Coping with Artificial Intelligence Ethical Dilemma and Ethical
Position Choices?
Sylvie Gerbaix
, Sylvie Michel
and Marc Bidan
Laboratoire MRM, Montpellier University, Aix-Marseille University, France
Univ. Bordeaux, IRGO, UR 4190, F-33000 Bordeaux, France
LEMNA, Nantes Université, France
Keywords: Artificial Intelligence (AI), Algorithmic System, Ethical Theory, Bias, Responsibility, Transparency.
Abstract: The aim of this conceptual article is to demonstrate that proposing measures, actions, and decisions to
improve the ethics of Artificial Intelligence (AI) depends on the ethical theoretical position chosen. To
achieve this, we proceeded in two stages. Firstly, we characterized and synthesized three different ethical
issues posed by AI. Secondly, we selected two main ethical positions proposed by philosophical literature.
Finally, we showed that the choice of an ethical theoretical position for each category of ethical issues of AI
leads to different decisions. We demonstrated that for each category of ethical problems, the ethical
decisions and their consequences differ depending on the ethical theory chosen. The value of this paper is to
highlight that the literature on AI ethics often neglects the implications of choosing an ethical position. In
order to attempt to solve ethical issues, it is necessary to reach agreements and have discussions that take
into account the different ethical theoretical positions and their consequences in terms of decision-making.
Today, AI helps us in selecting footage, music,
friends, and partners (Milano et al., 2020). It also
supports institutions in making legal decisions,
maintaining public order, and helps doctors in
providing a diagnosis (Obermeyer, 2019), traders in
trading (Aggarwal, 2021), and armies in using killer
robots to achieve their goals. Numerous areas seem
to be under the yoke of AI capacities, including
logistics, health, education, research, defense,
banking, agri-food, culture, leisure, social, and
professional networks. Since algorithms are at the
heart of human relations and exchanges, questions
relating to uses (and misuses), benefits (and limits)
have become crucial. Faced with this surge of
artificial intelligence, the ethical question is urgent.
Artificial intelligence covers a wide range of
research and computer applications, including
machine learning, computer vision, knowledge
representation, language processing, and decision
support. With the notion of AI, we propose that it is
not the algorithm alone that can be problematic, but
rather its embeddedness in a system, a set of actors,
power norms, and complexity (Neyland, 2016;
Seaver, 2017). Thus, in this contribution, we
consider AI not as purely technical objects, but as
technical systems embedded in culture(s) and which
can be seen, used, and approached from different
perspectives (legal, technological, cultural, social). It
is a technical construction that is both deeply social
and cultural. It does not escape social and cultural
construction like all other tools, neither in its
development, nor in the inputs (data), nor in the
interpretation of the results (output), nor in its use.
Some authors (Hamet & Michel, 2018) have
shown that the ethical questions that arise in
information systems are specific to this field, in the
sense that the ethical dilemmas posed by AIs do not
arise, or do not arise in the same way in other fields.
However, even if some questions appear to be
specific to AI, it seems necessary to have a
theoretical framework to study them and provide
Gerbaix, S., Michel, S. and Bidan, M.
Coping with Artificial Intelligence Ethical Dilemma and Ethical Position Choices?.
DOI: 10.5220/0012726000003690
Paper published under CC license (CC BY-NC-ND 4.0)
In Proceedings of the 26th International Conference on Enterprise Information Systems (ICEIS 2024) - Volume 1, pages 382-388
ISBN: 978-989-758-692-7; ISSN: 2184-4992
Proceedings Copyright © 2024 by SCITEPRESS – Science and Technology Publications, Lda.
Numerous articles today attempt to analyze the
main ethical problems linked to AI by proposing, for
the most part, courses of action, standards, and codes
to be put in place (Mittelstadt et al., 2016; Berreby et
al., 2017; Anderson et Anderson, 2018; Yu et al.,
2018; Buhmann et al., 2020; Tsamados et al., 2021).
