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PERSPECTIVE
The role of artificial intelligence in achieving the
Sustainable Development Goals
Ricardo Vinuesa 1*, Hossein Azizpour 2, Iolanda Leite2, Madeline Balaam3,
Virginia Dignum4,SamiDomisch 5, Anna Felländer6, Simone Daniela Langhans7,8,
Max Tegmark9& Francesco Fuso Nerini 10*
The emergence of artificial intelligence (AI) and its progressively wider impact on many
sectors requires an assessment of its effect on the achievement of the Sustainable Devel-
opment Goals. Using a consensus-based expert elicitation process, we find that AI can enable
the accomplishment of 134 targets across all the goals, but it may also inhibit 59 targets.
However, current research foci overlook important aspects. The fast development of AI needs
to be supported by the necessary regulatory insight and oversight for AI-based technologies
to enable sustainable development. Failure to do so could result in gaps in transparency,
safety, and ethical standards.
The emergence of artificial intelligence (AI) is shaping an increasing range of sectors. For
instance, AI is expected to affect global productivity1, equality and inclusion2, environ-
mental outcomes3, and several other areas, both in the short and long term4. Reported
potential impacts of AI indicate both positive5and negative6impacts on sustainable develop-
ment. However, to date, there is no published study systematically assessing the extent to which
AI might impact all aspects of sustainable development—defined in this study as the 17 Sus-
tainable Development Goals (SDGs) and 169 targets internationally agreed in the 2030 Agenda
for Sustainable Development7. This is a critical research gap, as we find that AI may influence the
ability to meet all SDGs.
Here we present and discuss implications of how AI can either enable or inhibit the delivery of
all 17 goals and 169 targets recognized in the 2030 Agenda for Sustainable Development.
Relationships were characterized by the methods reported at the end of this study, which can be
summarized as a consensus-based expert elicitation process, informed by previous studies aimed
at mapping SDGs interlinkages8–10. A summary of the results is given in Fig. 1and the Sup-
plementary Data 1 provides a complete list of all the SDGs and targets, together with the detailed
results from this work. Although there is no internationally agreed definition of AI, for this study
we considered as AI any software technology with at least one of the following capabilities:
perception—including audio, visual, textual, and tactile (e.g., face recognition), decision-making
(e.g., medical diagnosis systems), prediction (e.g., weather forecast), automatic knowledge
https://doi.org/10.1038/s41467-019-14108-y OPEN
1Linné FLOW Centre, KTH Mechanics, SE-100 44 Stockholm, Sweden. 2Division of Robotics, Perception, and Learning, School of EECS, KTH Royal Institute
Of Technology, Stockholm, Sweden. 3Division of Media Technology and Interaction Design, KTH Royal Institute of Technology, Lindstedtsvägen 3,
Stockholm, Sweden. 4Responsible AI Group, Department of Computing Sciences, Umeå University, SE-90358 Umeå, Sweden. 5Leibniz-Institute of
Freshwater Ecology and Inland Fisheries, Müggelseedamm 310, 12587 Berlin, Germany. 6AI Sustainability Center, SE-114 34 Stockholm, Sweden. 7Basque
Centre for Climate Change (BC3), 48940 Leioa, Spain. 8Department of Zoology, University of Otago, 340 Great King Street, 9016 Dunedin, New Zealand.
9Center for Brains, Minds and Machines, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA. 10 Unit of Energy Systems Analysis
(dESA), KTH Royal Institute of Technology, Brinellvagen, 68SE-1004 Stockholm, Sweden. *email: rvinuesa@mech.kth.se;francesco.fusonerini@energy.kth.se
NATURE COMMUNICATIONS | (2020) 11:233 | https://doi.org/10.1038/s41467-019-14108-y | www.nature.com/naturecommunications 1
1234567890():,;
extraction and pattern recognition from data (e.g., discovery of
fake news circles in social media), interactive communication
(e.g., social robots or chat bots), and logical reasoning (e.g., theory
development from premises). This view encompasses a large
variety of subfields, including machine learning.
Documented connections between AI and the SDGs
Our review of relevant evidence shows that AI may act as an
enabler on 134 targets (79%) across all SDGs, generally through a
technological improvement, which may allow to overcome certain
present limitations. However, 59 targets (35%, also across all
SDGs) may experience a negative impact from the development
of AI. For the purpose of this study, we divide the SDGs into
three categories, according to the three pillars of sustainable
development, namely Society, Economy, and Environment11,12
(see the Methods section). This classification allows us to provide
an overview of the general areas of influence of AI. In Fig. 1,we
also provide the results obtained when weighting how appropriate
is the evidence presented in each reference to assess an inter-
linkage to the percentage of targets assessed, as discussed in the
Methods section and below. A detailed assessment of the Society,
Economy, and Environment groups, together with illustrative
examples, are discussed next.
