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AI4People—An Ethical Framework for a Good AI Society: Opportunities, Risks, Principles, and Recommendations


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This article reports the findings of AI4People, an Atomium—EISMD initiative designed to lay the foundations for a “Good AI Society”. We introduce the core opportunities and risks of AI for society; present a synthesis of five ethical principles that should undergird its development and adoption; and offer 20 concrete recommendations—to assess, to develop, to incentivise, and to support good AI—which in some cases may be undertaken directly by national or supranational policy makers, while in others may be led by other stakeholders. If adopted, these recommendations would serve as a firm foundation for the establishment of a Good AI Society.
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Minds and Machines (2018) 28:689–707
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AI4People—An Ethical Framework foraGood AI Society:
Opportunities, Risks, Principles, andRecommendations
LucianoFloridi1,2 · JoshCowls1,2· MonicaBeltrametti3· RajaChatila4,5·
PatriceChazerand6· VirginiaDignum7,8· ChristophLuetge9·
RobertMadelin10· UgoPagallo11· FrancescaRossi12,13· BurkhardSchafer14·
PeggyValcke15,16· EyVayena17
Received: 28 October 2018 / Accepted: 2 November 2018 / Published online: 26 November 2018
© The Author(s) 2018
This article reports the findings of AI4People, an Atomium—EISMD initia-
tivedesigned to lay the foundations for a “Good AI Society”. We introduce the core
opportunities and risks of AI for society; present a synthesis of five ethical principles
that should undergird its development and adoption; and offer 20 concrete recom-
mendations—to assess, to develop, to incentivise, and to support good AI—which in
some cases may be undertaken directly by national or supranational policy makers,
while in others may be led by other stakeholders. If adopted, these recommendations
would serve as a firm foundation for the establishment of a Good AI Society.
Keywords Artificial intelligence· AI4People· Data governance· Digital ethics·
Governance· Ethics of AI
1 Introduction
AI is not another utility that needs to be regulated once it is mature. It is a power-
ful force, a new form of smart agency, which is already reshaping our lives, our
interactions, and our environments. AI4People was set up to help steer this powerful
force towards the good of society, everyone in it, and the environments we share.
This article is the outcome of the collaborative effort by the AI4People Scientific
* Luciano Floridi
Extended author information available on the last page of the article
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L.Floridi et al.
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Committee—comprising 12 experts and chaired by Luciano Floridi1—to propose a
series of recommendations for the development of a Good AI Society.
The article synthesises three things: the opportunities and associated risks that
AI technologies offer for fostering human dignity and promoting human flourish-
ing; the principles that should undergird the adoption of AI; and 20specific recom-
mendations that, if adopted, will enable all stakeholders to seize the opportunities,
to avoid or at least minimise and counterbalance the risks, to respect the principles,
and hence to develop a Good AI Society.
The article is structured around four more sections after this introduction. Sec-
tion2 states the core opportunities for promoting human dignity and human flourish-
ing offered by AI, together with their corresponding risks.2 Section3 offers a brief,
high-level view of the advantages for organisations of taking an ethical approach
to the development and use of AI. Section4 formulates 5 ethical principles for AI,
building on existing analyses, which should undergird the ethical adoption of AI in
society at large. Finally, Sect.5 offers 20 recommendations for the purpose of devel-
oping a Good AI Society in Europe.
Since the launch of AI4People in February 2018, the Scientific Committee has
acted collaboratively to develop the recommendations in the final section of this
paper. Through this work, we hope to have contributed to the foundation of a Good
AI Society we can all share.
2 The Opportunities andRisks ofAI forSociety
That AI will have a major impact on society is no longer in question. Current debate
turns instead on how far this impact will be positive or negative, for whom, in which
ways, in which places, and on what timescale. Put another way, we can safely dis-
pense with the question of whether AI will have an impact; the pertinent questions
now are by whom, how, where, and when this positive or negative impact will be felt.
In order to frame these questions in a more substantive and practical way, we
introduce here what we consider the four chief opportunities for society that AI
offers. They are four because they address the four fundamental points in the under-
standing of human dignity and flourishing: who we can become (autonomous self-
realisation); what we can do (human agency); what we can achieve (individual and
societal capabilities); and how we can interact with each other and the world (soci-
etal cohesion). In each case, AI can be used to foster human nature and its potentiali-
ties, thus creating opportunities; underused, thus creating opportunity costs; or over-
used and misused, thus creating risks. As the terminology indicates, the assumption
2 The analysis in this and the following two sections is also available in Cowls and Floridi (2018). Fur-
ther analysis and more information on the methodology employed will be presented in Cowls and Floridi
1 Besides Luciano Floridi, the members of the Scientific Committee are: Monica Beltrametti, Raja Cha-
tila, Patrice Chazerand, Virginia Dignum, Christoph Luetge, Robert Madelin, Ugo Pagallo, Francesca
Rossi, Burkhard Schafer, Peggy Valcke, and Effy Vayena. Josh Cowls is the rapporteur. Thomas Burri
contributed to an earlier draft.
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AI4People—An Ethical Framework foraGood AI Society:…
is that the use of AI is synonymous with good innovation and positive applications
of this technology. However, fear, ignorance, misplaced concerns or excessive reac-
tion may lead a society to underuse AI technologies below their full potential, for
what might be broadly described as the wrong reasons. This may cause significant
opportunity costs. It might include, for example, heavy-handed or misconceived
regulation, under-investment, or a public backlash akin to that faced by genetically
modified crops (Imperial College 2017). As a result, the benefits offered by AI tech-
nologies may not be fully realised by society. These dangers arise largely from unin-
tended consequences and relate typically to good intentions gone awry. However, we
must also consider the risks associated with inadvertent overuse or wilful misuse of
AI technologies, grounded, for example, in misaligned incentives, greed, adversarial
geopolitics, or malicious intent. Everything from email scams to full-scale cyber-
warfare may be accelerated or intensified by the malicious use of AI technologies
(Taddeo 2018). And new evils may be made possible (King etal. 2018). The possi-
bility of social progress represented by the aforementioned opportunities above must
be weighed against the risk that malicious manipulation will be enabled or enhanced
by AI. Yet a broad risk is that AI may be underused out of fear of overuse or misuse.
We summarise these risks in Fig.1 below, and offer a more detailed explanation in
the text that follows.
2.1 Who We Can Become: Enabling Human Self‑Realisation, Without Devaluing
Human Abilities
AI may enable self-realisation, by which we mean the ability for people to flour-
ish in terms of their own characteristics, interests, potential abilities or skills,
aspirations, and life projects. Much as inventions, such as the washing machine,
liberated people—particularly women—from the drudgery of domestic work,
the “smart” automation of other mundane aspects of life may free up yet more
time for cultural, intellectual and social pursuits, and more interesting and
rewarding work. More AI may easily mean more human life spent more intel-
ligently. The risk in this case is not the obsolescence of some old skills and the
Fig. 1 Overview of the four core opportunities offered by AI, four corresponding risks, and the opportu-
nity cost of underusing AI
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L.Floridi et al.