However, we note that these studies are not part of a
theoretical ethical current. We would like to show
that when faced with an ethical problem raised by
AI, the recommendations and decisions may not be
the same depending on the initial theoretical ethical
position. To this end, we will first present two main
theoretical ethical currents that we consider
important: Kant's deontological ethics and Hegel's
consequentialist ethics. A synthesis of the ethical
questions raised by AI is then presented. In a third
point, we confront them with the two theoretical
ethical positions. We then show that the decisions to
be taken may differ. Thus, we show that the norms
to be put in place in the face of the ethical problems
of AI will only be effective if a theoretical ethical
position is chosen ex ante.
2.1 AI and Responsibility
Here we need to point to De George (2009) who
highlights an unfortunate trend of giving up the
assignment of responsibilities when it comes to
Information Systems or algorithms, a renunciation
that comes from two sources. The first is what De
George (2009) refers to as the myth of amoral
Information and communication technology (ICT).
This myth amounts to limiting ICT to their technical
aspect and to considering that machines cannot,
obviously, be held responsible for the consequences
of their use. There is an avoidance of responsibility
through “the computer said so” type of denial
(Karppi, 2018). The second source of dilution of
responsibility comes from a split, in decision-
making, between the developers, who believe they
are fulfilling their duty by strictly respecting the
requests they receive, and the management, who
does not consider itself responsible for the
technology-related flaws. However, these developers
are never face to face with the stakeholders which
creates a new ethical problem linked to the distance
in decision-making (Rubel et al., 2019). This issue
of responsibility is very current, particularly with the
development of AI (Martin, 2019; Yu et al., 2019,
Wieringa, 2020).
Faced with this dilution of responsibilities, some
authors (Chatterjee et al., 2009; Light and McGrath,
2010) call for the adoption of a disclosing ethic
approach considering the algorithms as an actor in
its own right, and aiming to reveal the ethical
questions posed by their design, and not just their
use. Machines should be able to make ethical
decisions using ethical frameworks (Anderson et
Anderson, 2018). Davison (2000), Stahl, (2004) or
Reddy (2019) note the importance of assigning
responsibility, especially given the seriousness of the
potential consequences of an error. Some authors
suggest identifying levels of responsibility
(individual, hierarchical, collective and
organizational) or suggest assigning responsibilities
according to the role of stakeholders, according to
their decision-making (Chander, 2017; Kraemer et
al., 2011; Torresen, 2018). Others (Chander, 2017;
Kemper and Kolkman, 2019; Buhmann et al., 2020)
stresses that an organization should take
responsibility for their algorithms regardless of how
opaque they are (Malhotra et al., 2018).
2.2 AI and Bias
The algorithms that work on language are fed by
billions of data (texts, images, videos...) steeped in
our cultures. When a system becomes expert enough
to simulate a conversation and produce language that
sounds natural, it relies on the commonly accepted
ideas of the society it is addressing. Not surprisingly,
it reproduces ethically questionable historical
cultural representations. Also, for example, from the
2000s, a new and important question emerged in the
literature, under the term of social sorting (Hamet
and Michel, 2018). This trend, stemming from
surveillance studies, examines the risk that the
analysis of personal data will lead to segregation or
discrimination. Employers however no longer
hesitate to use tri- social algorithms to recruit. Banks
and insurance companies conduct scoring policies
based on these social sorting technologies. Real
estate agencies and social landlords also carry out de
facto discrimination by establishing choices of
allocation or not, using housing algorithms which
are based on the last name, on first name, on
address, on the mastery of the French language, etc.
If people tend to recruit fewer women, the algorithm
will implicitly reproduce this trend. This is a crucial
issue with AI and deep learning algorithms with
which a machine is able to learn through its own
data processing. Thus, biases in AI, based on masses
Coping with Artificial Intelligence Ethical Dilemma and Ethical Position Choices?
of previous data, are likely to result in discrimination
and exclusion, on a scaled-down scale by reproducing
prejudices incorporated in unprocessed data. AI,
combined with the processing of massive data, even
induces an autonomous functioning of processing
social characteristics. It is the self-learning AI itself
that produces and reproduces “social sorting”. In this
sense, AI did not invent discrimination, but they
participate in this movement, by reproducing it, even
by intensifying it.
