by automated machines (Danaher

2019a). This is often presented as a negative prospect, where the question is how and whether a

world without work would offer people any prospects for fulfilling and meaningful activities,

since certain goods achieved through work (other than income) are hard to achieve in other con-

texts (Gheaus and Herzog 2016). However, some authors have argued that work in the modern

world exposes many people to various kinds of harm (Anderson 2017). Danaher (2019a) exam-

ines the important question of whether a world with less work might actually be preferable.

Some argue that existential boredom would proliferate if human beings can no longer find a

meaningful purpose in their work (or even their life) because machines have replaced them

(Bloch 1954). In contrast, Jonas (1984) criticises Bloch, arguing that boredom will not be a sub-

stantial issue at all. Another related issue—perhaps more relevant in the short and medium-

term—is how we can make increasingly technologised work remain meaningful (Smids et al.

2020).

iii. AI and the Future of Personal Relationships

Various AI-driven technologies affect the nature of friendships, romances and other interper-

sonal relationships and could impact them even more in future. Online ‘friendships’ arranged

through social media have been investigated by philosophers who disagree as to whether rela-

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tionships that are partly curated by AI algorithms, could be true friendships (Cocking et al.

2012; McFall 2012; Kaliarnta 2016; Elder 2017). Some philosophers have sharply criticised AI-

driven dating apps, which they think might reinforce negative stereotypes and negative gender

expectations (Frank and Klincewicz 2018). In more science-fiction-like philosophising, which

might nevertheless become increasingly present in real life, there has also been discussion about

whether human beings could have true friendships or romantic relationships with robots and

other artificial agents equipped with advanced AI (Levy 2008; Sullins 2012; Elder 2017;

Hauskeller 2017; Nyholm and Frank 2017; Danaher 2019c; Nyholm 2020).

iv. AI and the Concern About Human ‘Enfeeblement’

If more and more aspects of our lives are driven by the recommendations of AI systems (since

we do not understand its functioning and we might question the propriety of its functioning),

the results could include ‘a crisis in moral agency’ (Danaher 2019d), human ‘enfeeblement’

(Russell 2019), or ‘de-skilling’ in different areas of human life (Vallor 2015, 2016). This scenario

becomes even more likely should technological singularity be attained, because at that point all

work, including all research and engineering, could be done by intelligent machines. After some

generations, human beings might indeed be completely dependent on machines in all areas of

life and unable to turn the clock back. This situation is very dangerous; hence it is of utmost im-

portance that human beings remain skilful and knowledgeable while developing AI capacities.

v. Anthropomorphism

The very idea of artificial intelligent machines that imitate human thinking and behaviour might

incorporate, according to some, a form of anthropomorphising that ought to be avoided. In

other words, attributing humanlike qualities to machines that are not human might pose a prob-

lem. A common worry about many forms of AI technologies (or about how they are presented to

the general public) is that they are deceptive (for example, Boden et al. 2017). Many have ob-

jected that companies tend to exaggerate the extent to which their products are based on AI

technology. For example, several prominent AI researchers and ethicists have criticised the

makers of Sophia the robot for falsely presenting her as much more humanlike than she really is

(for example, Sharkey 2018; Bryson 2010, 2019), and as being designed to prompt anthropo-

morphising responses in human beings that are somehow problematic or unfitting. The related

question of whether anthropomorphising responses to AI technologies are always problematic

requires further consideration, which it is increasingly receiving (for example, Coeckelbergh

2010; Darling 2016, 2017; Gunkel 2018; Danaher 2020; Nyholm 2020; Smids 2020).

This list of emerging topics within AI ethics is not exhaustive, as the field is very fertile, with

new issues arising constantly. This is perhaps the fastest-growing field within the study of ethics

and moral philosophy.

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3. Ethical Guidelines for AI

As a result of widespread awareness of and interest in the ethical issues related to AI, several

influential institutions (including governments, the European Union, large companies and other

associations) have already tasked expert panels with drafting policy documents and ethical

guidelines for AI. Such documents have proliferated to the point at which it is very difficult to

keep track of all the latest AI ethical guidelines being released. Additionally, AI ethics is receiv-

ing substantial funding from various public and private sources, and multiple research centres

for AI ethics have been established. These developments have mostly received positive re-

sponses, but there have also been some worries about the so-called ‘ethics washing’—that is, giv-

ing an ethical stamp of approval to something that might be, from a more critical point of view,

ethically problematic (compare Tigard 2020b)—along with concerns that some efforts may be

relatively toothless or too centred on the West, ignoring non-Western perspectives on AI ethics.