AI and societal outcomes. Sixty-seven targets (82%) within the
Society group could potentially benefit from AI-based technolo-
gies (Fig. 2). For instance, in SDG 1 on no poverty, SDG 4 on
quality education, SDG 6 on clean water and sanitation, SDG 7 on
affordable and clean energy, and SDG 11 on sustainable cities, AI
may act as an enabler for all the targets by supporting the pro-
vision of food, health, water, and energy services to the popula-
tion. It can also underpin low-carbon systems, for instance, by
supporting the creation of circular economies and smart cities
that efficiently use their resources13,14. For example, AI can
enable smart and low-carbon cities encompassing a range of
interconnected technologies such as electrical autonomous vehi-
cles and smart appliances that can enable demand response in the
electricity sector13,14 (with benefits across SDGs 7, 11, and 13 on
climate action). AI can also help to integrate variable renewables
by enabling smart grids that partially match electrical demand to
times when the sun is shining and the wind is blowing13. Fewer
targets in the Society group can be impacted negatively by AI (31
targets, 38%) than the ones with positive impact. However, their
consideration is crucial. Many of these relate to how the tech-
nological improvements enabled by AI may be implemented in
countries with different cultural values and wealth. Advanced AI
technology, research, and product design may require massive
computational resources only available through large computing
centers. These facilities have a very high energy requirement and
carbon footprint15. For instance, cryptocurrency applications
such as Bitcoin are globally using as much electricity as some
nations’electrical demand16, compromising outcomes in the SDG
7 sphere, but also on SDG 13 on Climate Action. Some estimates
suggest that the total electricity demand of information and
communications technologies (ICTs) could require up to 20% of
the global electricity demand by 2030, from around 1% today15.
Green growth of ICT technology is therefore essential17. More
efficient cooling systems for data centers, broader energy effi-
ciency, and renewable-energy usage in ICTs will all play a role in
containing the electricity demand growth15. In addition to more
efficient and renewable-energy-based data centers, it is essential
to embed human knowledge in the development of AI models.
Besides the fact that the human brain consumes much less energy
than what is used to train AI models, the available knowledge
introduced in the model (see, for instance, physics-informed deep
learning18) does not need to be learnt through data-intensive
training, a fact that may significantly reduce the associated energy
consumption. Although AI-enabled technology can act as a cat-
alyst to achieve the 2030 Agenda, it may also trigger inequalities
100%
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Positive impacts of AI: 79% (71%) Negative impacts of AI: 35% (23%)
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Fig. 1 Summary of positive and negative impact of AI on the various SDGs. Documented evidence of the potential of AI acting as (a) an enabler or (b)an
inhibitor on each of the SDGs. The numbers inside the colored squares represent each of the SDGs (see the Supplementary Data 1). The percentages on
the top indicate the proportion of all targets potentially affected by AI and the ones in the inner circle of the figure correspond to proportions within each
SDG. The results corresponding to the three main groups, namely Society, Economy, and Environment, are also shown in the outer circle of the figure. The
results obtained when the type of evidence is taken into account are shown by the inner shaded area and the values in brackets.
PERSPECTIVE NATURE COMMUNICATIONS | https://doi.org/10.1038/s41467-019-14108-y
2NATURE COMMUNICATIONS | (2020) 11:233 | https://doi.org/10.1038/s41467-019-14108-y | www.nature.com/naturecommunications
that may act as inhibitors on SDGs 1, 4, and 5. This duality is
reflected in target 1.1, as AI can help to identify areas of poverty
and foster international action using satellite images5. On the
other hand, it may also lead to additional qualification require-
ments for any job, consequently increasing the inherent
inequalities19 and acting as an inhibitor towards the achievement
of this target.
Another important drawback of AI-based developments is that
they are traditionally based on the needs and values of nations in
which AI is being developed. If AI technology and big data are
used in regions where ethical scrutiny, transparency, and
democratic control are lacking, AI might enable nationalism,
hate towards minorities, and bias election outcomes20. The term
“big nudging”has emerged to represent using big data and AI to
exploit psychological weaknesses to steer decisions—creating
problems such as damaging social cohesion, democratic princi-
ples, and even human rights21. AI has been recently utilized to
develop citizen scores, which are used to control social
behavior22. This type of score is a clear example of threat to
human rights due to AI misuse and one of its biggest problems is
the lack of information received by the citizens on the type of
analyzed data and the consequences this may have on their lives.
It is also important to note that AI technology is unevenly
distributed: for instance, complex AI-enhanced agricultural
equipment may not be accessible to small farmers and thus
produce an increased gap with respect to larger producers in
more developed economies23, consequently inhibiting the
achievement of some targets of SDG 2 on zero hunger. There is
another important shortcoming of AI in the context of SDG 5 on
gender equality: there is insufficient research assessing the
potential impact of technologies such as smart algorithms, image
recognition, or reinforced learning on discrimination against
women and minorities. For instance, machine-learning algo-
rithms uncritically trained on regular news articles will
inadvertently learn and reproduce the societal biases against
women and girls, which are embedded in current languages.