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emergence of new ones per se, but the pace at which this is happening and the
unequal distributions of the costs and benefits that result. A very fast devalua-
tion of old skills and hence a quick disruption of the job market and the nature
of employment can be seen at the level of both the individual and society. At the
level of the individual, jobs are often intimately linked to personal identity, self-
esteem, and social role or standing, all factors that may be adversely affected by
redundancy, even putting to one side the potential for severe economic harm.
Furthermore, at the level of society, the deskilling in sensitive, skill-intensive
domains, such as health care diagnosis or aviation, may create dangerous vulner-
abilities in the event of AI malfunction or an adversarial attack. Fostering the
development of AI in support of new abilities and skills, while anticipating and
mitigating its impact on old ones will require both close study and potentially
radical ideas, such as the proposal for some form of “universal basic income”,
which is growing in popularity and experimental use. In the end, we need some
intergenerational solidarity between those disadvantaged today and those advan-
taged tomorrow, to ensure that the disruptive transition between the present and
the future will be as fair as possible, for everyone.
2.2 What We Can Do: Enhancing Human Agency, Without Removing Human
AI is providing a growing reservoir of “smart agency”. Put at the service of
human intelligence, such a resource can hugely enhance human agency. We can
do more, better, and faster, thanks to the support provided by AI. In this sense of
Augmented Intelligence”, AI could be compared to the impact that engines have
had on our lives. The larger the number of people who will enjoy the opportuni-
ties and benefits of such a reservoir of smart agency “on tap”, the better our soci-
eties will be. Responsibility is therefore essential, in view of what sort of AI we
develop, how we use it, and whether we share with everyone its advantages and
benefits. Obviously, the corresponding risk is the absence of such responsibility.
This may happen not just because we have the wrong socio-political framework,
but also because of a “black box” mentality, according to which AI systems for
decision-making are seen as being beyond human understanding, and hence con-
trol. These concerns apply not only to high-profile cases, such as deaths caused
by autonomous vehicles, but also to more commonplace but still significant uses,
such as in automated decisions about parole or creditworthiness.
Yet the relationship between the degree and quality of agency that people
enjoy and how much agency we delegate to autonomous systems is not zero-
sum, either pragmatically or ethically. In fact, if developed thoughtfully, AI
offers the opportunity of improving and multiplying the possibilities for human
agency. Consider examples of “distributed morality” in human-to-human sys-
tems such as peer-to-peer lending (Floridi 2013). Human agency may be ulti-
mately supported, refined and expanded by the embedding of “facilitating frame-
works”, designed to improve the likelihood of morally good outcomes, in the set
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AI4People—An Ethical Framework foraGood AI Society:…
of functions that we delegate to AI systems. AI systems could, if designed effec-
tively, amplify and strengthen shared moral systems.
2.3 What We Can Achieve: Increasing Societal Capabilities, Without Reducing
Human Control
Artificial intelligence offers myriad opportunities for improving and augmenting
the capabilities of individuals and society at large. Whether by preventing and cur-
ing diseases or optimising transportation and logistics, the use of AI technologies
presents countless possibilities for reinventing society by radically enhancing what
humans are collectively capable of. More AI may support better coordination, and
hence more ambitious goals. Human intelligence augmented by AI could find new
solutions to old and new problems, from a fairer or more efficient distribution of
resources to a more sustainable approach to consumption. Precisely because such
technologies have the potential to be so powerful and disruptive, they also introduce
proportionate risks. Increasingly, we may not need to be either ‘in or on the loop’
(that is, as part of the process or at least in control of it), if we can delegate our
tasks to AI. However, if we rely on the use of AI technologies to augment our own
abilities in the wrong way, we may delegate important tasks and above all decisions
to autonomous systems that should remain at least partly subject to human supervi-
sion and choice. This in turn may reduce our ability to monitor the performance of
these systems (by no longer being ‘on the loop’ either) or preventing or redressing
errors or harms that arise (‘post loop’). It is also possible that these potential harms
may accumulate and become entrenched, as more and more functions are delegated
to artificial systems. It is therefore imperative to strike a balance between pursuing
the ambitious opportunities offered by AI to improve human life and what we can
achieve, on the one hand, and, on the other hand, ensuring that we remain in control
of these major developments and their effects.
2.4 How We Can Interact: Cultivating Societal Cohesion, Without Eroding Human
From climate change and antimicrobial resistance to nuclear proliferation and fun-
damentalism, global problems increasingly have high degrees of coordination
complexity, meaning that they can be tackled successfully only if all stakeholders
co-design and co-own the solutions and cooperate to bring them about. AI, with
its data-intensive, algorithmic-driven solutions, can hugely help to deal with such
coordination complexity, supporting more societal cohesion and collaboration. For
example, efforts to tackle climate change have exposed the challenge of creating a
cohesive response, both within societies and between them. The scale of this chal-
lenge is such that we may soon need to decide between engineering the climate
directly and designing societal frameworks to encourage a drastic cut in harmful
emissions. This latter option might be undergirded by an algorithmic system to cul-
tivate societal cohesion. Such a system would not be imposed from the outside; it
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L.Floridi et al.
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would be the result of a self-imposed choice, not unlike our choice of not buying
chocolate if we had earlier chosen to be on a diet, or setting up an alarm clock to
wake up. “Self-nudging” to behave in socially preferable ways is the best form of
nudging, and the only one that preserves autonomy. It is the outcome of human deci-
sions and choices, but it can rely on AI solutions to be implemented and facilitated.
Yet the risk is that AI systems may erode human self-determination, as they may
lead to unplanned and unwelcome changes in human behaviours to accommodate
the routines that make automation work and people’s lives easier. AI’s predictive
power and relentless nudging, even if unintentional, should be at the service of
human self-determination and foster societal cohesion, not undermining human dig-
nity or human flourishing.
Taken together, these four opportunities, and their corresponding challenges,
paint a mixed picture about the impact of AI on society and the people in it. Accept-
ing the presence of trade-offs, seizing the opportunities while working to anticipate,
avoid, or minimise the risks head-on will improve the prospect for AI technologies
to promote human dignity and flourishing. Having outlined the potential benefits
to individuals and society at large of an ethically engaged approach to AI, in the
next section we highlight the “dual advantage” to organisations of taking such an
3 The Dual Advantage ofanEthical Approach toAI
Ensuring socially preferable outcomes of AI relies on resolving the tension between
incorporating the benefits and mitigating the potential harms of AI, in short, simul-
taneously avoiding the misuse and underuse of these technologies. In this context,
the value of an ethical approach to AI technologies comes into starker relief. Com-
pliance with the law is merely necessary (it isthe least that is required), but signifi-
cantly insufficient (it isnot the most than can and should be done) (Floridi 2018).