This section, before discussing such criticisms, reviews examples of already published ethical

guidelines and considers whether any consensus can emerge between these differing guidelines.

An excellent resource in this context is the overview by Jobin et al. (2019), who conducted a sub-

stantial comparative review of 84 sets of ethical guidelines issued by national or international

organisations from various countries. Jobin et al. found strong convergence around five key

principles—transparency, justice and fairness, non-maleficence, responsibility, and privacy,

among many. Their findings are reported here to illustrate the extent of this convergence on

some (but not all) of the principles discussed in the original paper. The number on the left indi-

cates the number of ethical guideline documents, among the 84 examined, in which a particular

principle was prominently featured. The codes Jobin et al. used are included so that readers can

see the basis for their classification.

Ethical principle

Number of documents (N = 84)

Codes included

Transparency

Transparency, explainability, ex-

plicability, understandability, in-

terpretability, communication,

disclosure

Justice and fairness

Justice, fairness, consistency, in-

clusion, equality, equity,

(non-)bias, (non-)discrimination,

diversity, plurality, accessibility,

reversibility, remedy, redress,

challenge, access, distribution

Non-maleficence

Non-maleficence, security, safety,

harm, protection, precaution, in-

tegrity (bodily or mental), non-

subversion

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Responsibility

Responsibility, accountability, li-

ability, acting with integrity

Privacy

Privacy, personal or private infor-

mation

Beneficence

Benefits, beneficence, well-being,

peace, social good, common good

Freedom and autonomy

Freedom, autonomy, consent,

choice, self-determination, lib-

erty, empowerment

Trust

Sustainability

Sustainability, environment (na-

ture), energy, resources (energy)

Dignity

Solidarity

Solidarity, social security, cohe-

sion

The review conducted by Jobin et al. (2019) reveals, at least with respect to the first five princi-

ples on the list, a significant degree of overlap in these attempts to create ethical guidelines for

AI (see Gabriel 2020). On the other hand, the last six items on the list (beginning with benefi-

cence) appeared as key principles in fewer than half of the documents studied. Relatedly, re-

searchers working on the ‘moral machine’ research project, which examined people’s attitudes

as to what self-driving cars should be programmed to do in various crash dilemma scenarios,

also found great variation, including cross-cultural variation (Awad et al. 2018).

These ethical guidelines have received a fair amount of criticism—both in terms of their content

and with respect to how they were created (for example, Metzinger 2019). For Metzinger, the

very idea of ‘trustworthy AI’ is ‘nonsense’ since only human beings and not machines can be, or

fail to be, trustworthy. Furthermore, the EU high-level expert group on AI had very few experts

from the field of ethics but numerous industry representatives, who had an interest in toning

down any ethical worries about the AI industry. In addition, the EU document ‘Ethical

Guidelines for Trustworthy AI’ uses vague and non-confrontational language. It is, to use the

term favoured by Resseguier and Rodrigues (2020), a mostly ‘toothless’ document. The EU ethi-

cal guidelines that industry representatives have supposedly made toothless illustrate the con-

cerns raised about the possible ‘ethics washing’.

Another point of criticism regarding these kinds of ethical guidelines is that many of the expert

panels drafting them are non-inclusive and fail to take non-Western (for example, African and

Asian) perspectives on AI and ethics into account. Therefore, it would be important for future

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versions of such guidelines—or new ethical guidelines—to include non-Western contributions.

Notably, in academic journals that focus on the ethics of technology, there has been modest

progress towards publishing more non-Western perspectives on AI ethics—for example, apply-

ing Dao (Wong 2012), Confucian virtue-ethics perspectives (Jing and Doorn 2020), and south-

ern African relational and communitarian ethics perspectives including the ‘ubuntu’ philosophy

of personhood and interpersonal relationships (see Wareham 2020).