Word embeddings, a popular technique in natural language
processing, have been found to exacerbate existing gender
stereotypes2. In addition to the lack of diversity in datasets,
another main issue is the lack of gender, racial, and ethnic
diversity in the AI workforce24. Diversity is one of the main
principles supporting innovation and societal resilience, which
will become essential in a society exposed to changes associated to
AI development25. Societal resilience is also promoted by
decentralization, i.e., by the implementation of AI technologies
adapted to the cultural background and the particular needs of
different regions.
AI and economic outcomes. The technological advantages pro-
vided by AI may also have a positive impact on the achievement
of a number of SDGs within the Economy group. We have
identified benefits from AI on 42 targets (70%) from these SDGs,
whereas negative impacts are reported in 20 targets (33%), as
shown in Fig. 1. Although Acemoglu and Restrepo1report a net
positive impact of AI-enabled technologies associated to
increased productivity, the literature also reflects potential nega-
tive impacts mainly related to increased inequalities26–29. In the
context of the Economy group of SDGs, if future markets rely
heavily on data analysis and these resources are not equally
available in low- and middle- income countries, the economical
gap may be significantly increased due to the newly introduced
inequalities30,31 significantly impacting SDGs 8 (decent work and
economic growth), 9 (industry, innovation and infrastructure),
and 10 (reduced inequalities). Brynjolfsson and McAfee31 argue
that AI can exacerbate inequality also within nations. By repla-
cing old jobs with ones requiring more skills, technology dis-
proportionately rewards the educated: since the mid 1970s, the
salaries in the United States (US) salaries rose about 25% for
those with graduate degrees, while the average high-school
dropout took a 30% pay cut. Moreover, automation shifts cor-
porate income to those who own companies from those who
work there. Such transfer of revenue from workers to investors
helps explain why, even though the combined revenues of
Society
Enabler
Inhibitor
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Fig. 2 Detailed assessment of the impact of AI on the SDGs within the Society group. Documented evidence of positive or negative impact of AI on the
achievement of each of the targets from SDGs 1, 2, 3, 4, 5, 6, 7, 11, and 16 (https://www.un.org/sustainabledevelopment/). Each block in the diagram
represents a target (see the Supplementary Data 1 for additional details on the targets). For targets highlighted in green or orange, we found published
evidence that AI could potentially enable or inhibit such target, respectively. The absence of highlighting indicates the absence of identified evidence. It is
noteworthy that this does not necessarily imply the absence of a relationship. (The content of of this figure has not been reviewed by the United Nations
and does not reflect its views).
NATURE COMMUNICATIONS | https://doi.org/10.1038/s41467-019-14108-y PERSPECTIVE
NATURE COMMUNICATIONS | (2020) 11:233 | https://doi.org/10.1038/s41467-019-14108-y | www.nature.com/naturecommunications 3
Detroit's “Big 3”(GM, Ford, and Chrysler) in 1990 were almost
identical to those of Silicon Valley's “Big 3”(Google, Apple, and
Facebook) in 2014, the latter had 9 times fewer employees and
were worth 30 times more on the stock market32. Figure 3shows
an assessment of the documented positive and negative effects on
the various targets within the SDGs in the Economy group.
Although the identified linkages in the Economy group are
mainly positive, trade-offs cannot be neglected. For instance, AI
can have a negative effect on social media usage, by showing users
content specifically suited to their preconceived ideas. This may
lead to political polarization33 and affect social cohesion21 with
consequences in the context of SDG 10 on reduced inequalities.
On the other hand, AI can help identify sources of inequality and
conflict34,35, and therewith potentially reduce inequalities, for
instance, by using simulations to assess how virtual societies may
respond to changes. However, there is an underlying risk when
using AI to evaluate and predict human behavior, which is the
inherent bias in the data. It has been reported that a number of
discriminatory challenges are faced in the automated targeting of
online job advertising using AI35, essentially related to the
previous biases in selection processes conducted by human
recruiters. The work by Dalenberg35 highlights the need of
modifying the data preparation process and explicitly adapting
the AI-based algorithms used for selection processes to avoid
such biases.
AI and environmental outcomes. The last group of SDGs, i.e.,
the one related to Environment, is analyzed in Fig. 4. The three
SDGs in this group are related to climate action, life below water
and life on land (SDGs 13, 14, and 15). For the Environment
group, we identified 25 targets (93%) for which AI could act as an
enabler. Benefits from AI could be derived by the possibility of
analyzing large-scale interconnected databases to develop joint
actions aimed at preserving the environment. Looking at SDG 13
on climate action, there is evidence that AI advances will support
the understanding of climate change and the modeling of its
possible impacts. Furthermore, AI will support low-carbon
energy systems with high integration of renewable energy and
energy efficiency, which are all needed to address climate
change13,36,37. AI can also be used to help improve the health of
ecosystems. The achievement of target 14.1, calling to prevent and
significantly reduce marine pollution of all kinds, can benefit
from AI through algorithms for automatic identification of pos-
sible oil spills38. Another example is target 15.3, which calls for
combating desertification and restoring degraded land and soil.