With an analogy, it is the difference between playing according to the rules, and
playing well, so that one may win the game. Adopting an ethical approach to AI con-
fers what we define here as a “dual advantage”. On one side, ethics enables organi-
sations to take advantage of the social value that AI enables. This is the advantage
of being able to identify and leverage new opportunities that are socially acceptable
or preferable. On the other side, ethics enables organisations to anticipate and avoid
or at least minimise costly mistakes. This is the advantage of prevention and mitiga-
tion of courses of action that turn out to be socially unacceptable and hence rejected,
even when legally unquestionable. This also lowers the opportunity costs of choices
not made or options not grabbed for fear of mistakes.
Ethics’ dual advantage can only function in an environment of public trust and
clear responsibilities more broadly. Public acceptance and adoption of AI technolo-
gies will occur only if the benefits are seen as meaningful and risks as potential, yet
preventable, minimisable, or at least something against which one can be protected,
through risk management (e.g. insurance) or redressing. These attitudes will depend
in turn on public engagement with the development of AI technologies, openness
about how they operate, and understandable, widely accessible mechanisms of
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AI4People—An Ethical Framework foraGood AI Society:…
regulation and redress. In this way, an ethical approach to AI can also be seen as
an early warning system against risks that might endanger entire organisations. The
clear value to any organisation of the dual advantage of an ethical approach to AI
amply justifies the expense of engagement, openness, and contestability that such an
approach requires.
4 A Unied Framework ofPrinciples forAI inSociety
AI4People is not the first initiative to consider the ethical implications of AI. Many
organisations have already produced statements of the values or principles that
should guide the development and deployment of AI in society. Rather than conduct
a similar, potentially redundant exercise here, we strive to move the dialogue for-
ward, constructively, from principles to proposed policies, best practices, and con-
crete recommendations for new strategies. Such recommendations are not offered in
a vacuum. But rather than generating yet another series of principles to serve as an
ethical foundation for our recommendations, we offer a synthesis of existing sets of
principles produced by various reputable, multi-stakeholder organisations and initia-
tives. A fuller explanation of the scope, selection and method of assessing these sets
of principles is available in Cowls and Floridi (Forthcoming). Here, we focus on
the commonalities and noteworthy differences observable across these sets of prin-
ciples, in view of the 20 recommendations offered in the rest of the paper. The docu-
ments we assessed are:
1. The Asilomar AI Principles, developed under the auspices of the Future of Life
Institute, in collaboration with attendees of the high-level Asilomar conference
of January 2017 (hereafter “Asilomar”; Asilomar AI Principles 2017);
2. The Montreal Declaration for Responsible AI, developed under the auspices of
the University of Montreal, following the Forum on the Socially Responsible
Development of AI of November 2017 (hereafter “Montreal”; Montreal Declara-
tion 2017)3;
3. The General Principles offered in the second version of Ethically Aligned Design:
A Vision for Prioritizing Human Well-being with Autonomous and Intelligent Sys-
tems. This crowd-sourced global treatise received contributions from 250 global
thought leaders to develop principles and recommendations for the ethical devel-
opment and design of autonomous and intelligent systems, and was published in
December 2017 (hereafter “IEEE”; IEEE 2017)4;
4. The Ethical Principles offered in the Statement on Artificial Intelligence, Robotics
and ‘Autonomous’ Systems, published by the European Commission’s European
3 The Montreal Declaration is currently open for comments as part of a redrafting exercise. The princi-
ples we refer to here are those which were publicly announced as of 1st May, 2018.
4 The third version of Ethically Aligned Design will be released in 2019 following wider public consul-
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L.Floridi et al.
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Group on Ethics in Science and New Technologies, in March 2018 (hereafter
“EGE”; EGE 2018);
5. The “five overarching principles for an AI code” offered in paragraph 417 of the
UK House of Lords Artificial Intelligence Committee’s report, AI in the UK:
ready, willing and able?, published in April 2018 (hereafter “AIUK”; House of
Lords 2018); and
6. The Tenets of the Partnership on AI, a multistakeholder organisation consisting
of academics, researchers, civil society organisations, companies building and
utilising AI technology, and other groups (hereafter “the Partnership”; Partnership
on AI 2018).
Taken together, they yield 47 principles.5 Overall, we find an impressive and reas-
suring degree of coherence and overlap between the six sets of principles. This can
most clearly be shown by comparing the sets of principles with the set of four core
principles commonly used in bioethics: beneficence, non-maleficence, autonomy,
and justice. The comparison should not be surprising. Of all areas of applied ethics,
bioethics is the one that most closely resembles digital ethics in dealing ecologi-
cally with new forms of agents, patients, and environments (Floridi 2013). The four
bioethical principles adapt surprisingly well to the fresh ethical challenges posed
by artificial intelligence. But they are not exhaustive. On the basis of the following
comparative analysis, we argue that one more, new principle is needed in addition:
explicability, understood as incorporating both intelligibility and accountability.
4.1 Benecence: Promoting Well‑Being, Preserving Dignity, andSustaining
Of the four core bioethics principles, beneficence is perhaps the easiest to observe
across the six sets of principles we synthesise here. The principle of creating AI
technology that is beneficial to humanity is expressed in different ways, but it typi-
cally features at the top of each list of principles. Montreal and IEEE principles both
use the term “well-being”: for Montreal, “the development of AI should ultimately
promote the well-being of all sentient creatures”; while IEEE states the need to “pri-
oritize human well-being as an outcome in all system designs”. AIUK and Asilomar
both characterise this principle as the “common good”: AI should “be developed for
the common good and the benefit of humanity”, according to AIUK. The Partner-
ship describes the intention to “ensure that AI technologies benefit and empower as
many people as possible”; while the EGE emphasises the principle of both “human
dignity” and “sustainability”. Its principle of “sustainability” represents perhaps the
5 Of the six documents, the Asilomar Principles offer the largest number of principles with arguably
the broadest scope. The 23 principles are organised under three headings, “research issues”, “ethics and
values”, and “longer-term issues”. We have omitted consideration of the five “research issues” here as
they are related specifically to the practicalities of AI development, particularly in the narrower context
of academia and industry. Similarly, the Partnership’s eight Tenets consist of both intra-organisational
objectives and wider principles for the development and use of AI. We include only the wider principles
(the first, sixth, and seventh tenets).
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AI4People—An Ethical Framework foraGood AI Society:…
widest of all interpretations of beneficence, arguing that “AI technology must be
in line with … ensur[ing] the basic preconditions for life on our planet, continued
prospering for mankind and the preservation of a good environment for future gen-
erations”. Taken together, the prominence of these principles of beneficence firmly
underlines the central importance of promoting the well-being of people and the
4.2 Non‑malecence: Privacy, Security and“Capability Caution”
Though “do only good” (beneficence) and “do no harm” (non-maleficence) seem
logically equivalent, in both the context of bioethics and of the ethics of AI they
represent distinct principles, each requiring explication. While they encourage well-
being, the sharing of benefits and the advancement of the public good, each of the
six sets of principles also cautions against the many potentially negative conse-
quences of overusing or misusing AI technologies. Of particular concern is the pre-
vention of infringements on personal privacy, which is listed as a principle in five of
the six sets, and as part of the “human rights” principles in the IEEE document. In
each case, privacy is characterised as being intimately linked to individuals’ access
to, and control over, how personal data is used.