4. Conclusion

The ethics of AI has become one of the liveliest topics in philosophy of technology. AI has the

potential to redefine our traditional moral concepts, ethical approaches and moral theories. The

advent of artificial intelligent machines that may either match or supersede human capabilities

poses a big challenge to humanity’s traditional self-understanding as the only beings with the

highest moral status in the world. Accordingly, the future of AI ethics is unpredictable but likely

to offer considerable excitement and surprise.

5. References and Further Reading

Agar, N. (2020). How to Treat Machines That Might Have Minds.

Philosophy & Technology

, 33(2): 269–82.

Altman, M. C. (2011).

Kant and Applied Ethics: The Uses and Limits of Kant’s Practical Philosophy.

Malden,

NJ: Wiley-Blackwell.

Anderson, E. (2017).

Private Government: How Employers Rule Our Lives (and Why We Don’t Talk about

It).

Princeton, NJ: Princeton University Press.

Anderson, M., and Anderson, S. (2011).

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Author Information

John-Stewart Gordon

Email:

johnstgordon@pm.me

Vytautas Magnus University

Lithuania

and

Sven Nyholm

Email:

s.r.nyholm@uu.nl

The University of Utrecht

The Netherlands

Ethics of Artiﬁcial Intelligence | Internet Encyclopedia of Philosophy https://iep.utm.edu/ethic-ai/

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AI Ethics by Design: Implementing Customizable Guardrails for Responsible AI Development

Preprint

Full-text available

Nov 2024

This paper explores the development of an ethical guardrail framework for AI systems, emphasizing the importance of customizable guardrails that align with diverse user values and underlying ethics. We address the challenges of AI ethics by proposing a structure that integrates rules, policies, and AI assistants to ensure responsible AI behavior, while comparing the proposed framework to the existing state-of-the-art guardrails. By focusing on practical mechanisms for implementing ethical standards, we aim to enhance transparency, user autonomy, and continuous improvement in AI systems. Our approach accommodates ethical pluralism, offering a flexible and adaptable solution for the evolving landscape of AI governance. The paper concludes with strategies for resolving conflicts between ethical directives, underscoring the present and future need for robust, nuanced and context-aware AI systems.

Ethics of Socially Disruptive Technologies: An Introduction

Book

Full-text available

Sep 2023

Technologies shape who we are, how we organize our societies and how we relate to nature. For example, social media challenges democracy; artificial intelligence raises the question of what is unique to humans; and the possibility to create artificial wombs may affect notions of motherhood and birth. Some have suggested that we address global warming by engineering the climate, but how does this impact our responsibility to future generations and our relation to nature? This book shows how technologies can be socially and conceptually disruptive and investigates how to come to terms with this disruptive potential. Four technologies are studied: social media, social robots, climate engineering and artificial wombs. The authors highlight the disruptive potential of these technologies, and the new questions this raises. The book also discusses responses to conceptual disruption, like conceptual engineering, the deliberate revision of concepts.

The Future of Privacy: A Review on AI's Role in Shaping Data Security

Conference Paper

Full-text available

May 2024

The rapid adoption of Artificial Intelligence (AI) technological advances marked an era of exceptional growth, significantly converting how we interact with the world of technology. But this adoption has arrived at an enormous cost: the gradual loss of private life. The study starts with an indepth investigation into the complicated and modifying relationship between AI and privacy, acquiring information via case studies in the natural environment. One prominent case study is deploying AI technology to recognize faces in public spaces. Issues around illicit tracking and privacy intrusions have grown as governments and business entities regularly employ such technologies for recognition and protection. Assessing the challenges and findings associated with such deployments offers the conceptual debate a practical setting. It explores the various challenges that arise from AIdriven innovations and offers fresh strategies for maintaining and safeguarding individual privacy concerns in emerging data-centric circumstances. It emphasizes AI's dual role as a catalyst for outstanding outcomes and a potential risk to personal privacy. Finally, this study is interested in contributing to the current discussion on AI and Privacy, by providing an in-depth examination of the difficulties that might be utilized by lawmakers, stakeholders in the industry, as well as individuals alike. As humanity moves into a scenario where AI encompasses every aspect of daily life, the study acts as a guide for managing the evolving terrain concerning confidentiality in the era of AI, assisting appropriate AI advancement and preserving human privacy rights.