According to Mohamadi et al.39, neural networks and objective-
oriented techniques can be used to improve the classification of
vegetation cover types based on satellite images, with the possi-
bility of processing large amounts of images in a relatively short
time. These AI techniques can help to identify desertification
trends over large areas, information that is relevant for environ-
mental planning, decision-making, and management to avoid
further desertification, or help reverse trends by identifying the
major drivers. However, as pointed out above, efforts to achieve
SDG 13 on climate action could be undermined by the high-
energy needs for AI applications, especially if non carbon-neutral
energy sources are used. Furthermore, despite the many examples
of how AI is increasingly applied to improve biodiversity mon-
itoring and conservation40, it can be conjectured that an increased
access to AI-related information of ecosystems may drive over-
exploitation of resources, although such misuse has so far not
Enabler
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Fig. 3 Detailed assessment of the impact of AI on the SDGs within the Economy group. Documented evidence of positive or negative impact of AI on the
achievement of each of the targets from SDGs 8, 9, 10, 12, and 17 (https://www.un.org/sustainabledevelopment/). The interpretation of the blocks and
colors is as in Fig. 2. (The content of of this figure has not been reviewed by the United Nations and does not reflect its views).
Environment
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Fig. 4 Detailed assessment of the impact of AI on the SDGs within the Environment group. Documented evidence of positive or negative impact of AI on
the achievement of each of the targets from SDGs 13, 14, and 15 (https://www.un.org/sustainabledevelopment/). The interpretation of the blocks and
colors is as in Fig. 2. (The content of of this figure has not been reviewed by the United Nations and does not reflect its views).
PERSPECTIVE NATURE COMMUNICATIONS | https://doi.org/10.1038/s41467-019-14108-y
4NATURE COMMUNICATIONS | (2020) 11:233 | https://doi.org/10.1038/s41467-019-14108-y | www.nature.com/naturecommunications
been sufficiently documented. This aspect is further dis-
cussed below, where currently identified gaps in AI research are
considered.
An assessment of the collected evidence on the interlinkages.A
deeper analysis of the gathered evidence was undertaken as
shown in Fig. 1(and explained in the Methods section). In
practice, each interlinkage was weighted based on the applic-
ability and appropriateness of each of the references to assess a
specific interlinkage—and possibly identify research gaps.
Although accounting for the type of evidence has a relatively
small effect on the positive impacts (we see a reduction of posi-
tively affected targets from 79% to 71%), we observe a more
significant reduction (from 35% to 23%) in the targets with
negative impact of AI. This can be partly due the fact that AI
research typically involves quantitative methods that would bias
the results towards the positive effects. However, there are some
differences across the Society, Economy and Environment
spheres. In the Society sphere, when weighting the appropriate-
ness of evidence, positively affected targets diminish by 5 per-
centage points (p.p.) and negatively affected targets by 13 p.p. In
particular, weighting the appropriateness of evidence on negative
impacts on SDG 1 (on no poverty) and SDG 6 (on clean water
and sanitation) reduces the fraction of affected targets by 43 p.p.
and 35 p.p., respectively. In the Economy group instead, positive
impacts are reduced more (15 p.p.) than negative ones (10 p.p.)
when taking into account the appropriateness of the found evi-
dence to speak of the issues. This can be related to the extensive
study in literature assessing the displacement of jobs due to AI
(because of clear policy and societal concerns), but overall the
longer-term benefits of AI on the economy are perhaps not so
extensively characterized by currently available methods. Finally,
although the weighting of evidence decreases the positive impacts
of AI on the Environment group only by 8 p.p., the negative
impacts see the largest average reduction (18 p.p.). This is
explained by the fact that, although there are some indications of
the potential negative impact of AI on this SDG, there is no
strong evidence (in any of the targets) supporting this claim, and
therefore this is a relevant area for future research.
In general, the fact that the evidence on interlinkages between
AI and the large majority of targets is not based on tailored
analyses and tools to refer to that particular issue provides a
strong rationale to address a number of research gaps, which are
identified and listed in the section below.
Research gaps on the role of AI in sustainable development
The more we enable SDGs by deploying AI applications, from
autonomous vehicles41 to AI-powered healthcare solutions42 and
smart electrical grids13, the more important it becomes to invest
in the AI safety research needed to keep these systems robust and
beneficial, so as to prevent them from malfunctioning, or from
getting hacked43. A crucial research venue for a safe integration of
AI is understanding catastrophes, which can be enabled by a
systemic fault in AI technology. For instance, a recent World
Economic Forum (WEF) report raises such a concern due to the
integration of AI in the financial sector44. It is therefore very
important to raise awareness on the risks associated to possible
failures of AI systems in a society progressively more dependent
on this technology. Furthermore, although we were able to find
numerous studies suggesting that AI can potentially serve as an
enabler for many SDG targets and indicators, a significant frac-
tion of these studies have been conducted in controlled laboratory
environments, based on limited datasets or using prototypes45–47.