Yet the infringement of privacy is not the only danger to be avoided in the adop-
tion of AI. Several of the documents also emphasise the importance of avoiding the
misuse of AI technologies in other ways. The Asilomar Principles are quite spe-
cific on this point, citing the threats of an AI arms race and of the recursive self-
improvement of AI, as well as the need for “caution” around “upper limits on future
AI capabilities”. The Partnership similarly asserts the importance of AI operating
“within secure constraints”. The IEEE document meanwhile cites the need to “avoid
misuse”, while the Montreal Declaration argues that those developing AI “should
assume their responsibility by working against the risks arising from their techno-
logical innovations”, echoed by the EGE’s similar need for responsibility.
From these various warnings, it is not entirely clear whether it is the people devel-
oping AI, or the technology itself, which should be encouraged not to do harm—in
other words, whether it is Frankenstein or his monster against whose maleficence we
should be guarding. Confused also is the question of intent: promoting non-malefi-
cence can be seen to incorporate the prevention of both accidental (what we above
call “overuse”) and deliberate (what we call “misuse”) harms arising. In terms of the
principle of non-maleficence, this need not be an either/or question: the point is sim-
ply to prevent harms arising, whether from the intent of humans or the unpredicted
behaviour of machines (including the unintentional nudging of human behaviour
in undesirable ways). Yet these underlying questions of agency, intent and control
become knottier when we consider the next principle.
4.3 Autonomy: The Power toDecide (Whether toDecide)
Another classic tenet of bioethics is the principle of autonomy: the idea that indi-
viduals have a right to make decisions for themselves about the treatment they do or
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L.Floridi et al.
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not receive. In a medical context, this principle of autonomy is most often impaired
when patients lack the mental capacity to make decisions in their own best interests;
autonomy is thus surrendered involuntarily. With AI, the situation becomes rather
more complex: when we adopt AI and its smart agency, we willingly cede some of
our decision-making power to machines. Thus, affirming the principle of autonomy
in the context of AI means striking a balance between the decision-making power
we retain for ourselves and that which we delegate to artificial agents.
The principle of autonomy is explicitly stated in four of the six documents.
The Montreal Declaration articulates the need for a balance between human- and
machine-led decision-making, stating that “the development of AI should promote
the autonomy of all human beings and control … the autonomy of computer sys-
tems” (italics added). The EGE argues that autonomous systems “must not impair
[the] freedom of human beings to set their own standards and norms and be able to
live according to them”, while AIUK adopts the narrower stance that “the auton-
omous power to hurt, destroy or deceive human beings should never be vested in
AI”. The Asilomar document similarly supports the principle of autonomy, insofar
as “humans should choose how and whether to delegate decisions to AI systems, to
accomplish human-chosen objectives”.
These documents express a similar sentiment in slightly different ways, echo-
ing the distinction drawn above between beneficence and non-maleficence: not only
should the autonomy of humans be promoted, but also the autonomy of machines
should be restricted and made intrinsically reversible, should human autonomy need
to be re-established (consider the case of a pilot able to turn off the automatic pilot
and regain full control of the airplane). Taken together, the central point is to protect
the intrinsic value of human choice—at least for significant decisions—and, as a
corollary, to contain the risk of delegating too much to machines. Therefore, what
seems most important here is what we might call “meta-autonomy”, or a “decide-to-
delegate” model: humans should always retain the power to decide which decisions
to take, exercising the freedom to choose where necessary, and ceding it in cases
where overriding reasons, such as efficacy, may outweigh the loss of control over
decision-making. As anticipated, any delegation should remain overridable in prin-
ciple (deciding to decide again).
The decision to make or delegate decisions does not take place in a vacuum. Nor
is this capacity to decide (to decide, and to decide again) distributed equally across
society. The consequences of this potential disparity in autonomy are addressed in
the final of the four principles inspired by bioethics.
4.4 Justice: Promoting Prosperity andPreserving Solidarity
The last of the four classic bioethics principles is justice, which is typically invoked
in relation to the distribution of resources, such as new and experimental treatment
options or simply the general availability of conventional healthcare. Again, this
bioethics principle finds clear echoes across the principles for AI that we analyse.
The importance of “justice” is explicitly cited in the Montreal Declaration, which
argues that “the development of AI should promote justice and seek to eliminate all
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AI4People—An Ethical Framework foraGood AI Society:…
types of discrimination”, while the Asilomar Principles include the need for both
“shared benefit” and “shared prosperity” from AI. Under its principle named “Jus-
tice, equity and solidarity”, the EGE argues that AI should “contribute to global
justice and equal access to the benefits” of AI technologies. It also warns against
the risk of bias in datasets used to train AI systems, and—unique among the docu-
ments—argues for the need to defend against threats to “solidarity”, including “sys-
tems of mutual assistance such as in social insurance and healthcare”. The emphasis
on the protection of social support systems may reflect geopolitics, insofar as the
EGE is a European body. The AIUK report argues that citizens should be able to
“flourish mentally, emotionally and economically alongside artificial intelligence”.
The Partnership, meanwhile, adopts a more cautious framing, pledging to “respect
the interests of all parties that may be impacted by AI advances”.
As with the other principles already discussed, these interpretations of what jus-
tice means as an ethical principle in the context of AI are broadly similar, yet con-
tain subtle distinctions. Across the documents, justice variously relates to
(a) Using AI to correct past wrongs such as eliminating unfair discrimination;
(b) Ensuring that the use of AI creates benefits that are shared (or at least shareable);
(c) Preventing the creation of new harms, such as the undermining of existing social
Notable also are the different ways in which the position of AI, vis-à-vis people,
is characterised in relation to justice. In Asilomar and EGE respectively, it is AI
technologies themselves that “should benefit and empower as many people as pos-
sible” and “contribute to global justice”, whereas in Montreal, it is “the develop-
ment of AI” that “should promote justice” (italics added). In AIUK, meanwhile,
people should flourish merely “alongside” AI. Our purpose here is not to split
semantic hairs. The diverse ways in which the relationship between people and AI
is described in these documents hints at broader confusion over AI as a man-made
reservoir of “smart agency”. Put simply, and to resume our bioethics analogy, are we
(humans) the patient, receiving the “treatment” of AI, the doctor prescribing it? Or
both? It seems that we must resolve this question before seeking to answer the next
question of whether the treatment will even work. This is the core justification for
our identification within these documents of a new principle, one that is not drawn
from bioethics.
4.5 Explicability: Enabling theOther Principles Through Intelligibility
The short answer to the question of whether “we” are the patient or the doctor is that
actually we could be either—depending on the circumstances and on who “we” are
in our everyday life. The situation is inherently unequal: a small fraction of human-
ity is currently engaged in the design and development of a set of technologies that
are already transforming the everyday lives of just about everyone else. This stark
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L.Floridi et al.
