## 1. Challenges to moral responsibility



Moral responsibility is about human action and its intentions and
consequences (Fisher 1999, Eshleman 2016, Talbert 2022). Generally
speaking a person or a group of people is morally responsible when
their voluntary actions have morally significant outcomes that would
make it appropriate to blame or praise them. Thus, we may consider it
a person's moral responsibility to jump in the water and try to
rescue another person, when she sees that person drowning. If she
manages to pull the person from the water we are likely to praise her,
whereas if she refuses to help we may blame her. Ascribing moral
responsibility establishes a link between a person or a group of
people and someone or something affected by the actions of this person
or group. The person or group that performs the action and causes
something to happen is often referred to as the *agent*. The
person, group or thing that is affected by the action is referred to
as the *patient*. Establishing a link in terms of moral
responsibility between the agent and the patient can be done both
retrospectively as well as prospectively. That is, sometimes
ascriptions of responsibility involve giving an account of who was at
fault for an accident and who should be punished. It can also be about
prospectively determining the obligations and duties a person has to
fulfill in the future and what she ought to do.



However, the circumstances under which it is appropriate to ascribe
moral responsibility are not always clear. On the one hand the concept
has varying meanings and debates continue on what sets moral
responsibility apart from other kinds of responsibility (Hart 1968,
Talbert 2022, Tigard 2021a). The concept is intertwined and sometimes
overlaps with notions of accountability, liability, blameworthiness,
role-responsibility and causality. Opinions also differ on which
conditions warrant the attribution of moral responsibility; whether it
requires an agent with free will or not and whether humans are the
only entities to which moral responsibility can be attributed (see the
entry on
 moral responsibility).
 



On the other hand, it can be difficult to establish a direct link
between the agent and the patient because of the complexity involved
in human activity, in particular in today's technological
society. Individuals and institutions generally act with and in
*sociotechnical* systems in which tasks are distributed among
human and technological components, which mutually affect each other
in different ways depending on the context (Bijker, Hughes and Pinch
1987, Felt et al. 2016). Increasingly complex technologies can
exacerbate the difficulty of identifying who or what is
'responsible'. When something goes wrong, a retrospective
account of what happened is expected and the more complex the system,
the more challenging is the task of ascribing responsibility (Johnson
and Powers 2005). Indeed, Matthias argues that there is a growing
'responsibility gap': the more complex computer
technologies become and the less human beings can directly control or
intervene in the behavior of these technologies, the less we can
reasonably hold human beings responsible for these technologies
(Matthias, 2004).



The increasing pervasiveness of computer technologies poses various
challenges to figuring out what moral responsibility entails and how
it should be properly ascribed. To explain how computing complicates
the ascription of responsibility we have to consider the conditions
under which it makes sense to hold someone responsible. Despite the
ongoing philosophical debates on the issue, most analysis of moral
responsibility share at least the following three conditions (Eshleman
2016; Jonas 1984):


1. There should be a causal connection between the person and the
outcome of actions. A person is usually only held responsible if they
had some control over the outcome of events.
2. The subject has to have knowledge of and be able to consider the
possible consequences of her actions. We tend to excuse someone from
blame if they could not have known that their actions would lead to a
harmful event.
3. The subject has to be able to freely choose to act in certain way.
That is, it does not make sense to hold someone responsible for a
harmful event if her actions were completely determined by outside
forces.



A closer look at these three conditions shows that computing can
complicate the applicability of each of these conditions.


### 1.1 Causal contribution



In order for a person to be held morally responsible for a particular
event, she has to be able to exert some kind of influence on that
event. It does not make sense to blame someone for an accident if she
could not have avoided it by acting differently or if she had no
control over the events leading up to the accident.



However, computer technologies can obscure the causal connections
between a person's actions and the eventual consequences.
Tracing the sequence of events that led to a computer-related
catastrophic incident, such as a plane crash, usually leads in many
directions, as such incidents are seldom the result of a single error
or mishap. Technological accidents are commonly the product of an
accumulation of mistakes, misunderstanding or negligent behavior of
various individuals involved in the development, use and maintenance
of computer systems, including designers, engineers, technicians,
regulators, managers, users, manufacturers, sellers, resellers and
even policy makers.



The involvement of multiple actors in the development and deployment
of technologies gives rise to what is known as the problem of
'many hands': it is difficult to determine who was
responsible for what when multiple individuals contributed to the
outcome of events (Jonas 1984; Friedman 1990; Nissenbaum 1994; van de
Poel et al. 2015). One classic example of the problem of many hands in
computing is the case of the malfunctioning radiation treatment
machine Therac-25 (Leveson and Turner 1993; Leveson 1995). This
computer-controlled machine was designed for the radiation treatment
of cancer patients as well as for X-rays. During a two-year period in
the 1980s the machine massively overdosed six patients, contributing
to the eventual death of three of them. These incidents were the
result of the combination of a number of factors, including software
errors, inadequate testing and quality assurance, exaggerated claims
about the reliability, bad interface design, overconfidence in
software design, and inadequate investigation or follow-up on accident
reports. Nevertheless, in their analysis of the events Leveson and
Turner conclude that it is hard to place the blame on a single person.
The actions or negligence of all those involved might not have proven
fatal were it not for the other contributing events. A more recent
example of the problem of many hands is the crash of two 737 MAX
passenger aircraft in late 2018 and early 2019. These crashes led to
mulitple investigations that highlighted various factors that
contributed to the tragic outcome, include design and human errors as
well as organizational culture and lack of training (Heckert et al
2020). This is not to say that there is no moral responsibility in
these case (Nissenbaum 1994; Gotterbarn 2001; Coeckelbergh 2012;
Floridi 2013, Santonio De Sio et al, 2021), as many actors could have
acted differently, but it makes it more difficult to retrospectively
identify the appropriate person that can be called upon to answer and
make amends for the outcome.



Adding to the problem of many hands is the temporal and physical
distance that computing creates between a person and the consequences
of her actions, as this distance can blur the causal connection
between actions and events (Friedman 1990). Computational technologies
extend the reach of human activity through time and space. With the
help of social media and communication technologies people can
interact with others on the other side of the world. Satellites and
advanced communication technologies allow pilots to fly a remotely
controlled drone from their ground-control station half way across the
world. These technologies enable people to act over greater distances,
but this remoteness can dissociate the original actions from its
eventual consequences (Waelbers 2009; Polder-Verkiel 2012;
Coeckelbergh 2013). When a person uses a technological artifact to
perform an action thousands of miles a way, that person might not know
the people that will be affected and she might not directly, or only
partially, experience the consequences. This can reduce the sense of
responsibility the person feels and it may interfere with her ability
to fully comprehend the significance of her actions. Similarly, the
designers of an automated decision-making system determine ahead of
time how decisions should be made, but they will rarely see how these
decisions will impact the individuals they affect. Their original
actions in programming the system may have effects on people years
later.