Hence, extrapolating this information to evaluate the real-world
effects often remains a challenge. This is particularly true when
measuring the impact of AI across broader scales, both tempo-
rally and spatially. We acknowledge that conducting controlled
experimental trials for evaluating real-world impacts of AI can
result in depicting a snapshot situation, where AI tools are tai-
lored towards that specific environment. However, as society is
constantly changing (also due to factors including non-AI-based
technological advances), the requirements set for AI are changing
as well, resulting in a feedback loop with interactions between
society and AI. Another underemphasized aspect in existing lit-
erature is the resilience of the society towards AI-enabled chan-
ges. Therefore, novel methodologies are required to ensure that
the impact of new technologies are assessed from the points of
view of efficiency, ethics, and sustainability, prior to launching
large-scale AI deployments. In this sense, research aimed at
obtaining insight on the reasons for failure of AI systems,
introducing combined human–machine analysis tools48, are an
essential step towards accountable AI technology, given the large
risk associated to such a failure.
Although we found more published evidence of AI serving as
an enabler than as an inhibitor on the SDGs, there are at least two
important aspects that should be considered. First, self-interest
can be expected to bias the AI research community and industry
towards publishing positive results. Second, discovering detri-
mental aspects of AI may require longer-term studies and, as
mentioned above, there are not many established evaluation
methodologies available to do so. Bias towards publishing positive
results is particularly apparent in the SDGs corresponding to the
Environment group. A good example of this bias is target 14.5 on
conserving coastal and marine areas, where machine-learning
algorithms can provide optimum solutions given a wide range of
parameters regarding the best choice of areas to include in con-
servation networks49. However, even if the solutions are optimal
from a mathematical point of view (given a certain range of
selected parameters), additional research would be needed to
assess the long-term impact of such algorithms on equity and
fairness6, precisely because of the unknown factors that may
come into play. Regarding the second point stated above, it is
likely that the AI projects with the highest potential of max-
imizing profit will get funded. Without control, research on AI is
expected to be directed towards AI applications where funding
and commercial interests are. This may result in increased
inequality50. Consequently, there is the risk that AI-based tech-
nologies with potential to achieve certain SDGs may not be
prioritized, if their expected economic impact is not high. Fur-
thermore, it is essential to promote the development of initiatives
to assess the societal, ethical, legal, and environmental implica-
tions of new AI technologies.
Substantive research and application of AI technologies to
SDGs is concerned with the development of better data-mining
and machine-learning techniques for the prediction of certain
events. This is the case of applications such as forecasting
extreme weather conditions or predicting recidivist offender
behavior. The expectation with this research is to allow the
preparation and response for a wide range of events. However,
there is a research gap in real-world applications of such systems,
e.g., by governments (as discussed above). Institutions have a
number of barriers to the adoption AI systems as part of their
decision-making process, including the need of setting up mea-
sures for cybersecurity and the need to protect the privacy of
citizens and their data. Both aspects have implications on human
rights regarding the issues of surveillance, tracking, commu-
nication, and data storage, as well as automation of processes
without rigorous ethical standards21. Targeting these gaps would
be essential to ensure the usability and practicality of AI tech-
nologies for governments. This would also be a prerequisite for
understanding long-term impacts of AI regarding its potential,
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while regulating its use to reduce the possible bias that can be
inherent to AI6.
Furthermore, our research suggests that AI applications are
currently biased towards SDG issues that are mainly relevant to
those nations where most AI researchers live and work. For
instance, many systems applying AI technologies to agriculture,
e.g., to automate harvesting or optimize its timing, are located
within wealthy nations. Our literature search resulted in only a
handful of examples where AI technologies are applied to SDG-
related issues in nations without strong AI research. Moreover, if
AI technologies are designed and developed for technologically
advanced environments, they have the potential to exacerbate
problems in less wealthy nations (e.g., when it comes to food
production). This finding leads to a substantial concern that
developments in AI technologies could increase inequalities both
between and within countries, in ways which counteract the
overall purpose of the SDGs. We encourage researchers and
funders to focus more on designing and developing AI solutions,
which respond to localized problems in less wealthy nations and
regions. Projects undertaking such work should ensure that
solutions are not simply transferred from technology-intensive
nations. Instead, they should be developed based on a deep
understanding of the respective region or culture to increase the
likelihood of adoption and success.