1 3
reality is not lost on the authors whose documents we analyse. In all, reference is
made to the need to understand and hold to account the decision-making processes
of AI. This principle is expressed using different terms: “transparency” in Asilomar;
“accountability” in EGE; both “transparency” and “accountability” in IEEE; “intel-
ligibility” in AIUK; and as “understandable and interpretable” for the Partnership.
Though described in different ways, each of these principles captures something
seemingly novel about AI: that its workings are often invisible or unintelligible to all
but (at best) the most expert observers.
The addition of this principle, which we synthesise as “explicability” both in the
epistemological sense of “intelligibility” (as an answer to the question “how does
it work?”) and in the ethical sense of “accountability” (as an answer to the ques-
tion: “who is responsible for the way it works?”), is therefore the crucial missing
piece of the jigsaw when we seek to apply the framework of bioethics to the ethics
of AI. It complements the other four principles: for AI to be beneficent and non-
maleficent, we must be able to understand the good or harm it is actually doing to
society, and in which ways; for AI to promote and not constrain human autonomy,
our “decision about who should decide” must be informed by knowledge of how AI
would act instead of us; and for AI to be just, we must ensure that the technology—
or, more accurately, the people and organisations developing and deploying it—are
held accountable in the event of a negative outcome, which would require in turn
some understanding of why this outcome arose. More broadly, we must negotiate
the terms of the relationship between ourselves and this transformative technology,
on grounds that are readily understandable to the proverbial person “on the street”.
Taken together, we argue that these five principles capture the meaning of each of
the 47 principles contained in the six high-profile, expert-driven documents, form-
ing an ethical framework within which we offer our recommendations below. This
framework of principles is shown in Fig.2.
5 Recommendations foraGood AI Society
This section introduces the Recommendations for a Good AI Society. It consists of
two parts: a Preamble, and 20 Action Points.
Fig. 2 An ethical framework for AI, formed of four traditional principles and a new one
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AI4People—An Ethical Framework foraGood AI Society:…
There are four kinds of Action Points: to assess, to develop, to incentivise and to
support. Some recommendations may be undertaken directly, by national or Euro-
pean policy makers, in collaboration with stakeholders where appropriate. For oth-
ers, policy makers may play an enabling role for efforts undertaken or led by third
5.1 Preamble
We believe that, in order to create a Good AI Society, the ethical principles identi-
fied in the previous section should be embedded in the default practices of AI. In
particular, AI should be designed and developed in ways that decrease inequality
and further social empowerment, with respect for human autonomy, and increase
benefits that are shared by all, equitably. It is especially important that AI be expli-
cable, as explicability is a critical tool to build public trust in, and understanding of,
the technology.
We also believe that creating a Good AI Society requires a multistakeholder
approach, which is the most effective way to ensure that AI will serve the needs of
society, by enabling developers, users and rule-makers to be on board and collabo-
rating from the outset.
Different cultural frameworks inform attitudes to new technology. This docu-
ment represents a European approach, which is meant to be complementary to other
approaches. We are committed to the development of AI technology in a way that
secures people’s trust, serves the public interest, and strengthens shared social
Finally, this set of recommendations should be seen as a “living document”. The
Action Points are designed to be dynamic, requiring not simply single policies or
one-off investments, but rather, continuous, ongoing efforts for their effects to be
5.2 Action Points
5.2.1 Assessment
1. Assess the capacity of existing institutions, such as national civil courts, to redress
the mistakes made or harms inflicted by AI systems. This assessment should
evaluate the presence of sustainable, majority-agreed foundations for liability
from the design stage onwards, in order to reduce negligence and conflicts (see
also Recommendation 5).6
2. Assess which tasks and decision-making functionalities should not be delegated to
AI systems, through the use of participatory mechanisms to ensure alignment with
6 Determining accountability and responsibility may usefully borrow from lawyers in Ancient Rome
who would go by this formula ‘cuius commoda eius et incommoda’ (‘the person who derives an advan-
tage from a situation must also bear the inconvenience’). A good 2200years old principle that has a well-
established tradition and elaboration could properly set the starting level of abstraction in this field.
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L.Floridi et al.
1 3
societal values and understanding of public opinion. This assessment should take
into account existing legislation and be supported by ongoing dialogue between
all stakeholders (including government, industry, and civil society) to debate how
AI will impact society opinion (in concert with Recommendation 17).
3. Assess whether current regulations are sufficiently grounded in ethics to provide a
legislative framework that can keep pace with technological developments. This
may include a framework of key principles that would be applicable to urgent
and/or unanticipated problems.
5.2.2 Development
4. Develop a framework to enhance the explicability of AI systems that make
socially significant decisions. Central to this framework is the ability for individu-
als to obtain a factual, direct, and clear explanation of the decision-making pro-
cess, especially in the event of unwanted consequences. This is likely to require
the development of frameworks specific to different industries, and professional
associations should be involved in this process, alongsideexperts in science,
business, law, and ethics.
5. Develop appropriate legal procedures and improve the IT infrastructure of the
justice system to permit the scrutiny of algorithmic decisions in court. This is
likely to include the creation of a framework for AI explainability as indicated in
Recommendation 4, specific to the legal system. Examples of appropriate proce-
dures may include the applicable disclosure of sensitive commercial information
in IP litigation, and—where disclosure poses unacceptable risks, for instance to
national security—the configuration of AI systems to adopt technical solutions by
default, such as zero-knowledge proofs in order to evaluate their trustworthiness.
6. Develop auditing mechanisms for AI systems to identify unwanted consequences,
such as unfair bias, and (for instance, in cooperation with the insurance sector)
a solidarity mechanism to deal with severe risks in AI-intensive sectors. Those
risks could be mitigated by multistakeholder mechanisms upstream. Pre-digital
experience indicates that, in some cases, it may take a couple of decades before
society catches up with technology by way of rebalancing rights and protec-
tion adequately to restore trust. The earlier that users and governments become
involved—as made possible by ICT—the shorter this lag will be.
7. Develop a redress process or mechanism to remedy or compensate for a wrong
or grievance caused by AI. To foster public trust in AI, society needs a widely
accessible and reliable mechanism of redress for harms inflicted, costs incurred,
or other grievances caused by the technology. Such a mechanism will necessarily
involve a clear and comprehensive allocation of accountability to humans and/or
organisations. Lessons could be learnt from the aerospace industry, for example,
which has a proven system of handling unwanted consequences thoroughly and
seriously. The development of this process must follow from the assessment of
existing capacity outlined in Recommendation 1. If a lack of capacity is identi-
fied, additional institutional solutions should be developed at national and/or EU
levels, to enable people to seek redress. Such solutions may include:
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AI4People—An Ethical Framework foraGood AI Society:…
An “AI ombudsperson” to ensure the auditing of allegedly unfair or inequita-
ble uses of AI;
A guided process for registering a complaint akin to making a Freedom of
Information request; and
The development of liability insurance mechanisms, which would be required
as an obligatory accompaniment of specific classes of AI offerings in EU and
other markets. This would ensure that the relative reliability of AI-powered
artefacts, especially in robotics, is mirrored in insurance pricing and therefore
in the market prices of competing products.7
Whichever solutions are developed, these are likely to rely on the framework for
intelligibility proposed in Recommendation 4.