2.3 AI and Transparency
Due to the multiplicity of actors, stakeholders and
links, algorithms, are increasingly opaque. However,
it is necessary to open the black box of algorithms,
to offer better transparency (Guidotti et al., 2020;
Buhmann et al., 2020) and then possible traceability.
As Turilli and Floridi (2009) note, transparency is not
an “ethical principle in itself but a pro-ethical
condition for enabling or impairing other ethical
practices or principles” (p.105). This is why it is
important to distinguish between the different factors
that may hinder transparency of algorithms, identify
their cause (Diakopoulos and Koliska, 2017). This
transparency is also a request from individuals in
relation to the protection of their privacy, the
confidentiality of stored data and surveillance. This
search for transparency involves being able to clarify
and decipher multiple involved and intertwined
processes, processes such as the collection of data and
their validation, the action and decision-making
processes of the chain of actors in the realization of
algorithms (initial decision, project funders, AI
researchers, analysts, developers), decision-makers
and funders of uses (for example, for health systems
or autonomous cars) (Martin, 2019). Also, the ethical
problem of the transparency of algorithms can lead us
to broader ethical questions. For example, as early as
the 1960s, some worried that advances in science and
technology could threaten the functioning of
democracy, because only a few experts were able to
truly understand complex technologies (Habermas,
1970). The technocracy hypothesis arose through
portraying a future society where experts would make
decisions based on their own value system, offering
their best solution. Then, after the experts, the AI
could also lead to a form of algorithmic
governmentality based on the statistical processing of
data communicated (voluntarily or without their
knowledge) by citizens, in particular thanks to
sensors connected, leading to a pre-elaboration of
the collective decisions thus elaborated by the
MIT has initiated the Moral Machine Project, which
leverages the collective wisdom of crowds to devise
solutions to ethical dilemmas related to autonomous
vehicles controlled by AI that could potentially
cause harm to pedestrians and/or passengers in case
of malfunction. While the wisdom of the crowd is
used as an ethical reference in this project, one could
alternatively appeal to the categorical imperative, the
concept of virtue, the consequences of actions, or
other norms. Therefore, we believe it is essential to
present the major ethical theories at hand to properly
address ethical issues (Hamet and Michel, 2018).
Hence, we will discuss two primary ethical
movements, namely Kant's deontological ethics and
Hegel's consequentialist ethics. While we
acknowledge that this is not an exhaustive list, these
two ethical currents can demonstrate the significant
challenges encountered in addressing the ethical
problems posed by algorithms.
3.1 Kant’s Deontological Ethics
During the 18th century, Kant, in his work Critique
of Practical Reason (1788), attempted to answer the
question, "What shall I do?" This is Kant's second
question (after "what can I know," which is dealt
with in the Critique of Pure Reason), and the third is
"what can I hope for," which is dealt with in the
Critique of the Faculty of Judgement. At the
beginning of the Critique of Practical Reason, Kant
asks whether it is possible to construct a moral
rationalism, a supreme principle of rationality which
would be the moral law. He transforms the question
"what shall I do" into "what are the supreme
principles of morality." In this work, he promotes
the autonomy of the will. It is no longer up to the
human will to align itself with the good as an
external standard, but rather it is up to the will to
define the good as that which is universally
desirable. Kant sets out to find "practical laws," i.e.,
"objective principles valid for the will of every
reasonable being." To be objective and universal,
moral obligation must be expressed by a formal
principle, an a priori, universal, and necessary
criterion. It is therefore the autonomy of the will that
must constitute the sole principle of all moral laws
and duties. The fundamental law of pure practical
reason can be stated as follows: "Act only according
to the maxim that you can will at the same time that
it becomes a universal law."
ICEIS 2024 - 26th International Conference on Enterprise Information Systems
3.2 Hegel’s Ethics
Unlike Kant, Hegel relies on the experience of
human beings and aims to understand what makes
morality, rather than looking for where it should
come from.