To each Technology Its Own Ethics? A Reply to Sætra & Danaher (and Their Critics)

Article

Full-text available

Aug 2024

Contemporary ethics is currently ramifying into different sub-ethics specific to each type of technology. Although this trend has been very timely and rightly called into question by Sætra and Danaher, both these authors and their critics Llorca Albareda and Rueda leave the matter unsolved from a discipline point of view. In this commentary, we clarify the statute of the ethics of technology, which corresponds to that of a subsidiary applied ethics, and show how it is precisely that, what renders the creation of an ethics for each technology inappropriate. We thus provide a disciplinary reason to support Sætra and Danaher’s concern on tech ethics proliferation and to refute Llorca Albareda and Rueda’s relativization of it. In turn, we conclude by drawing some guidelines for tech ethics in practice.

What Is This Thing Called the Ethics of AI and What Calls for It?

Chapter

Jul 2024

Sven Nyholm

Algorithmic Prioritarianism and Original Position: To What Extent Are Large Language Models Aligned With the Principles of Justice?

Preprint

Full-text available

Jul 2024

Jesse Koeppel

With the recent and very rapid popularization of generative Artificial Inteligence (AI), including image generators and chatbots, questions regarding stereotypically 'good' and 'bad' robots come into question. In A Theory of Justice, John Rawls presents a thought experiment known as the Original Position to initially decide what values a society should have to maximize the greatest good. It is understood that prioritarianism is effective at aligning artificial moral agents and can be applied to LLMs. A selection of questions designed to challenge the alignment of the principles of justice and the stance of distributive justice thereof are asked of the various LLMs. The data will be coded with respect to the sector of political philosophy the question seeks to answer. Quantitatively, the most common overall recurring classification present that provides a rational philosophical judgment amongst the responses is the prioritarianistically derived class. Overall the presence of the difference principle in LLM decision-making regarding philosophical judgment is well defined and can be considered somewhat aligned. A noticeable inconsistency within the rationale throughout the three LLMs is the absence of a philosophical judgment. All things considered, it's safe to say that even if LLMs are moderately aligned with the principles of justice, as found, there are too many uncertainties with the state of LLMs to give this practical use.

Data Ethics: Normative Principles and Their Regulatory Challenges

Chapter

Full-text available

Sep 2024

This handbook is currently in development, with individual articles publishing online in advance of print publication. At this time, we cannot add information about unpublished articles in this handbook, however the table of contents will continue to grow as additional articles pass through the review process and are added to the site. Please note that the online publication date for this handbook is the date that the first article in the title was published online. For more information, please read the site FAQs.

Non-Binary and Trans-Inclusive AI: A Catalogue of Best Practices for Developing Automatic Gender Recognition Solutions

Article

Aug 2024

Artificial intelligence (AI) has significantly optimized processes across various sectors, enhancing efficiency and transforming digital interactions. However, as AI becomes more integrated into daily life, concerns about its social impacts and inherent biases have emerged. This study explores how AI technologies, such as facial recognition and Automatic Gender Recognition (AGR), can perpetuate and amplify societal prejudices, especially against transgender and non-binary individuals. The 2018 case of Amazon's Rekognition technology, which exhibited high false positive rates for individuals with dark skin, highlights the risks of algorithmic bias and mass surveillance. Given these challenges, this research performed performed a systematic mapping study of the literature on AI to present an analysis of problems and respective causes brought by facial recognition and AGR applications to trans and non-binary people. In a second phase, we developed and empirically assessed a catalog of 19 best practices for an ethical AI development grounded in Justice, Equity, Diversity, and Inclusion principles. We aim to establish ethical standards that promote inclusivity to trans and non-binary people, mitigating algorithmic discrimination.

AN EXPLORATION STUDY ON MARKETING COMMUNICATION STRATEGIES IN THE GROCERY RETAIL SECTOR OF KWAZULU NATAL PROVINCE, SOUTH AFRICA