8. Develop agreed-upon metrics for the trustworthiness of AI products and ser-
vices, to be undertaken either by a new organisation, or by a suitable existing
organisation. These metrics would serve as the basis for a system that enables
the user-driven benchmarking of all marketed AI offerings. In this way, an index
for trustworthy AI can be developed and signalled, in addition to a product’s
price. This “trust comparison index” for AI would improve public understanding
and engender competitiveness around the development of safer, more socially
beneficial AI (e.g., “”). In the longer term, such a system could
form the basis for a broader system of certification for deserving products and
services, administered by the organisation noted here, and/or by the oversight
agency proposed in Recommendation 9. The organisation could also support
the development of codes of conduct (see Recommendation 18). Furthermore,
those who own or operate inputs to AI systems and profit from it could be tasked
with funding and/or helping to develop AI literacy programs for consumers, in
their own best interest.
9. Develop a new EU oversight agency responsible for the protection of public wel-
fare through the scientific evaluation and supervision of AI products, software,
systems, or services. This may be similar, for example, to the European Medi-
cines Agency. Relatedly, a “post-release” monitoring system for AIs similar to,
for example, the one available for drugs should be developed, with reporting
duties for some stakeholders and easy reporting mechanisms for other users.
10. Develop a European observatory for AI. The mission of the observatory would
be to watch developments, provide a forum to nurture debate and consensus,
provide a repository for AI literature and software (including concepts and links
to available literature), and issue step-by-step recommendation and guidelines
for action.
11. Develop legal instruments and contractual templates to lay the foundation for a
smooth and rewarding human–machine collaboration in the work environment.
7 Of course, to the extent that AI systems are ‘products’, general tort law still applies in the same way to
AI as it applies in any instance involving defective products or services that injure users or do not per-
form as claimed or expected.
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L.Floridi et al.
1 3
Shaping the narrative on the ‘Future of Work’ is instrumental to winning “hearts
and minds”. In keeping with ‘A Europe that protects’, the idea of “inclusive
innovation” and to smooth the transition to new kinds of jobs, a European AI
Adjustment Fund could be set up along the lines of the European Globalisation
Adjustment Fund.
5.2.3 Incentivisation
12. Incentivise financially, at the EU level, the development and use of AI tech-
nologies within the EU that are socially preferable (not merely acceptable) and
environmentally friendly (not merely sustainable but favourable to the environ-
ment). This will include the elaboration of methodologies that can help assess
whether AI projects are socially preferable and environmentally friendly. In this
vein, adopting a ‘challenge approach’ (see DARPA challenges) may encourage
creativity and promote competition in the development of specific AI solutions
that are ethically sound and in the interest of the common good.
13. Incentivise financially a sustained, increased and coherent European research
effort, tailored to the specific features of AI as a scientific field of investigation.
This should involve a clear mission to advance AI for social good, to serve as a
unique counterbalance to AI trends with less focus on social opportunities.
14. Incentivise financially cross-disciplinary and cross-sectoral cooperation and
debate concerning the intersections between technology, social issues, legal
studies, and ethics. Debates about technological challenges may lag behind the
actual technical progress, but if they are strategically informed by a diverse,
multistakeholder group, they may steer and support technological innovation in
the right direction. Ethics should help seize opportunities and cope with chal-
lenges, not only describe them. It is essential in this respect that diversity infuses
the design and development of AI, in terms of gender, class, ethnicity, discipline
and other pertinent dimensions, in order to increase inclusivity, toleration, and
the richness of ideas and perspectives.
15. Incentivise financially the inclusion of ethical, legal and social considerations
in AI research projects. In parallel, incentivise regular reviews of legislation to
test the extent to which it fosters socially positive innovation. Taken together,
these two measures will help ensure that AI technology has ethics at its heart
and that policy is oriented towards innovation.
16. Incentivise financially the development and use of lawfully de-regulated special
zones within the EU for the empirical testing and development of AI systems.
These zones may take the form of a “living lab” (or Tokku), building on the
experience of existing “test highways” (or Teststrecken). In addition to aligning
innovation more closely with society’s preferred level of risk, sandbox experi-
ments such as these contribute to hands-on education and the promotion of
accountability and acceptability at an early stage. “Protection by design” is
intrinsic to this kind of framework.
17. Incentivise financially research about public perception and understanding of
AI and its applications, and the implementation of structured public consulta-
tion mechanisms to design policies and rules related to AI. This may include
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AI4People—An Ethical Framework foraGood AI Society:…
the direct elicitation of public opinion via traditional research methods, such
as opinion polls and focus groups, as well as more experimental approaches,
such as providing simulated examples of the ethical dilemmas introduced by AI
systems, or experiments in social science labs. This research agenda should not
serve merely to measure public opinion, but should also lead to the co-creation
of policies, standards, best practices, and rules as a result.
5.2.4 Support
18. Support the development of self-regulatory codes of conduct for data and AI
related professions, with specific ethical duties. This would be along the lines
of other socially sensitive professions, such as medical doctors or lawyers, i.e.,
with the attendant certification of ‘ethical AI’ through trust-labels to make sure
that people understand the merits of ethical AI and will therefore demand it
from providers. Current attention manipulation techniques may be constrained
through these self-regulating instruments.
19. Support the capacity of corporate boards of directors to take responsibility for
the ethical implications of companies’ AI technologies. For example, this may
include improved training for existing boards and the potential development
of an ethics committee with internal auditing powers. This could be developed
within the existing structure of both one-tier and two-tier board systems, and/or
in conjunction with the development of a mandatory form of “corporate ethical
review board” to be adopted by organisations developing or using AI systems,
to evaluate initial projects and their deployment with respect to fundamental
20. Support the creation of educational curricula and public awareness activities
around the societal, legal, and ethical impact of Artificial Intelligence. This may
Curricula for schools, supporting the inclusion of computer science among
the basic disciplines to be taught;
Initiatives and qualification programmes in businesses dealing with AI tech-
nology, to educate employees on the societal, legal, and ethical impact of
working alongside AI;
A European-level recommendation to include ethics and human rights in the
degrees of data and AI scientists and other scientific and engineering curric-
ula dealing with computational and AI systems;
The development of similar programmes for the public at large, with a spe-
cial focus on those involved at each stage of management of the technology,
including civil servants, politicians and journalists;
Engagement with wider initiatives such as the ITU AI for Good events and
NGOs working on the UN Sustainable Development Goals.
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L.Floridi et al.
1 3
6 Conclusion
Europe, and the world at large, face the emergence of a technology that holds much
exciting promise for many aspects of human life, and yet seems to pose major threats
as well. This article—and especially the Recommendations in the previous section—
seek to nudge the tiller in the direction of ethically and socially preferable outcomes
from the development, design and deployment of AI technologies. Building on our
identification of both the core opportunities and the risks of AI for society as well as
the set of five ethical principles we synthesised to guide its adoption, we formulated
20 Action Points in the spirit of collaboration and in the interest of creating concrete
and constructive responses to the most pressing social challenges posed by AI.