3.2.1 Hegel’s Critique of the Kant’s Moral
In The Principles of Legal Philosophy, in its second
part, Hegel addresses several criticisms of Kant’s
ethical principles and his moral law. In short:
First, the moral law is dolorist: it always
presupposes suffering in its execution, in the sense
that one must do with aversion what duty dictates.
According to Hegel, one can do one’s duty with
Second, circumstantial context is not taken into
account. This means that moral duties are
conditioned by the situation. Duties are not
immediate; they require reflection. Most norms
certainly work as a habit, which frees the mind,
allowing one to think about when it is necessary to
take the situation into account. There is a hierarchy
of norms. In the famous example of lying, for Hegel,
lying is seen as the least bad solution in a given
Third, the moral law presents a theory that is
ahistorical. This is the most problematic point for
Hegel: Kant does not take into account the evolution
of norms and society. Hegel would say that morality
has something provisional, that it evolves over time
(referring to Descartes and his provisional morality).
So, Hegel proposes a theory of action and moral
imputation: what makes an individual’s morality is
their actions. What makes the morality of an action
is norms and morality. What makes it possible to
impute the action to the individual is ethics.
3.2.2 Hegel’s Concept of Ethical Life
In the third and final part of The Principles of the
Philosophy of Law, Hegel develops his concept of
ethical life, which encompasses the family, civil
society, the state, and world history (§142 to §360).
Ethical life is the set of norms that emanate from the
institutions that regulate the life of people: the
family, the corporations, and the state, which is the
basic institution and the condition of possibility for
other institutions. In ethical life, there are
obligations, which today are called norms.
4.1 Lack of Ethical Positioning in the
Recent literature on these ethical problems has
proposed some interesting avenues for reflection and
action. For example, on the issue of fairness, some
authors recommend to develop a sociotechnical
framework to address and improve the fairness of
algorithms (Edwards and Veale, 2017; Selbst et al.,
2019; Wong, 2019; Abebe et al., 2020). Concerning
the issue of responsibility, for example, Shah (2017)
analysis points out that the risk of some stakeholders
failing to meet their responsibilities can be
addressed, for example, by creating separate bodies
for the ethical oversight of algorithms. However,
others show that expecting a single oversight body,
such as a research ethics committee or institutional
review board, to be 'solely responsible for ensuring
ethical rigor, utility and probity is unrealistic
(Lipworth et al., 2017). Concerning the issue of
transparency, for example, Gebru et al. (2020)
propose that the transparency constraints posed by
AI can be resolved, in part, by using standard
documentation procedures similar to those deployed
in the electronics industry. In addition, another
recent approach is the use of technical tools to test
and audit AI and decision making. This involves
checking algorithms for negative trends, such as
unfair discrimination, and auditing a prediction or
decision track in detail, (Weller, 2019; Malhotra et
al., 2018; Brundage et al. 2020). We do not question
these courses of action, but we insist that in order to
choose one of the proposed courses of action, it is
first necessary to be part of an ethical current, as the
answers differ, as we will show in the following.
4.2 Different Answers to Ethical
Questions Depending on the
Positions Chosen
AI are loaded with values (Brey et Søraker, 2009;
Kraemer et al., 2011; Mittelstadt et al., 2016;
Tsamados et al, 2021) contradicting the myth of the
long-lived neutral algorithm. Indeed, any algorithm
involves a multitude of decisions, whether
classification, prioritization, display, filtering,
learning. However, the choice of filtering
techniques, classified data, the options chosen may
well reflect a certain understanding of the world. AI
Coping with Artificial Intelligence Ethical Dilemma and Ethical Position Choices?
are nothing more than ideas, opinions formalized in
code, and in no way escape the subjectivity of
developers, managers, contractors or society.