Thesis

Full-text available

Nov 2023

Grocery stores in South Africa require both proactive and reactive communication methods to maintain sales owing to the country's competitive landscape, shifting consumer preferences, and rapidly evolving economic and technological advancements. The aim of the study was to investigate the MCS’s in grocery retail sector of KZN Province, South Africa. Primary and secondary goals were established for this study in order to solve the aforementioned challenges: The primary objective of this study was to investigate the MCS’s in grocery retail sector of KZN Province, South Africa. The following secondary objectives were set for this study to support the primary objective: to determine the MCS’s adopted by the grocery retailers in attracting customers; to determine the application of the MCS’s in the grocery retail industry. to determine the challenges of adopting and implementing MCS’s in the grocery retail industry. In view of this, a qualitative approach was adopted in the study and case study design with ten grocery retail stores. The study employed thematic analysis and data analysis was also done using Atlas.ti. The study found the specific MCS’s adopted, namely; business sustainability strategy, customer loyalty strategy and effective customer acquisition strategy. The study also discovered that grocery retailers use both new and traditional media. In particular, social media and online marketing are frequently used in communicating and attracting customers. The findings reveal that the success of these MCS’s is determined by improved customer satisfaction and business transformation.

General Introduction

Chapter

May 2023

John-Stewart Gordon

In this chapter, the author delves into the complex topic of robot rights, outlining the book’s central theme and primary argument. The discussion begins with an examination of two prominent sceptical viewpoints on robot rights, attributed to scholars A. Birhane, J. van Dijk, and L. Floridi. Birhane and van Dijk champion human exceptionalism, emphasising human uniqueness and superiority, thus casting doubt on the necessity for robot rights. In contrast, Floridi’s mindless morality concept contests the idea of robots having moral agency, questioning the legitimacy of granting them rights. The author argues that these sceptical perspectives are ultimately unconvincing. To facilitate a more insightful and nuanced dialogue on robot rights, it is crucial to remain open-minded when contemplating the ethical considerations and potential ramifications of general artificial intelligence and superintelligence. By reorienting the conversation, the author invites readers to reassess preconceived beliefs about robots’ roles, responsibilities, and the evolving dynamics between humans and advanced technologies.

Ethical Issues with Artificial Ethics Assistants

Article

Full-text available

Nov 2021

This chapter examines the possibility of using artificial intelligence (AI) technologies to improve human moral reasoning and decision-making. The authors characterize such technologies as artificial ethics assistants (AEAs). The authors focus on just one part of the AI-aided moral improvement question: the case of the individual who wants to improve their morality, where what constitutes an improvement is evaluated by the individual’s own values. The authors distinguish three broad areas in which an individual might think their own moral reasoning and decision-making could be improved: one’s actions, character, or other attributes fall short of one’s values and moral beliefs; one sometimes misjudges or is uncertain about what the right thing to do is, given one’s values; or one is uncertain about some fundamental moral questions or recognizes a possibility that some of one’s core moral beliefs and values are mistaken. The authors sketch why one might think AI tools could be used to support moral improvement in those areas and distinguish two types of assistance: preparatory assistance, including advice and training supplied in advance of moral deliberation, and on-the-spot assistance, including on-the-spot advice and facilitation of moral functioning over the course of moral deliberation. Then, the authors turn to ethical issues that AEAs might raise, looking in particular at three under-appreciated problems posed by the use of AI for moral self-improvement: namely, reliance on sensitive moral data, the inescapability of outside influences on AEAs, and AEA usage prompting the user to adopt beliefs and make decisions without adequate reasons.

Responsible AI and moral responsibility: a common appreciation

Article

Full-text available

Oct 2020

Daniel Tigard

Responsibility is among the most widespread buzzwords in the ethics of artificial intelligence (AI) and robotics. Yet, the term often remains unsubstantiated when employed in these important technological domains. Indeed, notions like ‘responsible AI’ and ‘responsible robotics’ may sound appealing, for they seem to convey a sense of moral goodness or ethical approval, thereby inciting psychological connections to self-regulation, social acceptance, or political correctness. For AI and ethics to come together in truly harmonious ways, we will need to work toward establishing a common appreciation. In this commentary, I breakdown three varieties of the term and invoke insights from the analytic ethics literature as a means of offering a robust understanding of moral responsibility in emerging technology. While I do not wish to accuse any parties of incorrect usage, my hope is that together researchers in AI and ethics can be better positioned to appreciate and to develop notions of responsibility for technological domains.