With the rapid pace of technological change, it can be tempting to view the politi-
cal process in the liberal democracies of today as old-fashioned, out-of-step, and no
longer up to the task of preserving the values and promoting the interests of society
and everyone in it. We disagree. With the Recommendations we offer here, includ-
ing the creation of centres, agencies, curricula, and other infrastructure, we have
made the case for an ambitious, inclusive, equitable programme of policy making
and technological innovation, which we believe will contribute to securing the ben-
efits and mitigating the risks of AI, for all people, and for the world we share.
Acknowledgements This publication would not have been possible without the generous support of Ato-
mium—European Institute for Science, Media and Democracy. We are particularly grateful to Michelan-
gelo Baracchi Bonvicini, Atomium’s President, to Guido Romeo, its Editor in Chief, the staff of Atomium
for their help, and to all the partners of the AI4People project and members of its Forum (http://www.
eismd .eu/ai4pe ople) for their feedback. Luciano Floridi’s work has also been supported by the Privacy-
Enhancing and Identification-Enabling Solutions for IoT (PEIESI) project, part of the PETRAS Internet
of Things research hub, funded by the Engineering and Physical Sciences Research Council (EPSRC),
grant agreement no. EP/N023013/1. The authors of this article are the only persons responsible for its
contents and any remaining mistakes.
Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 Interna-
tional License (http://creat iveco mmons .org/licen ses/by/4.0/), which permits unrestricted use, distribution,
and reproduction in any medium, provided you give appropriate credit to the original author(s) and the
source, provide a link to the Creative Commons license, and indicate if changes were made.
Asilomar AI Principles. (2017). Principles developed in conjunction with the 2017 Asilomar conference
[Benevolent AI 2017]. Retrieved September 18, 2018 from https ://futur eofli iples .
Cowls, J., & Floridi, L. (2018). Prolegomena to a White Paper on Recommendations for the Ethics of AI
(June 19, 2018). Available at SSRN: https :// act=31987 32.
Cowls, J., & Floridi, L. (Forthcoming). The Utility of a Principled Approach to AI Ethics.
European Group on Ethics in Science and New Technologies. (2018). Statement on Artificial Intelligence,
Robotics and ‘Autonomous’ Systems. Retrieved September 18, 2018 from https ://ec.europ
news/ethic s-artifi cial -intel ligen ce-state ment-ege-relea sed-2018-apr-24_en.
Floridi, L. (2013). The ethics of information. Oxford: Oxford University Press.
Floridi, L. (2018). Soft ethics and the governance of the digital. Philosophy & Technology, 31(1), 1–8.
House of Lords Artificial Intelligence Committee. (2018). AI in the UK: ready, willing and able?
Retrieved September 18, 2018 from https ://publi catio ns.parli ament .uk/pa/ld201 719/ldsel ect/
ldai/100/10002 .htm.
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
1 3
AI4People—An Ethical Framework foraGood AI Society:…
Imperial College London. (2017). Written Submission to House of Lords Select Committee on Artificial
Intelligence [AIC0214]. Retrieved September 18, 2018 from ET.
King, T., Aggarwal, N., Taddeo, M., & Floridi, L. (2018). Artificial Intelligence Crime: An Interdisci-
plinary Analysis of Foreseeable Threats and Solutions. Available at SSRN: https ://
act=31832 38.
Montreal Declaration for a Responsible Development of Artificial Intelligence. (2017). Announced at the
conclusion of the Forum on the Socially Responsible Development of AI. Retrieved September 18,
2018 from https ://www.montr ealde clara tion-respo nsibl ratio n.
Partnership on AI. (2018). Tenets. Retrieved September 18, 2018 from https ://www.partn ershi ponai .org/
tenet s/.
Taddeo, M. (2018). The limits of deterrence theory in cyberspace. Philosophy & Technology, 31(3),
The IEEE Initiative on Ethics of Autonomous and Intelligent Systems. (2017). Ethically Aligned Design,
v2. Retrieved September 18, 2018 from https ://ethic sinac
LucianoFloridi1,2 · JoshCowls1,2· MonicaBeltrametti3· RajaChatila4,5·
PatriceChazerand6· VirginiaDignum7,8· ChristophLuetge9· RobertMadelin10·
UgoPagallo11· FrancescaRossi12,13· BurkhardSchafer14· PeggyValcke15,16·
1 Oxford Internet Institute, University ofOxford, Oxford, UK
2 The Alan Turing Institute, London, UK
3 Naver Corporation, Grenoble, France
4 French National Center ofScientific Research, Paris, France
5 Institute ofIntelligent Systems andRobotics, Pierre andMarie Curie University, Paris, France
6 Digital Europe, Brussels, Belgium
7 University ofUmeå, Umeå, Sweden
8 Delft Design forValues Institute, Delft University ofTechnology, Delft, TheNetherlands
9 TUM School ofGovernance, Technical University ofMunich, Munich, Germany
10 Centre forTechnology andGlobal Affairs, University ofOxford, Oxford, UK
11 Department ofLaw, University ofTurin, Turin, Italy
12 IBM Research, NewYork, USA
13 University ofPadova, Padua, Italy
14 University ofEdinburgh Law School, Edinburgh, UK
15 Centre forIT & IP Law, Catholic University ofLeuven, Flanders, Belgium
16 Bocconi University, Milan, Italy
17 Bioethics, Health Ethics andPolicy Lab, ETH Zurich, Zurich, Switzerland
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... Other remarkable initiatives on AI ethics are "The Asilomar Principles for the Future of Artificial Intelligence" [4], "The OnLife Manifesto" [5], "The Manifesto for Conscientious Design of Hybrid Online Social Systems", and "Responsible Artificial Intelligence" [6]. Stemming from these works, we can highlight several points that can be added to the IEEE principles to flesh them out: (i) the importance of explainability (or explicability) to steer clear of opaque decisions [7]; (ii) the emergence of machine ethics, or "how a machine could act ethically in an autonomous fashion" [8], and (iii) the development of bias-averse strategies to minimise negative impacts in society, avoiding the risks of harming vulnerable people. ...
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This paper investigates the multifaceted issue of algorithmic bias in artificial intelligence (AI) systems and explores its ethical and human rights implications. The study encompasses a comprehensive analysis of AI bias, its causes, and potential remedies, with a particular focus on its impact on individuals and marginalized communities.
L’Explicabilité de l’Intelligence Artificielle (IA) est citée par la littérature comme un pilier de l’éthique de l’IA. Mais rares sont les études qui explorent sa réalité organisationnelle. Cette étude propose de remédier à ce manque, à travers des interviews d’acteurs en charge de concevoir et déployer des IA au sein de 17 organisations. Nos résultats mettent en lumière la substitution massive de l’explicabilité par la mise en avant d’indicateurs de performance ; la substitution de l’exigence de compréhension par une exigence d’ accountability (rendre des comptes) ; et la place ambiguë des experts métiers au sein des processus de conception, mobilisés pour valider l’apparente cohérence d’algorithmes « boîtes noires » plutôt que pour les ouvrir et les comprendre. Dans la pratique organisationnelle, l’explicabilité apparaît alors comme suffisamment indéfinie pour faire coïncider des injonctions contradictoires. Comparant les prescriptions de la littérature et les pratiques sur le terrain, nous discutons du risque de cristallisation de ces problématiques organisationnelles via la standardisation des outils de gestion utilisés dans le cadre de (ou à la place de) l’effort d’explicabilité des IA.