The problem of many hands and the distancing effects of the use of
technology illustrate the mediating role of technological artifacts in
the confusion about moral responsibility. Technological artifacts
bring together the various different intentions of their creators and
users. People create and deploy technologies with the objective of
producing some effect in the world. Software developers develop an
automated content moderation tool, often at the request of their
managers or clients, with the aim of shielding particular content from
users and influencing what these users can or cannot read. The
software has inscribed in its design the various intentions of the
developers, managers and clients; it is poised to behave, given a
particular input, according to their ideas about which information is
appropriate (Friedman 1997, Gorwa, Binns, & Katzenbach 2020).
Moral responsibility can therefore not be attributed without looking
at the causal efficacy of these artifacts and how they constrain and
enable particular human activities.



However, although technological artefacts may influence and shape
human action, they do not determine it. They are not isolated
instruments that mean and work the same regardless of why, by whom,
and in what context they are used; they have interpretive flexibility
(Bijker et al. 1987) or multistability (Ihde
 1990).[2]
 Although the design of the technology provides a set of conditions
for action, the form and meaning of these actions is the result of how
human agents choose to use these technologies in particular contexts.
People often use technologies in ways unforeseen by their designers.
This interpretive flexibility makes it difficult for designers to
anticipate all the possible outcomes of the use of their technologies.
The mediating role of computer technologies complicates the effort of
retrospectively tracing back the causal connection between actions and
outcomes, but it also complicates forward-looking responsibility.


### 1.2 Considering the consequences



As computer technologies shape how people perceive and experience the
world, they affect the second condition for attributing moral
responsibility. In order to make appropriate decisions a person has to
be able to consider and deliberate about the consequences of their
actions. They have to be aware of the possible risks or harms that
their actions might cause. It is unfair to hold someone responsible
for something if they could not have reasonably known that their
actions might lead to harm.



On the one hand, computer technologies can help users to think through
what their actions or choices may lead to. They help the user to
capture, store, organize and analyze data and information (Zuboff
1982). For example, one often-named advantage of remote-controlled
robots used by the armed forces or rescue workers is that they enable
their operators to acquire information that would not be able
available without them. They allow their operators to look
"beyond the next hill" or "around the next
corner" and they can thus help operators to reflect on what the
consequences of particular tactical decisions might be (US Department
of Defense 2009). Similarly, data analysis tools can find patterns in
large volumes of data that human data analysts cannot manually process
(Boyd and Crawford 2012).



On the other hand the use of computers can constrain the ability of
users to understand or consider the outcomes of their actions. These
complex technologies, which are never fully free from errors,
increasingly hide the automated processes behind the interface (Van
den Hoven 2002). An example that illustrates how computer technologies
can limit understanding of the outcomes are the controversial risk
assessment tools used by judges in several states in the U.S. for
parole decisions and sentencing. In 2016 a civil society organization
found, based on an analysis of the risk scores of 7000 defendants
produced by one particular algorithm, that the scores poorly reflected
the actual recidivism rate and seemed to have a racial bias (Angwin et
al. 2016). Regardless of whether its findings were correct or not,
what is particularly relevant here is that the investigation also
showed that judges did not have a full understanding of how the
probabilities were calculated, in part because the algorithm was
proprietary. The judges were basing their sentencing on the suggestion
of an algorithm that they did not fully understand. This is the case
for most computer technologies today. Users only see part of the many
computations that a computer performs and are for the most part
unaware of how it performs them; they usually only have a partial
understanding of the assumptions, models and theories on which the
information on their computer screen is based. The increasing
complexity of computer systems and their reliance on opaque machine
learning algorithms makes it even more difficult to understand what is
happening behind the interface (Pasquale 2015, Diakopoulos 2020). 



The opacity of many computer systems can get in the way of assessing
the validity and relevance of the information and can prevent a user
from making appropriate decisions. People have a tendency to either
rely too much or not enough on the accuracy automated systems
(Cummings 2004; Parasuraman & Riley 1997). This tendency is called
*automation bias*. A person's ability to act responsibly,
for example, can suffer when she distrust the automation as result of
a high rate of false alarms. In the Therac 25 case, one of the
machine's operators testified that she had become used to the
many cryptic error messages the machine gave and most did not involve
patient safety (Leveson and Turner 1993, p.24). She tended to ignore
them and therefore failed to notice when the machine was set to
overdose a patient. Too much reliance on automated systems can have
equally disastrous consequences. In 1988 the missile cruiser U.S.S.
Vincennes shot down an Iranian civilian jet airliner, killing all 290
passengers onboard, after it mistakenly identified the airliner as an
attacking military aircraft (Gray 1997). The cruiser was equipped with
an Aegis defensive system that could automatically track and target
incoming missiles and enemy aircrafts. Analyses of the events leading
up to the incident showed that overconfidence in the abilities of the
Aegis system prevented others from intervening when they could have.
Two other warships nearby had correctly identified the aircraft as
civilian. Yet, they did not dispute the Vincennes'
identification of the aircraft as a military aircraft. In a later
explanation Lt. Richard Thomas of one of the nearby ships stated,
"We called her Robocruiser... she always seemed to have a
picture... She always seemed to be telling everybody to get on or
off the link as though her picture was better" (as quoted in
Gray 1997, p. 34). The captains of both ships thought that the
sophisticated Aegis system provided the crew of Vincennes with
information they did not have.