Towards sustainable AI
The great wealth that AI-powered technology has the potential
to create may go mainly to those already well-off and educated,
while job displacement leaves others worse off. Globally, the
growing economic importance of AI may result in increased
inequalities due to the unevenly distributed educational and
computing resources throughout the world. Furthermore, the
existing biases in the data used to train AI algorithms may
result in the exacerbation of those biases, eventually leading to
increased discrimination. Another related problem is the usage
of AI to produce computational (commercial, political) pro-
paganda based on big data (also defined as “big nudging”),
which is spread through social media by independent AI agents
with the goals of manipulating public opinion and producing
political polarization51. Despite the fact that current scientific
evidence refutes technological determinism of such fake news51,
long-term impacts of AI are possible (although unstudied) due
to a lack of robust research methods. A change of paradigm is
therefore needed to promote cooperation and to limit the
possibilities for control of citizen behavior through AI. The
concept of Finance 4.0 has been proposed52 as a multi-currency
financial system promoting a circular economy, which is
aligned with societal goals and values. Informational self-
determination (in which the individual takes an active role in
how their data are handled by AI systems) would be an essential
aspect of such a paradigm52. The data intensiveness of AI
applications creates another problem: the need for more and
more detailed information to improve AI algorithms, which is
in conflict with the need of more transparent handling and
protection of personal data53. One area where this conflict is
particularly important is healthcare: Panch et al.54 argue that
although the vast amount of personal healthcare data could lead
to the development of very powerful tools for diagnosis and
treatment, the numerous problems associated to data owner-
ship and privacy call for careful policy intervention. This is also
an area where more research is needed to assess the possible
long-term negative consequences. All the challenges mentioned
above culminate in the academic discourse about legal per-
sonality of robots55, which may lead to alarming narratives of
technological totalitarianism.
Many of these aspects result from the interplay between
technological developments on one side and requests from indi-
viduals, response from governments, as well as environmental
resources and dynamics on the other. Figure 5shows a schematic
representation of these dynamics, with emphasis on the role of
technology. Based on the evidence discussed above, these inter-
actions are not currently balanced and the advent of AI has
exacerbated the process. A wide range of new technologies are
being developed very fast, significantly affecting the way indivi-
duals live as well as the impacts on the environment, requiring
new piloting procedures from governments. The problem is that
neither individuals nor governments seem to be able to follow the
pace of these technological developments. This fact is illustrated
by the lack of appropriate legislation to ensure the long-term
viability of these new technologies. We argue that it is essential to
reverse this trend. A first step in this direction is to establish
adequate policy and legislation frameworks, to help direct the vast
potential of AI towards the highest benefit for individuals and the
environment, as well as towards the achievement of the SDGs.
Regulatory oversight should be preceded by regulatory
insight, where policymakers have sufficient understanding of AI
challenges to be able to formulate sound policy. Developing such
insight is even more urgent than oversight, as policy formulated
without understanding is likely to be ineffective at best and
counterproductive at worst.
Although strong and connected institutions (covered by SDG
16) are needed to regulate the future of AI, we find that there is
limited understanding of the potential impact of AI on institu-
tions. Examples of the positive impacts include AI algorithms
aimed at improving fraud detection56,57 or assessing the possible
effects of certain legislation58,59. Another concern is that data-
driven approaches for policing may hinder equal access to justice
because of algorithm bias, particularly towards minorities60.
Consequently, we believe that it is imperative to develop legisla-
tion regarding transparency and accountability of AI, as well as to
decide the ethical standards to which AI-based technology should
be subjected to. This debate is being pushed forward by initiatives
such as the IEEE (Institute of Electrical and Electronics Engi-
neers) ethical aligned design60 and the new EU (European
Union) ethical guidelines for trustworthy AI61. It is noteworthy
that despite the importance of an ethical, responsible, and
trustworthy approach to AI development and use, in a sense, this
issue is independent of the aims of the article. In other words, one
can envision AI applications that improve SDG outcomes while
not being fully aligned with AI ethics guidelines. We therefore
recommend that AI applications that target SDGs are open and
explicit about guiding ethical principles, also by indicating
explicitly how they align with the existing guidelines. On the
other hand, the lack of interpretability of AI, which is currently
one of the challenges of AI research, adds an additional com-
plication to the enforcement of such regulatory actions62. Note
that this implies that AI algorithms (which are trained with data
consisting of previous regulations and decisions) may act as a
“mirror”reflecting biases and unfair policy. This presents an
opportunity to possibly identify and correct certain errors in the
existing procedures. The friction between the uptake of data-
driven AI applications and the need of protecting the privacy and
security of the individuals is stark. When not properly regulated,
the vast amount of data produced by citizens might potentially be
used to influence consumer opinion towards a certain product or
political cause51.
AI applications that have positive societal welfare implica-
tions may not always benefit each individual separately41.This
inherent dilemma of collective vs. individual benefit is relevant
in the scope of AI applications but is not one that should be
solved by the application of AI itself. This has always been an
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issue affecting humankind and it cannot be solved in a simple
way, since such a solution requires participation of all involved
stakeholders. The dynamicity of context and the level of
abstraction at which human values are described imply that
there is not a single ethical theory that holds all the time in all
situations63. Consequently, a single set of utilitarian ethical
principles with AI would not be recommendable due to the high
complexity of our societies52. It is also essential to be aware of
the potential complexity in the interaction between human
and AI agents, and of the increasing need for ethics-driven
legislation and certification mechanisms for AI systems. This is
true for all AI applications, but especially those that, if they
became uncontrolled, could have even catastrophic effects on
humanity, such as autonomous weapons. Regarding the latter,
associations of AI and robotics experts are already getting
together to call for legislation and limitations of their use64.