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The first part of this paper contextualizes the debate on the ethical regulation of Artificial Intelligence, and then reviews the main Anglo-Eurocentric elements of public initiatives about the 'Ethics of AI' that have recently emerged. All this with the ultimate purpose of analyzing the basis of principlism of the Ethics of AI. We refer to this with the specific purpose of presenting it in terms of a paradigmatic case of 'colonized' ethics that hides the different moral judgments and the alternative cultural axiologies. We begin with the hypothesis that there is a possibility of questioning the real purpose and contribution of this applied ethics, using a dilemmatic perspective specific to human and legal sciences. To execute this type of analysis, which is highly required to understand what the future of these proposals is and how it can affect the regulation of AI, an analysis related to the contemporary literature from the Decoloniality field will be performed.
The scope of application of generative artificial intelligence (GAI) in industrial functions is gaining high prominence in academic and industrial discourses. In this article, we explore the usage of GAI and large language models (LLMs) in industrial applications. It promises myriad advantages such as greater engagement, cooperation and accessibility. LLMs like ChatGPT are able to evaluate unstructured queries, assess alternatives and offer actionable advice to users. It is being used to produce fast reports, flexible responses, environment scanning capabilities and insights that can enhance organisation flexibility in making better and quicker decisions, improving customer experiences and thereby augmenting firm profitability. This article offers a comprehensive review of scientific and grey literature in GAI and language models. The synthesis of complementary sources of information brings exciting perspectives in this fast evolving field. We provide directions surrounding future use of GAI as well as research directions for management researchers.
Purpose In spite of the merits of artificial intelligence (AI) in marketing and social media, harm to consumers has prompted calls for AI auditing/certification. Understanding consumers’ approval of AI certification entities is vital for its effectiveness and companies’ choice of certification. This study aims to generate important insights into the consumer perspective of AI certifications and stimulate future research. Design/methodology/approach A literature and status-quo-driven search of the AI certification landscape identifies entities and related concepts. This study empirically explores consumer approval of the most discussed entities in four AI decision domains using an online experiment and outline a research agenda for AI certification in marketing/social media. Findings Trust in AI certification is complex. The empirical findings show that consumers seem to approve more of non-profit entities than for-profit entities, with the government approving the most. Research limitations/implications The introduction of AI certification to marketing/social media contributes to work on consumer trust and AI acceptance and structures AI certification research from outside marketing to facilitate future research on AI certification for marketing/social media scholars. Practical implications For businesses, the authors provide a first insight into consumer preferences for AI-certifying entities, guiding the choice of which entity to use. For policymakers, this work guides their ongoing discussion on “who should certify AI” from a consumer perspective. Originality/value To the best of the authors’ knowledge, this work is the first to introduce the topic of AI certification to the marketing/social media literature, provide a novel guideline to scholars and offer the first set of empirical studies examining consumer approval of AI certifications.
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Artificial intelligence (AI) research and regulation seek to balance the benefits of innovation against any potential harms and disruption. However, one unintended consequence of the recent surge in AI research is the potential re-orientation of AI technologies to facilitate criminal acts, term in this article AI-Crime (AIC). AIC is theoretically feasible thanks to published experiments in automating fraud targeted at social media users, as well as demonstrations of AI-driven manipulation of simulated markets. However, because AIC is still a relatively young and inherently interdisciplinary area—spanning socio-legal studies to formal science—there is little certainty of what an AIC future might look like. This article offers the first systematic, interdisciplinary literature analysis of the foreseeable threats of AIC, providing ethicists, policy-makers, and law enforcement organisations with a synthesis of the current problems, and a possible solution space.
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What is the relation between the ethics, the law, and the governance of the digital? In this article I articulate and defend what I consider the most reasonable answer.
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In this article, I analyse deterrence theory and argue that its applicability to cyberspace is limited and that these limits are not trivial. They are the consequence of fundamental differences between deterrence theory and the nature of cyber conflicts and cyberspace. The goals of this analysis are to identify the limits of deterrence theory in cyberspace, clear the ground of inadequate approaches to cyber deterrence, and define the conceptual space for a domain-specific theory of cyber deterrence, still to be developed.
This is the first philosophical monograph entirely and exclusively dedicated to Information Ethics.Information and Communication Technologies (ICTs) have profoundly changed many aspects of life, including the nature of entertainment, work, communication, education, health care, industrial production and business, social relations, and conflicts.Therefore, they have had a radical and widespread impact on our moral lives and on contemporary ethical debates. Privacy, ownership, freedom of speech, responsibility, technological determinism, the digital divide, online pornography, are only some of the pressing issues that characterize the ethical discourse in the information society. They are the subject of Information Ethics (IE), the new philosophical area of research that investigates the ethical impact of ICTs on human life and society.The book lays down, for the first time, the conceptual foundations for Information Ethics. It does so systematically, by pursuing three goals:a). metatheoretical goal: it describes what Information Ethics is, its problems, approaches, and methods;b). introductory goal: it helps the reader to gain a better grasp of the complex and multifarious nature of the various concepts and phenomena related to Information Ethics;c) analytic goal: it answers several key theoretical questions of great philosophical interest, arising from the investigation of the ethical implications of ICTs.Although entirely independent of The Philosophy of Information (OUP, 2011), the previous book by the same author, it complements it as part of the tetralogy on the foundations of the philosophy of information (Principia Philosophiae Informationis).
Principles developed in conjunction with the 2017 Asilomar conference
  • A I Asilomar
  • Principles
Asilomar AI Principles. (2017). Principles developed in conjunction with the 2017 Asilomar conference [Benevolent AI 2017].
Forthcoming). The Utility of a Principled Approach to AI Ethics
  • J Cowls
  • L Floridi
Cowls, J., & Floridi, L. (Forthcoming). The Utility of a Principled Approach to AI Ethics.
Prolegomena to a White Paper on Recommendations for the Ethics of AI
  • J Cowls
  • L Floridi
Cowls, J., & Floridi, L. (2018). Prolegomena to a White Paper on Recommendations for the Ethics of AI (June 19, 2018). Available at SSRN: https :// act=31987 32.
Written Submission to House of Lords Select Committee on Artificial Intelligence
  • Imperial College London
House of Lords Artificial Intelligence Committee
  • L Floridi
Floridi, L. (2018). Soft ethics and the governance of the digital. Philosophy & Technology, 31(1), 1-8. House of Lords Artificial Intelligence Committee. (2018). AI in the UK: ready, willing and able? Retrieved September 18, 2018 from https ://publi catio ns.parli 719/ldsel ect/ ldai/100/10002.htm.