Considering the possible consequences of one's actions is
further complicated as computer technologies make it possible for
humans to do things that they could not do before. Several decades
ago, the philosopher Ladd pointed out, "[C]omputer technology
has created new modes of conduct and new social institutions, new
vices and new virtues, new ways of helping and new ways of abusing
other people" (Ladd 1989, p. 210-11). Computer
technologies of today have had a similar effect. The social or legal
conventions that govern what we can do with these technologies take
some time to emerge and the initial absence of these conventions
contributes to confusion about responsibilities (Taddeo and Floridi
2015). For example, the ability for users to upload and share text,
videos and images publicly on the Internet raised a whole set of
questions about who is responsible for the content of the uploaded
material. Such questions were at the heart of the debate about the
conviction of three Google executives in Italy for a violation of the
data protection act (Sartor and Viola de Azevedo Cunha 2010). The case
concerned a video on YouTube of four students assaulting a disabled
person. In response to a request by the Italian Postal Police, Google,
as owner of YouTube, took the video down two months after the students
uploaded it. The judge, nonetheless, ruled that Google was criminally
liable for processing the video without taking adequate precautionary
measures to avoid privacy violations. The judge also held Google
liable for failing to adequately inform the students, who uploaded the
videos, of their data protection obligations (p. 367). In the ensuing
debate about the verdict, those critical of the ruling insisted that
it threatened the freedom of expression on the Internet and it sets a
dangerous precedent that can be used by authoritarian regimes to
justify web censorship (see also Singel 2010). Moreover, they claimed
that platform providers could not be held responsible for the actions
of their users, as they could not realistically approve every upload
and it was not their job to censure. Yet, others instead argued that
it would be immoral for Google to be exempt from liability for the
damage that others suffered due to Google's profitable
commercial activity. Cases like this one show that in the confusion
about the possibilities and limitations of new technologies it can be
difficult to determine one's moral obligations to others.



The lack of experience with new technological innovations can also
affect what counts as negligent use of the technology. In order to
operate a new computer system, users typically have to go through a
process of training and familiarization with the system. It requires
skill and experience to understand and imagine how the system will
behave (Coeckelbergh and Wackers 2007). Friedman describes the case of
a programmer who invented and was experimenting with a 'computer
worm', a piece of code that can replicate itself. At the time
this was a relatively new computational entity (1990). The programmer
released the worm on the Internet, but the experiment quickly got out
of the control when the code replicated much faster than he had
expected (see also Denning 1989). Today we would not find this a
satisfactory excuse, familiar as we have become with computer worms,
viruses and other forms of malware. However, Friedman poses the
question of whether the programmer really acted in a negligent way if
the consequences were truly unanticipated. Does the computer
community's lack of experience with a particular type of
computational entity influence what we judge to be negligent
behavior?


### 1.3 Free to act



The freedom to act is probably the most important condition for
attributing moral responsibility and also one of the most contested
(Talbert 2022). We tend to excuse people from moral blame if they had
no other choice but to act in the way that they did. We typically do
not hold people responsible if they were coerced or forced to take
particular actions. In moral philosophy, the freedom to act can also
mean that a person has free will or autonomy (Fisher 1999). Someone
can be held morally responsible because she acts on the basis of her
own authentic thoughts and motivations and has the capacity to control
her behavior (Johnson 2001). Note that this conception of autonomy is
different from the way the term 'autonomy' is often used
in computer science, where it tends to refer to the ability of a robot
or computer system to independently perform (i.e. without the
'human in the loop') complex tasks in unpredictable
environments for extended periods of time (Noorman 2009, Zerilli et al
2021).



Nevertheless, there is little consensus on what capacities human
beings have, that other entities do not have, which enables them to
act freely (see the entries on
 free will,
 autonomy in moral and political philosophy,
 personal autonomy
 and
 compatibilism).
 Does it require rationality, emotion, intentionality or cognition?
Indeed, one important debate in moral philosophy centers on the
question of whether human beings really have autonomy or free will?
And, if not, can moral responsibility still be attributed (Talbert
2022)?



In practice, attributing autonomy or free will to humans on the basis
of the fulfillment of a set of conditions turns out to be a less than
straightforward endeavor. We attribute autonomy to persons in degrees.
An adult is generally considered to be more autonomous than a child.
As individuals in a society our autonomy is thought to vary because we
are manipulated, controlled or influenced by forces outside of
ourselves, such as by our parents or through peer pressure. Moreover,
internal physical or psychological influences, such as addictions or
mental problems, are perceived as further constraining the autonomy of
a person.



Computing, like other technologies, adds an additional layer of
complexity to determining whether someone is free to act, as it
affects the choices that humans have and how they make them. One of
the biggest application areas of computing is the automation of
decision-making processes and control. Automation can help to
centralize and increase control over multiple processes for those in
charge, while it limits the discretionary power of human operators on
the lower-end of the decision-making chain. An example is provided by
the automation of decision-making in public administration (Bovens and
Zouridis 2002). Large public sector organizations have over the last
decades progressively standardized and formalized their production
processes. In a number of countries, the process of issuing decisions
about (student) loans, social benefits, speeding tickets or tax
returns is carried out to a significant extent by computer systems.
This has reduced the scope of the administrative discretion that many
officials, such as tax inspectors, welfare workers, and policy
officers, have in deciding how to apply formal policy rules in
individual cases (Eubanks 2018). In some cases, citizens no longer
interact with officials that have significant responsibility in
applying their knowledge of the rules and regulations to decide what
is appropriate (e.g., would it be better to let someone off with a
warning or is a speeding ticket required?). Rather, decisions are
pre-programmed in the algorithms that apply the same measures and
rules regardless of the person or the context (e.g., a speeding camera
does not care about the context or personal circumstances), and the
human beings that citizens do interact with have little opportunity to
interrogate or change decisions (Dignum 2020). Responsibility for
decisions made, in these cases, has moved from 'street-level
bureaucrats' to the 'system-level bureaucrats', such
as managers and computer experts, that decide on how to convert policy
and legal frameworks into algorithms and decision-trees (Bovens and
Zouridis 2002).



The automation of bureaucratic processes illustrates that some
computer technologies are intentionally designed to limit the
discretion of some human beings. An example is the anti-alcohol lock
that is already in use in a number of countries, including the USA,
Canada, Sweden and the UK. It requires the driver to pass a breathing
test before she can start the car. This technology forces a particular
kind of action and leaves the driver with hardly any choice. Other
technologies might have a more subtle way of steering behavior, by
either persuading or nudging users (Verbeek 2016). For example, the
onboard computer devices in some cars that show, in real-time,
information about fuel consumption can encourage the driver to
optimize fuel efficiency. Such technologies are designed with the
explicit aim of making humans behave responsibly by limiting their
options or persuading them to choose in a certain way.