Furthermore, associations such as the Future of Life Institute
are reviewing and collecting policy actions and shared princi-
ples around the world to monitor progress towards sustainable-
development-friendly AI65. To deal with the ethical dilemmas
raised above, it is important that all applications provide
openness about the choices and decisions made during design,
development, and use, including information about the pro-
venance and governance of the data used for training algo-
rithms, and about whether and how they align with existing AI
guidelines. It is therefore important to adopt decentralized AI
approaches for a more equitable development of AI66.
We are at a critical turning point for the future of AI. A global
and science-driven debate to develop shared principles and leg-
islation among nations and cultures is necessary to shape a future
in which AI positively contributes to the achievement of all the
SDGs. The current choices to develop a sustainable-development-
Technology
Planetary boundaries
Individuals Government
Political requests
Policy and legislation
+/– impacts
Resources
Environment
Resources, well-being
Resources
Needs, +/– impacts, behaviors
New needs
Technology advancements
New technology for legislation
Legislation and standards
Needs, +/– impacts, policy
Fig. 5 Interaction of AI and society. Schematic representation showing the identified agents and their roles towards the development of AI. Thicker arrows
indicate faster change. In this representation, technology affects individuals through technical developments, which change the way people work and
interact with each other and with the environment, whereas individuals would interact with technology through new needs to be satisfied. Technology
(including technology itself and its developers) affects governments through new developments that need appropriate piloting and testing. Also,
technology developers affect government through lobbying and influencing decision makers. Governments provide legislation and standards to technology.
The governments affect individuals through policy and legislation, and individuals would require new legislation consistent with the changing circumstances
from the governments. The environment interacts with technology by providing the resources needed for technological development and is affected by the
environmental impact of technology. Furthermore, the environment is affected either negatively or positively by the needs, impacts, and choices of
individuals and governments, which in turn require environmental resources. Finally, the environment is also an underlying layer that provides the
“planetary boundaries”to the mentioned interactions.
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friendly AI by 2030 have the potential to unlock benefits that
could go far-beyond the SDGs within our century. All actors in all
nations should be represented in this dialogue, to ensure that no
one is left behind. On the other hand, postponing or not having
such a conversation could result in an unequal and unsustainable
AI-fueled future.
Methods
In this section we describe the process employed to obtain the results described in
the present study and shown in the Supplementary Data 1. The goal was to answer
the question “Is there published evidence of AI acting as an enabler or an inhibitor
for this particular target?”for each of the 169 targets within the 17 SDGs. To this
end, we conducted a consensus-based expert elicitation process, informed by
previous studies on mapping SDGs interlinkages8,9and following Butler et al.67
and Morgan68. The authors of this study are academics spanning a wide range of
disciplines, including engineering, natural and social sciences, and acted as experts
for the elicitation process. The authors performed an expert-driven literature
search to support the identified connections between AI and the various targets,
where the following sources of information were considered as acceptable evidence:
published work on real-world applications (given the quality variation depending
on the venue, we ensured that the publications considered in the analysis were of
sufficient quality); published evidence on controlled/laboratory scenarios (given the
quality variation depending on the venue, we ensured that the publications con-
sidered in the analysis were of sufficient quality); reports from accredited organi-
zations (for instance: UN or government bodies); and documented commercial-
stage applications. On the other hand, the following sources of information were
not considered as acceptable evidence: educated conjectures, real-world applica-
tions without peer-reviewed research; media, public beliefs or other sources of
information.
The expert elicitation process was conducted as follows: each of the SDGs was
assigned to one or more main contributors, and in some cases to several additional
contributors as summarized in the Supplementary Data 1 (here the initials cor-
respond to the author names). The main contributors carried out a first literature
search for that SDG and then the additional contributors completed the main
analysis. One published study on a synergy or a trade-off between a target and AI
was considered enough for mapping the interlinkage. However, for nearly all
targets several references are provided. After the analysis of a certain SDG was
concluded by the contributors, a reviewer was assigned to evaluate the connections
and reasoning presented by the contributors. The reviewer was not part of the first
analysis and we tried to assign the roles of the main contributor and reviewer to
experts with complementary competences for each of the SDGs. The role of the
reviewer was to bring up additional points of view and considerations, while cri-
tically assessing the analysis. Then, the main contributors and reviewers iteratively
discussed to improve the results presented for each of the SDGs until the analysis
for all the SDGs was sufficiently refined.
After reaching consensus regarding the assessment shown in the Supplementary
Data 1, we analyzed the results by evaluating the number of targets for which AI
may act as an enabler or an inhibitor, and calculated the percentage of targets with
positive and negative impact of AI for each of the 17 goals, as shown in Fig. 1.In
addition, we divided the SDGs into the three following categories: Society, Econ-
omy, and Environment, consistent with the classification discussed by Refs. 11,12.
The SDGs assigned to each of the categories are shown in Fig. 6and the individual
results from each of these groups can be observed in Figs. 2–4. These figures
indicate, for each target within each SDG, whether any published evidence of
positive or negative impact was found.