Algorithmic coding contains a wide spectrum of
standards that can range from moral injunctions to
unconsciously integrated norms. We want to show
that choosing these solutions without first choosing
an ethical position is a bad approach. Indeed, the
solution chosen can only depend on the ethical
position initially taken. Responses to these ethical
problems will differ depending on whether one
adheres to a deontological Kantian ethics or a
consequentialist Hegelian ethics.
With regards to the ethical theme of biases,
according to Kantian’s ethics, AI in its entirety must
respond to a categorical imperative of non-
discrimination and justice. AI must not discriminate
or reproduce discriminations. This could be one of
the first categorical imperatives assigned to this
ethical theme. It would then be a question of
knowing how to put in place this ethic approach to
duty. Consequentialist’s, on the other hand, will be
interested in the consequences of these biases and
discriminations with regards to the well-being of
society. Some discrimination can be accepted. They
may consider that discrimination against a minority
is not detrimental to social well-being and therefore
that it is acceptable.
With regard to the theme of responsibility, we
find the two positions defined above. For Kantians
Ethics, faced with the tram dilemma type (thought
experiment which offers a person a choice of action,
knowing that if he acts, his gesture will benefit a
group of people, but will harm a person), dilemma
which is very similar to the dilemmas posed to the
autonomous car, it is not possible that a person will
be killed because of my action. For consequentialists
ethics, faced with this dilemma, between one or
more people, between young or old, depending on
the social utility of the person, we can make a
choice. Choosing to sacrifice one person to save five
can be understood according to this morality.
Finally, regarding the theme of transparency, the
ethical imperative leads us to consider the initiator
of the standard - whether it's the machine or the
human. Is it ethical to delegate decision-making to a
machine? Can a machine have good intentions?
According to Hegelian ethics, we must identify the
consequences of a loss of control and human
mastery. The time, financial, and fatigue savings
generated by delegating decision-making to a
machine can justify certain costs, such as the loss of
certain degrees of freedom and generalized
surveillance. On the other hand, Kantian ethics
emphasizes individual reflection. In order to create
contextualized ethical norms, Hegel proposes three
levels of action: family, civil society, and state. We
can summarize our approach in the following table1:
Table 1.
AI Ethical questions -
Synthetic formulation
Questions raised according
to ethical theorical positions
Questioning the
integration of societal
values in AI. The rules
included in the
algorithms are not
neutral and convey
conscious and
unconscious values of
the developers, the
organizations, the
Kantian ethics: questioning
of Universal moral rules to be
integrated into algorithms
(absence of discrimination)
and to be imposed on society.
Fairness couldn’t’ be
accepted, never.
Hegel ethics and
consequentialists current:
questioning the consequences
of the biases generated. Some
on theses could be accepted.
Amoral AI;
Identification of
responsibilities? Dilution
of responsibilities;
Revealing ethics and the
system as an actor in its
own right
Kantian ethics: questioning
a “categorical imperative”
(the autonomous car must not
kill, for example).
Responsibility must always
be accurately attributed
Hegel ethics and
consequentialists current:
questioning the consequences
of ethical dilemmas. A trade-
off between different
responsibilities is possible
Transparency of the
process: data/algorithmic
processing/ effects
Kantian ethics:
questioning the initiator of
the categorical imperative
(Human or algorithm)
Hegel ethics and
Consequentialists current:
questioning the
of the
excesses of the loss of
human control
In summary, in the proliferation of ethical problems
posed by AI, three paths of ethical questioning
related to AI have been identified: the question
of algorithmic biases, the question of responsibility,
ICEIS 2024 - 26th International Conference on Enterprise Information Systems
the question of transparency. We show that
these themes need to be according to the ethical
theory in which they are questioned, (Deontological,
The contributions of this article are therefore
twofold. First, we have consolidated the literature on
the topic of the ethics of AI, by putting forward a
synthesis of the main ethical questions. This
synthesis sheds light on this confused field. We have
also shown, with support of the main ethical
theoretical currents, that these questions can lead to
very different answers. Thus, in terms of practical
ethics, it is not enough for organizations to identify
ethical issues and propose ethical charters. The first
task, which underlies the whole, is indeed to choose
an ethical theoretical position.
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