Not all these technologies are designed to stimulate morally good
behavior. Yeung notes that these kinds of decision-guidance techniques
have become a key element of current day Big-Data analytic techniques,
as used on social media and in advertising. She argues that these
'hyper nudges' are extremely powerful techniques to
manipulate the behaviour of internet users and users of Internet of
Things (IoT) devices due to their networked, continuously updated,
dynamic and pervasive nature. As they gather data from a wide range of
sources about users to continuously make predictions in real-time
about the habits and preferences of users, they can target
advertisement, information and price incentives to gently and
unobtrusively nudge these users in directions preferred by the those
that control the algorithms (Yeung, 2017). When these nudges are
hardly noticeable and have a powerful effect, one can wonder how
autonomous the decision making of these users is. This is the case,
for example, with dark patterns, which use interfaces on websites or
apps that are designed to trick users to do things that did not intend
to do, such as purchasing additional expensive insurance (Ravenscraft
2020). 



Verbeek notes that critics of the idea of intentionally developing
technology to enforce morally desirable behavior have argued that it
jettisons the democratic principles of our society and threatens human
dignity. They argue that it deprives humans of their ability and
rights to make deliberate decisions and to act voluntarily. In
addition, critics have claimed that if humans are not acting freely,
their actions cannot be considered moral. These objections can be
countered, as Verbeek argues, by pointing to the rules, norms,
regulations and a host of technological artifacts that already set
conditions for actions that humans are able or allowed to perform.
Moreover, he notes, technological artifacts, as active mediators,
affect the actions and experiences of humans, but they do not
determine them. Some people have creatively circumvented the strict
morality of earlier versions of the alcohol lock by having an air pump
in the car (Vidal 2004). Nevertheless, these critiques underline the
issues at stake in automating decision-making processes: computing can
set constraints on the freedom a person has to act and thus affects
the extent to which she can be held morally responsible.



The challenges that computer technologies present with regard to the
conditions for ascribing responsibility indicate the limitations of
conventional ethical frameworks in dealing with the question of moral
responsibility. Traditional models of moral responsibility seem to be
developed for the kinds of actions performed by an individual that
have directly visible consequences (Waelbers 2009, Coeckelbergh
2009). However, in today's society attributions of
responsibility to an individual or a group of individuals are
intertwined with the artifacts with which they interact as well as
with intentions and actions of other human agents that these artifacts
mediate. Acting with computer technologies may require a different
kind of analysis of who can be held responsible and what it means to
be morally responsible. Below I discuss two ways in which scholars
have taken up this challenge: 1) reconsidering what it means to be a
moral agent and 2) reconsidering the concept of moral
responsibility.


## 2. Can computers be moral agents?



Moral responsibility is generally attributed to moral agents and, at
least in Western philosophical traditions, moral agency has been a
concept exclusively reserved for human beings (Johnson 2001; Doorn and
van de Poel 2012). Unlike animals or natural disasters, human beings
in these traditions can be the originators of morally significant
actions, as they can freely choose to act in one way rather than
another way and deliberate about the consequences of this choice. And,
although some people are inclined to anthropomorphize computers and
treat them as if they were moral agents (Reeves and Nass 1996; Nass
and Moon 2000; Rosenthal-von der Putten 2013), most
philosophers agree that current computer technologies should not be
called moral agents, if that would mean that they could be held
morally responsible. However, the limitations of traditional ethical
vocabularies in thinking about the moral dimensions of computing have
led some authors to rethink the concept of moral agency. It should be
noted that some authors have also argued for a reconsideration of
Western philosophical anthropocentric conceptions of moral patiency,
in particular in regard to the question concerning the moral standing
of artificial agents or robots (Floridi 2016, Gunkel 2020,
Coeckelbergh 2020). The following will nevertheless focus on
moral agency as these reflections on moral patiency tend to not
address the challenges to moral responsibility.


### 2.1 Computers as morally responsible agents



The increasing complexity of computer technology and the advances in
Artificial Intelligence (AI), challenge the idea that human beings are
the only entities to which moral responsibility can or should be
ascribed (Bechtel 1985; Kroes and Verbeek 2014). Dennett, for example,
suggested that holding a computer morally responsible is possible if
it concerned a higher-order intentional computer system (1997). An
intentional system, according to him, is one that can be predicted and
explained by attributing beliefs and desires to it, as well as
rationality. In other words, its behavior can be described by assuming
the systems has mental states and that it acts according what it
thinks it ought to do, given its beliefs and desires. At the time,
Dennett noted that many computers were already intentional systems,
but they lacked the higher-order ability to reflect on and reason
about their mental states. They did not have beliefs about their
beliefs or thoughts about desires. Dennett suggested that the
fictional HAL 9000 that featured in the movie *2001: A Space
Odyssey* would qualify as a higher-order intentional system that
can be held morally responsible. Although advances in AI might not
lead to HAL, he did see the development of computers systems with
higher-order intentionality as a real possibility.



Sullins argues in line with Dennett that moral agency is not
restricted to human beings (2006). He proposes that computers systems
or, more specifically, robots are moral agents when they have a
significant level of autonomy and they can be regarded at an
appropriate level of abstraction as exhibiting intentional behavior. A
robot, according to Sullins, would be significantly autonomous if it
was not under the direct control of other agents in performing its
tasks. Note that Sullins interprets autonomy in a narrow sense in
comparison to the conception of autonomy in moral philosophy as
property of human beings. He adds as a third condition that a robot
also has to be in a position of responsibility to be a moral agent.
That is, the robot performs some social role that carries with it some
responsibilities and in performing this role the robot appears to have
'beliefs' about and an understanding of its duties towards
other moral agents (p. 28). To illustrate what kind of capabilities
are required for "full moral agency", he draws an analogy
with a human nurse. He argues that if a robot was autonomous enough to
carry out the same duties as a human nurse and had an understanding of
its role and responsibilities in the health care systems, then it
would be a "full moral agent". Sullins maintains that it
will be some time before machines with these kinds of capabilities
will be available, but "even the modest robots of today can be
seen to be moral agents of a sort under certain, but not all, levels
of abstraction and are deserving of moral consideration" (p.
29).



Echoing objections to the early project of (strong) AI (Sack
 1997),[3]
 critics of analyses such as presented by Dennett and Sullins, have
objected to the idea that computer technologies can have capacities
that make human beings moral agents, such as mental states,
intentionality, common sense, emotion or empathy (Johnson 2006; Kuflik
1999; Nyholm 2018). They, for instance, point out that it makes no
sense to treat computer system as moral agents that can be held
responsible, for they cannot suffer and thus cannot be punished
(Sparrow 2007; Asaro 2011). Veliz argues that computers may act like
moral agents, but they lack sentience and are therefore 'moral
zombies' (2021). Hakli and Makela argue that computers
cannot have the kind of autonomy required for moral agency, because
their capacities are a result of engineering and programming which
undermines the autonomy of robots and disqualifies them as moral
agents (2019). Or they argue, as Stahl does, that computers are not
capable of moral reasoning, because they do not have the capacity to
understand the meaning of the information that they process (2006). In
order to comprehend the meaning of moral statements an agent has to be
part of the form of life in which the statement is meaningful; it has
to be able to take part in moral discourses. Similar to the debates
about AI, critics continue to draw a distinction between humans and
computers by noting various capacities that computers do not, and
cannot, have that would justify the attribution of moral agency.