Taking into account the types of evidence. In the methodology described above,
a connection between AI and a certain target is established if at least one reference
documenting such a link was found. As the analyzed studies rely on very different
types of evidence, it is important to classify the references based on the methods
employed to support their conclusions. Therefore, all the references in the Sup-
plementary Data 1 include a classification from (A) to (D) according to the fol-
lowing criteria:
●References using sophisticated tools and data to refer to this particular issue
and with the possibility to be generalized are of type (A).
●Studies based on data to refer to this particular issue, but with limited
generalizability, are of type (B).
●Anecdotal qualitative studies and methods are of type (C) .
●Purely theoretical or speculative references are of type (D).
The various classes were assigned following the same expert elicitation process
described above. Then, the contribution of these references towards the linkages is
weighted and categories (A), (B), (C), and (D) are assigned relative weights of 1,
0.75, 0.5, and 0.25, respectively. It is noteworthy that, given the vast range of studies
on all the SDG areas, the literature search was not exhaustive and, therefore, certain
targets are related to more references than others in our study. To avoid any bias
associated to the different amounts of references in the various targets, we
considered the largest positive and negative weight to establish the connection with
each target. Let us consider the following example: for a certain target, one
reference of type (B) documents a positive connection and two references of types
(A) and (D) document a negative connection with AI. In this case, the potential
positive impact of AI on that target will be assessed with 0.75, while the potential
negative impact is 1.
Limitations of the research. The presented analysis represents the perspective of
the authors. Some literature on how AI might affect certain SDGs could have been
missed by the authors or there might not be published evidence yet on such
interlinkage. Nevertheless, the employed methods tried to minimize the subjectivity
of the assessment. How AI might affect the delivery of each SDG was assessed and
reviewed by several authors and a number of studies were reviewed for each
interlinkage. Furthermore, as discussed in the Methods section, each interlinkage
was discussed among a subset of authors until consensus was reached on its nature.
Finally, this study relies on the analysis of the SDGs. The SDGs provide a
powerful lens for looking at internationally agreed goals on sustainable
development and present a leap forward compared with the Millenium
Development Goals in the representation of all spheres of sustainable development,
encompassing human rights69, social sustainability, environmental outcomes, and
economic development. However, the SDGs are a political compromise and might
be limited in the representation of some of the complex dynamics and cross-
interactions among targets. Therefore, the SDGs have to be considered in
conjunction with previous and current, and other international agreements9. For
instance, as pointed out in a recent work by UN Human Rights69, human rights
considerations are highly embedded in the SDGs. Nevertheless, the SDGs should be
considered as a complement, rather than a replacement, of the United Nations
Universal Human Rights Charter70.
Data availability
The authors declare that all the data supporting the findings of this study are available
within the paper and its Supplementary Data 1 file.
Received: 3 May 2019; Accepted: 16 December 2019;
Society
Economy
Environment
Fig. 6 Categorization of the SDGs (https://www.un.org/sustainabledevelopment/) into the Society, Economy, and Environment groups. (The content
of this figure has not been reviewed by the United Nations and does not reflect its views).
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Acknowledgements
R.V. acknowledges funding provided by KTH Sustainability Office. I.L. acknowledges the
Swedish Research Council (registration number 2017-05189) and funding through an
Early Career Research Fellowship granted by the Jacobs Foundation. M.B. acknowledges
Implicit SSF: Swedish Foundation for Strategic Research project RIT15-0046. V.D.
acknowledges the support of the Wallenberg AI, Autonomous Systems, and Software
Program (WASP) program funded by the Knut and Alice Wallenberg Foundation. S.D.
acknowledges funding from the Leibniz Competition (J45/2018). S.L. acknowledges
funding from the European Union’s Horizon 2020 Research and Innovation Programme
under the Marie Skłodowska–Curie grant agreement number 748625. M.T. was sup-
ported by the Ethics and Governance of AI Fund. F.F.N. acknowledges funding from the
Formas grant number 2018-01253.
Author contributions
R.V. and F.F.N. ideated, designed, and wrote the paper; they also coordinated inputs
from the other authors, and assessed and reviewed SDG evaluations as for the Supple-
mentary Data 1. H.A. and I.L. supported the design, wrote, and reviewed sections of the
paper; they also assessed and reviewed SDG evaluations as for the Supplementary Data 1.
M.B., V.D., S.D., A.F. and S.L. wrote and reviewed sections of the paper; they also
assessed and reviewed SDG evaluations as for the Supplementary Data 1. M.T. reviewed
the paper and acted as final editor.
Competing interests
The authors declare no competing interests.
Additional information
Supplementary information is available for this paper at https://doi.org/10.1038/s41467-
019-14108-y.
Correspondence and requests for materials should be addressed to R.V. or F.F.N.
Peer review information Nature Communications thanks Dirk Helbing and the other,
anonymous, reviewer(s) for their contribution to the peer review of this work.
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