Artificial Intelligence, Values, and Alignment

Article

Full-text available

Sep 2020
MIND MACH

Iason Gabriel

This paper looks at philosophical questions that arise in the context of AI alignment. It defends three propositions. First, normative and technical aspects of the AI alignment problem are interrelated, creating space for productive engagement between people working in both domains. Second, it is important to be clear about the goal of alignment. There are significant differences between AI that aligns with instructions, intentions, revealed preferences, ideal preferences, interests and values. A principle-based approach to AI alignment, which combines these elements in a systematic way, has considerable advantages in this context. Third, the central challenge for theorists is not to identify ‘true’ moral principles for AI; rather, it is to identify fair principles for alignment that receive reflective endorsement despite widespread variation in people’s moral beliefs. The final part of the paper explores three ways in which fair principles for AI alignment could potentially be identified.

Artificial moral and legal personhood

Article

Full-text available

Jun 2021
AI Soc

John-Stewart Gordon

This paper considers the hotly debated issue of whether one should grant moral and legal personhood to intelligent robots once they have achieved a certain standard of sophistication based on such criteria as rationality, autonomy, and social relations. The starting point for the analysis is the European Parliament’s resolution on Civil Law Rules on Robotics (2017) and its recommendation that robots be granted legal status and electronic personhood. The resolution is discussed against the background of the so-called Robotics Open Letter, which is critical of the Civil Law Rules on Robotics (and particularly of §59 f.). The paper reviews issues related to the moral and legal status of intelligent robots and the notion of legal personhood, including an analysis of the relation between moral and legal personhood in general and with respect to robots in particular. It examines two analogies, to corporations (which are treated as legal persons) and animals, that have been proposed to elucidate the moral and legal status of robots. The paper concludes that one should not ascribe moral and legal personhood to currently existing robots, given their technological limitations, but that one should do so once they have achieved a certain level at which they would become comparable to human beings.

The Ethics of Automated Vehicles

Thesis

Full-text available

Apr 2020

Geoff Keeling

My thesis is about the morality of automated vehicle (AV) decisions. What is the relevance of the trolley problem to AV decisions? What is the morally right method for AVs to distribute harms or risks of harm between the parties in collisions? Who is morally responsible for harm caused in collisions? How does the risk of harm to road-users trade-off against the AV's prudential goal of getting to its destination in reasonable time? What is the morally right amount of caution for AVs to exercise when uncertain about the classification of proximate objects? I will answer these questions by developing a deontological account of permissible killing for AVs and a deontological account of permissible risk-imposition. I also argue against some of the rival answers to these questions; and in doing so, attempt to show that my view does a better job than its rivals at capturing our considered moral judgements.

AI ethics should not remain toothless! A call to bring back the teeth of ethics

Article

Full-text available

Jul 2020

Ethics has powerful teeth, but these are barely being used in the ethics of AI today – it is no wonder the ethics of AI is then blamed for having no teeth. This article argues that ‘ethics’ in the current AI ethics field is largely ineffective, trapped in an ‘ethical principles’ approach and as such particularly prone to manipulation, especially by industry actors. Using ethics as a substitute for law risks its abuse and misuse. This significantly limits what ethics can achieve and is a great loss to the AI field and its impacts on individuals and society. This article discusses these risks and then highlights the teeth of ethics and the essential value they can – and should – bring to AI ethics now.

Extending legal protection to social robots: The effects of anthropomorphism, empathy, and violent behavior towards robotic objects

Chapter

Jan 2016

Kate Darling

Amazon Scraps Secret AI Recruiting Tool that Showed Bias against Women *

Chapter

Mar 2022

Jeffrey Dastin

African Values, Ethics, and Technology Questions, Issues, and Approaches: Questions, Issues, and Approaches

Book

Jan 2021

Beatrice Okyere-Manu

This book charts technological developments from an African ethical perspective. It explores the idea that while certain technologies have benefited Africans, the fact that these technologies were designed and produced in and for a different setting leads to conflicts with African ethical values. Written in a simple and engaging style, the authors apply an African ethical lens to themes such as: The Fourth Industrial Revolution, the moral status of technology, technology and sexual relations, and bioethics and technology. Beatrice Dedaa Okyere-Manu is Senior Lecturer in Applied Ethics in the School of Religion, Philosophy and Classics at the University of KwaZulu-Natal, South Africa, where she is also the Director of the Ethics Studies Department.

Just Ordinary Robots: Automation from Love to War

Book

Aug 2015

Ethics of Artificial Intelligence

Abstract

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Other Minds, Other Intelligences: The Problem of Attributing Agency to Machines