Some other critics do not contest that human beings might be able to
build computer systems with the required capacities for moral agency,
but question whether it is ethically appropriate to do so. Bryson, for
instance, argues that even if it was possible to create artifacts with
such capacities - and she assumes this might very well be
possible - human beings have a choice in the matter
(2018). She defines a moral agent as "something deemed
responsible by a society for its actions" (p. 16). Society can
thus at one point deem it appropriate to view certain computer systems
as moral agents, for instance as it would provide a short cut to
figuring out how responsibility should be distributed. However, she
argues that there is no necessary or predetermined position for these
technologies in our society. This is because, she notes, computer
technologies ethical frameworks are "artefacts of our societies,
and therefore subject to human control" (p. 15). We can choose
what capacities we equip these artefacts with, and she sees no
coherent reason for creating artificial agents that human beings have
to compete with in terms of moral agency or patiency.


### 2.2 Creating autonomous moral agents



In the absence of any definitive arguments for or against the
possibility of future computer systems being morally responsible,
researchers within the field of machine ethics aim to further develop
the discussion by focusing instead on creating computer system that
can behave *as if* they are moral agents (Moor 2006,
Cervantes et al 2019 , Zoshak and Dew 2021). Research
within this field has been concerned with the design and development
of computer systems that can independently determine what the right
thing to do would be in a given situation. According to Allen and
Wallach, such *autonomous moral agents* (AMAs) would have to be
capable of reasoning about the moral and social significance of their
behavior and use their assessment of the effects their behavior has on
sentient beings to make appropriate choices (2012; see also Wallach
and Allen 2009 and Allen et al. 2000). Such abilities are needed, they
argue, because computers are becoming more and more complex and
capable of operating without direct human control in different
contexts and environments. Progressively autonomous technologies
already in development, such as military robots, driverless cars or
trains and service robots in the home and for healthcare, will be
involved in moral situations that directly affect the safety and
well-being of humans. An autonomous bomb disposal robot might in the
future be faced with the decision which bomb it should defuse first,
in order to minimize casualties. Similarly, a moral decision that a
driverless car might have to make is whether to break for a crossing
dog or avoid the risk of causing injury to the driver behind him. Such
decisions require judgment. Currently operators make such moral
decisions, or the decision is already inscribed in the design of the
computer system. Machine ethics, Wallach and Allen argue, goes one
step beyond making engineers aware of the values they build into the
design of their products, as it seeks to build ethical decision-making
into the machines.



To further specify what it means for computers to make ethical
decisions or to put 'ethics in the machine', Moor
distinguished between three different kinds of ethical agents:
implicit ethical agents, explicit ethical agents, and full ethical
agents (2006). The first kind of agent is a computer that has the
ethics of its developers inscribed in their design. These agents are
constructed to adhere to the norms and values of the contexts in which
they are developed or will be used. Thus, ATM tellers are designed to
have a high level of security to prevent unauthorized people from
drawing money from accounts. An explicit ethical agent is a computer
that can 'do ethics'. In other words, it can on the basis
of an ethical model determine what would be the right thing to do,
given certain inputs. The ethical model can be based on ethical
traditions, such as Kantian, Confucianism, Ubuntu, or utilitarian
ethics--depending on the preferences of its creators. These
agents would 'make ethical decisions' on behalf of its
human users (and developers). Such agents are akin to the autonomous
moral agents described by Allen and Wallach. Finally, Moor defined
full ethical agents as entities that can make ethical judgments and
can justify them, much like human beings can. He claimed that although
at the time of his writing there were no computer technologies that
could be called fully ethical, it is an empirical question whether or
not it would be possible in the future. Few, if any, philosophers
today would argue that this question has been answered in the postive.




The effort to build AMAs raises the question of how this effort
affects the ascription of moral responsibility. As human beings would
design these artificial agents to behave within pre-specified
formalized ethical frameworks, it is likely that responsibility will
still be ascribed to these human actors and those that deploy these
technologies. However, as Allen and Wallach acknowledge, the danger of
exclusively focusing on equipping robots with moral decision-making
abilities, rather than also looking at the sociotechnical systems in
which these robots are embedded, is that it may cause further
confusion about the distribution of responsibility (2012). Robots with
moral decision-making capabilities may present similar challenges to
ascribing responsibility as other technologies, when they introduce
new complexities that further obfuscate causal connections that lead
back to their creators and users.


### 2.3 Expanding the concept of moral agency



The prospect of increasingly autonomous and intelligent computer
technologies and the growing difficulty of finding responsible human
agents lead Floridi and Sanders to take a different approach (2004).
They propose to extend the class of moral agents to include artificial
agents, while disconnecting moral agency and moral accountability from
the notion of moral responsibility. They contend that "the
insurmountable difficulties for the traditional and now rather
outdated view that a human can be found accountable for certain kinds
of software and even hardware" demands a different approach (p.
372). Instead, they suggest that artificial agents should be
acknowledged as moral agents that can be held accountable, but not
responsible. To illustrate they draw a comparison between artificial
agents and dogs as sources of moral actions. Dogs can be the cause of
a morally charged action, like damaging property or helping to save a
person's life, as in the case of search-and-rescue dogs. We can
identify them as moral agents even though we generally do not hold
them morally responsible, according to Floridi and Sanders: they are
the source of a moral action and can be held morally accountable by
correcting or punishing them.



Just like animals, Floridi and Sanders argue, artificial agents can be
seen as sources of moral actions and thus can be held morally
accountable when they can be conceived of as behaving like a moral
agent from an appropriate *level of abstraction*. The notion of
levels of abstraction refers to the stance one adopts towards and
entity to predict and explain its behavior. At a low level of
abstraction we would explain the behavior of a system in terms of its
mechanical or biological processes. At a higher level of abstraction
it can help to describe the behavior of a system in terms of beliefs,
desires and thoughts. If at a high enough level a computational system
can effectively be described as being interactive, autonomous and
adaptive, then it can be held accountable according to Floridi and
Sanders (p. 352). It, thus, does not require personhood or free will
for an agent to be morally accountable; rather the agent has to act as
if it had intentions and was able to make choices.



The advantage of disconnecting accountability from responsibility,
according to Floridi and Sanders, is that it places the focus on moral
agenthood, accountability and censure, instead of on figuring out
which human agents are responsible. "We are less likely to
assign responsibility at any cost, forced by the necessity to identify
a human moral agent. We can liberate technological development of AAs
[Artificial Agents] from being bound by the standard limiting
view" (p. 376). When artificial agents 'behave
badly' they can be dealt with directly, when their autonomous
behavior and complexity makes it too difficult to distribute
responsibility among human agents. Immoral agents can be modified or
deleted. It is then possible to attribute moral accountability even
when moral responsibility cannot be determined.



Critics of Floridi's and Sanders' view on accountability
and moral agency argue that placing the focus of analysis on
computational artifacts by treating them as moral agents will draw
attention away from the humans that deploy and develop them. Johnson,
for instance, makes the case that computer technologies remain
connected to the intentionality of their creators and users (2006).
She argues that although computational artifacts are a part of the
moral world and should be recognized as entities that have moral
relevance, they are not moral agents, for they are not intentional.
They are not intentional, because they do not have mental states or a
purpose that comes from the freedom to act. She emphasizes that
although these artifacts are not intentional, they do have
intentionality, but their intentionality is related to their
functionality. They are human-made artifacts and their design and use
reflect the intentions of designers and users. Human users, in turn,
use their intentionality to interact with and through the software. In
interacting with the artifacts they activate the inscribed intentions
of the designers and developers. It is through human activity that
computer technology is designed, developed, tested, installed,
initiated and provided with input and instructions to perform
specified tasks. Without this human activity, computers would do
nothing. Attributing independent moral agency to computers, Johnson
claims, disconnects them from the human behavior that creates, deploys
and uses them. It turns the attention away from the forces that shape
technological development and limits the possibility for intervention.
For instance, it leaves the issue of sorting out who is responsible
for dealing with malfunctioning or immoral artificial agents or who
should make amends for the harmful events they may cause. It postpones
the question of who has to account for the conditions under which
artificial agents are allowed to operate (Noorman 2009).



Yet, technologies can still be part of moral action, without being a
moral agent. Several philosophers have stressed that moral
responsibility cannot be properly understood without recognizing the
active role of technology in shaping human action (Jonas 1984; Verbeek
2006; Johnson and Powers 2005; Nyholm 2018). Johnson, for
instance, claims that although computers are not moral agents, the
artifact designer, the artifact, and the artifact user should all be
the focus of moral evaluation as they are all at work in an action
(Johnson 2006). Humans create these artifacts and inscribe in them
their particular values and intentions to achieve particular effects
in the world and in turn these technological artifacts influence what
human beings can and cannot do and affect how they perceive and
interpret the world.



Similarly, Verbeek maintains that technological artifacts alone do not
have moral agency, but moral agency is hardly ever
'purely' human. Moral agency generally involves a
mediating artifact that shapes human behavior, often in way not
anticipated by the designer (2008). Moral decisions and actions are
co-shaped by technological artifacts. He suggests that in all forms of
human action there are three forms of agency at work: 1) the agency of
the human performing the action; 2) the agency of the designer who
helped shaped the mediating role of the artifacts and 3) the artifact
mediating human action. The agency of artifacts is inextricably linked
to the agency of its designers and users, but it cannot be reduced to
either of them. For him, then, a subject that acts or makes moral
decisions is a composite of human and technological components. Moral
agency is not merely located in a human being, but in a complex blend
of humans and technologies.



In later papers, Floridi explores the concept of distributed moral
actions (2013, 2016). He argues that some moral significant outcomes
cannot be reduced to the moral significant actions of some
individuals. Morally neutral actions of several individuals can still
result in morally significant events. Individuals might not have
intended to cause harm, but nevertheless their combined actions may
still result in moral harm to someone or something. In order to deal
with the problem of subsequently assigning moral responsibility for
such distributed moral actions, he argues that the focus of analysis
should shift from the agents to the patients of moral actions. A moral
action can then be evaluated in terms of the harm to the patient,
regardless of the intentions of the agents involved. Assigning
responsibility then focuses on whether or not an agent is causally
accountable for the outcome and on adjusting their behavior to prevent
harm. If the agents causally accountable - be they artificial or
biological - are autonomous, can interact with each other and their
environments and can learn from their interactions they can be held
responsible for distributed moral actions, according to Floridi
(2016).


## 3. Rethinking the concept of moral responsibility



In light of the noted difficulties in ascribing moral responsibility,
several authors have critiqued the way in which the concept is used
and interpreted in relation to computing. They claim that the
traditional models or frameworks for dealing with moral responsibility
fall short and propose different perspectives or interpretations to
address some of the difficulties. Some of these will be discussed in
this section. 


### 3.1 Assigning responsibility



One approach is to rethink how moral responsibility is assigned
(Gotterbarn 2001; Waelbers 2009). When it comes to computing
practitioners, Gotterbarn identifies a potential to side-step or avoid
responsibility by looking for someone else to blame. He attributes
this potential to two pervasive misconceptions about responsibility.
The first misconception is that computing is an ethically neutral
practice. That is, according to Gotterbarn, the misplaced belief that
technological artifacts and the practices of building them are
ethically neutral is often used to justify a narrow
technology-centered focus on the development of computer system
without taking the broader context in which these technologies operate
into account. This narrow focus can have detrimental consequences.
Gotterbarn gives the tragic case of a patient's death as a
result of a faulty X-ray device as an example. A programmer was given
the assignment to write a program that could lower or raise the X-ray
device on a pole, after an X-ray technician set the required height.
The programmer focused on solving the given puzzle, but failed to take
account of the circumstances in which the device would be used and the
contingencies that might occur. He, thus, did not consider the
possibility that a patient could accidentally be in the way of the
device moving up and down the pole. This oversight eventually resulted
in a tragic accident. A patient was crushed by the device, when a
technician set the device to tabletop height, not realizing that the
patient was still underneath it. According to Gotterbarn, computer
practitioners have a moral responsibility to consider such
contingencies, even though they may not be legally required to do so.
The design and use of technological artifacts is a moral activity and
the choice for one particular design solution over another has real
and material consequences.



The second misconception is that responsibility is only about
determining blame when something goes wrong. Computer practitioners,
according to Gotterbarn, have conventionally adopted a malpractice
model of responsibility that focuses on determining the appropriate
person to blame for harmful incidents (2001). This malpractice model
leads to all sorts of excuses to shirk responsibility. In particular,
the complexities that computer technologies introduce allow computer
practitioners to side-step responsibility. The distance between
developers and the effects of the use of the technologies they create
can, for instance, be used to claim that there is no direct and
immediate causal link that would tie developers to a malfunction.
Developers can argue that their contribution to the chain of events
was negligible, as they are part of a team or larger organization and
they had limited opportunity to do otherwise. The malpractice model,
according to Gotterbarn, entices computer practitioners to distance
themselves from accountability and blame.



The two misconceptions are based on a particular retrospective view of
responsibility that places the focus on that which exempts one from
blame and liability. In reference to Ladd, Gotterbarn calls this
negative responsibility and distinguishes it from positive
responsibility (see also Ladd 1989). Positive responsibility
emphasizes "the virtue of having or being obliged to have regard
for the consequences that his or her actions have on others"
(Gotterbarn 2001, p. 227). Positive responsibility entails that part
of the professionalism of computer experts is that they strive to
minimize foreseeable undesirable events. It focuses on what ought to
be done rather than on blaming or punishing others for irresponsible
behavior. Gotterbarn argues that the computing professions should
adopt a positive concept of responsibility, as it emphasizes the
obligations and duties of computer practitioners to have regard for
the consequences of one's actions and to minimize the
possibility of causing harm. Computer practitioners have a moral
responsibility to avoid harm and to deliver a properly working
product, according to him, regardless of whether they will be held
accountable if things turn out differently.



The emphasis on the positive moral responsibility of computer
practitioners raises the question of how far this responsibility
reaches, in particular in light of systems that many hands help create
and the difficulties involved in anticipating contingencies that might
cause a system to malfunction (Stieb 2008; Miller 2008). To what
extent can developers and manufacturers be expected to exert
themselves to anticipate or prevent the consequences of the use of
their technologies or possible 'bugs' in their code?
Computer systems today are generally incomprehensible to any single
programmer and it seems unlikely that complex computer systems can be
completely error free. Martin argues in this respect that developers
and the companies that decide to sell a computer technology
*into* a particular context are responsible for the ethical
implication of the use of these technologies in that context (2019).
They are responsible for these implications because they are
knowledgeable about the design decisions and are in a unique position
to inscribe in the technology particular ideas (and biases) about what
the technology should do and how it should do it. Thus, a company that
creates and sells a risk-assessment system into the context of
judicial decision-making is responsible for the ethical implications
of biases resulting from its use and its opaqueness. The company
willingly "takes on the obligation to understand the values of
the decision to ensure the algorithms' ethical implications is
congruent with the context" (p. 10). This, however, leaves open
the question of what their responsibility is outside of that context.
Should manufacturers of mobile phones have anticipated that their
products would be used in roadside bombs? Manufacturers and their
designers and engineers cannot foresee all the possible conditions
under which their products will eventually operate. Moreover, how much
control should a person have to be or feel responsible for the outcome
of events? Such questions speak to what Santonio de Sio and Meccaci
(2020) call the "active responsibility gap". They
described active responsibility in much the same way as positive
responsibility, in that it relates to the moral obligations of persons
to ensure that the behavior of the systems they design, control, or
use minimizes harm. A gap in this responsibility, according to them,
results from these persons not being sufficiently aware, capable and
motivated to see and act according to these obligations (p.
1059).



To address gaps in active responsibility (as well as backward-looking
gaps in responsibility), Santonio de Sio and Meccaci suggest an
approach that underlines the need to look at the broader
sociotechnical system of human agents and technologies. Assigning
responsibility requires looking at the whole chain of design,
development and use from a social, technical as well as organizational
perspective. Each element in this change can be adjusted in an effort
to address responsibility gaps, including the design of the computer
systems as well as the organization that uses it. They base their
approach on the idea of designing sociotechnial systems for
*meaningful human control* as developed by Santonio de Sio and
van den Hoven (2018). Meaningful human control is a concept that
originally gained currency in the context of autonomous lethal weapons
as an approach to addressing responsibility gaps. The question of what
it means to have control over a technological system becomes
particularly pertinent in situations where weapon systems are
delegated tasks involved in target selection and engagement (Ekelhof
2019). Santoni di Sio and van den Hoven (2018) developed their
conception of *meaningful human control* to get a more
'actionable analysis of control' that can help engineers,
computing professionals, policy makers and designers think about how
to design sociotechnical systems with responsibility in mind.



Santonio de Sio and van den Hoven assume that technology is part of
the decisional mechanisms through which human agents carry out actions
in the world and these mechanisms should be responsive to moral
reasons for an agent to have control. Meaningful human control is thus
conditional on the extent to which an outcome can be connected to the
decisional mechanisms of human agents. To elucidate these connections,
they formulate two necessary conditions for meaningful control called
*tracking* and *tracing*.



*Tracking* requires that the whole sociotechnical system of
technical, human and organizational elements should be responsive to
moral reasons of the relevant agents and to the relevant facts of the
circumstances. That is, the behaviour of the system should reflect the
reasons, values, and intentions of these actors given particular
circumstances. For example, if a machine learning system is trained to
distinguish huskies from wolves, the system should act according to
the relevant reasons for doing this (e.g. alarming a farmer to the
presence of a wolf). A machine learning system that is trained to make
this distinction, but has only been shown pictures of wolves in the
snow and huskies in more urban environments, it may deduce that the
relevant distinguishing feature is snow. When shown a picture of a
wolf in urban environment, it might subsequently misclassify the wolf
as a husky. In this case, the system did not properly track the
reasons of relevant human agents and the facts of the environment.
Note that this tracking relation can involve multiple human agents
along the chain. That is, the moral reasons do not necessarily have to
come from the operator or user, they can also come from policy makers,
designers or programmers.



*Tracing* requires that outcomes can be traced back to earlier
decisions made by human agents that place them in the position
resulting in the outcome. An example that the authors give is a drunk
driver causing a serious accident. Even if the driver does not fulfill
the conditions of responsibility at the time of the accident - because
of mental incapacitation - the driver did make the earlier
decision of drinking too much. The *tracing condition*, as
Santoni di Sio and van den Hoven formulate it, assumes that it is
possible that more than one human agent is involved in the actions
that led to the outcome and that their actions are mediated by
non-human systems. According to them, the tracing condition requires
that the whole sociotechnical system is designed such that at least
one human agent can have sufficient knowledge and moral awareness to
be a potential target of legitimate response for the behavior of the
system.



Santonio de Sio and van den Hoven's understanding of meaningful
human control does not only have implications for the design of
computer systems, but also for the design of the environment and the
social and institutional practices. Tracking and tracing of relevant
human moral reasons occurs on all these levels of design.


### 3.2 Responsibility as practice



Santonio de Sio and van den Hoven's analysis of meaningful human
control draws attention to the social function of moral
responsibility, which provides yet another perspective on the issue
(Stahl 2006; Tigard 2021b). Both prospectively and retrospectively,
responsibility works to organize social relations between people and
between people and institutions. It sets expectations between people
for the fulfillment of certain obligations and duties and provides the
means to correct or encourage certain behavior. For instance, a
robotics company is expected to build in safeguards that prevent
robots from harming humans. If the company fails to live up to this
expectation, it will be held accountable and in some cases it will
have to pay for damages or undergo some other kind of punishment. The
punishment or prospect of punishment can encourage the company to have
more regard for system safety, reliability, sound design and the risks
involved in their production of robots. It might trigger the company
to take actions to prevent future accidents. Yet, it might also
encourage it to find ways to shift the blame. The idea that
responsibility is about interpersonal relationships and expectations
about duties and obligations places the focus on the practices of
holding someone responsible (Strawson 1962, Talbert 2022).



The particular practices and social structures that are in place to
ascribe responsibility and hold people accountable, have an influence
on how we relate to technologies. Just before the turn of the century,
Nissenbaum already noted that the difficulties in attributing moral
responsibility can, to a large extent, be traced back to the
particular characteristics of the organizational and cultural context
in which computer technologies are embedded. She argued that how we
conceive of the nature, capacities and limitations of computing is of
influence on the answerability of those who develop and use computer
technologies (1997). At the time, she observed a systematic erosion of
accountability in our increasingly computerized society, where she
conceived of accountability as a value and a practice that places an
emphasis on preventing harm and risk. Accountability means there will
be someone, or several people, to answer not only for the malfunctions
in life-critical systems that cause or risk grave injuries and cause
infrastructure and large monetary losses, but even for the malfunction
that cause individual losses of time, convenience, and contentment
(1994, p. 74). It can be used as "a powerful tool for motivating
better practices, and consequently more reliable and trustworthy
systems" (1997, p. 43). Holding people accountable for the harms
or risks caused by computer systems provides a strong incentive to
minimize them and can provide a starting point for assigning just
punishment.



Cultural and organizational practices, at the time, however seemed to
do the opposite, due to "the conditions under which computer
technologies are commonly developed and deployed, coupled with popular
conceptions about the nature, capacities and limitations of
computing" (p. 43). Nissenbaum identified four barriers to
accountability in society: (1) the problem of many hands, (2) the
acceptance of computer bugs as an inherent element of large software
systems, (3) using the computer as scapegoat and (4) ownership without
liability. According to Nissenbaum people have a tendency to shirk
responsibility and to shift the blame to others when accidents occur.
The problem of many hands and the idea that software bugs are an
inevitable by-product of complex computer systems are too easily
accepted as excuses for not answering for harmful outcomes. People are
also inclined to point the finger at the complexity of the computer
and argue that "it was the computer's fault" when
things go wrong. Finally, she perceived a tendency of companies to
claim ownership of the software they developed, but to dismiss the
responsibilities that come with ownership. To illustrate, she pointed
to extended license agreements that assert a manufacturer's
ownership of software, but disclaim any accountability for the quality
or performance of the product. 



These four barriers, Nissenbaum argued, stand in the way of a
"culture of accountability" that is aimed at maintaining
clear lines of accountability. Such a culture fosters a strong sense
of responsibility as a virtue to be encouraged and everyone connected
to an outcome of particular actions is answerable for it.
Accountability, according to Nissenbaum, is different from liability.
Liability is about looking for a person to blame and to compensate for
damages suffered after the event. Once that person has been found,
others can be let 'off the hook', which may encourage
people to look for excuses, such as blaming the computer.
Accountability, however, applies to all those involved. It requires a
particular kind of organizational context, one in which answerability
works to entice people to pay greater attention to system safety,
reliability and sound design, in order to establish a culture of
accountability (see also Martin 2019, Herkert et AL 2020) . An
organization that places less value on accountability and that has
little regards for responsibilities in organizing their production
processes is more likely to allow their technological products to
become incomprehensible. Nissenbaum's analysis illustrates that
our practices of holding someone responsible - the established ways of
holding people to account and of conveying expectations about duties
and obligations - are continuously changing and negotiated, partly as
a response to the introduction of new technologies (see also Noorman
2012).



A lack of such a culture of accountability can lead to responsibility
being attributed to the wrong people. These people become, what
Madeleine Clare Elish calls the moral crumple zone (2019).
These human actors absorb responsibility, even though they only have
very limited or no control over the systems they work with. Elish
argues that, given the complexity of technological systems, the media
and the public tend to blame accidents on human error and misattribute
responsibility to the nearest human operator, such as a pilot or
maintenance personnel, rather than the technological systems or the
decision-makers higher up the chain.



Nissenbaum argued that the context in which technologies are developed
and used has a significant influence on the ascription of moral
responsibility, but several authors have stressed that moral
responsibility cannot be properly understood without recognizing the
active role of technology in shaping human action (Jonas 1984; Verbeek
2006; Johnson and Powers 2005; Waelbers 2009). According to Johnson
and Powers it is not enough to just look at what humans intend and do.
"Ascribing more responsibility to persons who act with
technology requires coming to grips with the behavior of the
technology" (2005, p. 107). One has to consider the
various ways in which technological artifacts mediate human actions.
Moral responsibility is, thus, not only about how the actions of a
person or a group of people affect others in a morally significant
way; it is also about how their actions are shaped by technology.
Moral responsibility from this perspective is not located in an
individual or an interpersonal relationship, but is distributed among
humans and technologies.


## 4. Conclusion



Computer technologies have challenged conventional conceptions of
moral responsibility and have raised questions about how to distribute
responsibility appropriately. Can human beings still be held
responsible for the behavior of complex computer technologies that
they have limited control over or understanding of? Are human beings
the only agents that can be held morally responsible or can the
concept of moral agent be extended to include artificial computational
entities? In response to such questions philosophers have reexamined
the concepts of moral agency and moral responsibility. Although there
is no clear consensus on what these concepts should entail in an
increasingly digital society, what is clear from the discussions is
that any reflection on these concepts will need to address how these
technologies affect human action and where responsibility for action
begins and ends.