# On the Morality of Artificial Intelligence

Alexandra Luccioni and Yoshua Bengio
Université de Montréal, Mila Also CIFAR Senior Fellow

(December 2019)

###### Abstract

Much of the existing research on the social and ethical impact of Artificial
Intelligence has been focused on defining ethical principles and guidelines
surrounding Machine Learning (ML) and other Artificial Intelligence (AI)
algorithms (Jobin et al., 2019; IEEE, 2017). While this is extremely useful
for helping define the appropriate social norms of AI, we believe that it is
equally important to discuss both the potential and risks of ML and to inspire
the community to use ML for beneficial objectives. In the present article,
which is specifically aimed at ML practitioners, we thus focus more on the
latter, carrying out an overview of existing high-level ethical frameworks and
guidelines, but above all proposing both conceptual and practical principles
and guidelines for ML research and deployment, insisting on concrete actions
that can be taken by practitioners to pursue a more ethical and moral practice
of ML aimed at using AI for social good.

##  1 Artificial Intelligence Leaves the Research Lab

Progress in ML in the last decade has been extraordinary and has rekindled the
notion that AI systems could eventually reach human levels of performance,
which was abandoned for several decades. Even if we are still currently far
from this achievement, technological progress in ML has passed a threshold
which enables it to have a huge economic impact, estimated to be close to 16
trillion US dollars by 2030 (Szczepański, 2019). This contrasts with the first
few decades of ML progress, when researchers had the luxury of focusing purely
on the fundamental aspects of their work, not worrying too much about its
potential societal impacts – an object recognition algorithm could be tested
on a common dataset like MNIST (LeCun et al., 1998) or ImageNet (Deng et al.,
2009), and an objective performance metric would be obtained in order to
measure progress, without having to think about the messiness and complexity
of deployment and social impact. Something crucial has changed in recent
years, as algorithms initially developed in the lab are increasingly being
improved and deployed in society, in real-world applications such as
healthcare, transportation and industrial production with real-life
consequences, and we are likely seeing just the tip of the iceberg in terms of
social impact. Along the way, this deployment in society has forced the
realization that these algorithms have social impacts which could be positive
or negative. For example, we have realized that biases hidden in data and
algorithms could lead to more discrimination, in the simplest case simply
because of the data imbalance: facial recognition algorithms have been found
to underperform on gender and racial minorities (Buolamwini and Gebru, 2018).
Furthermore, above and beyond hidden biases, given the high impact potential
of ML research, the question stands of whether practitioners are acting with
the best interests of humanity and society in mind when developing their tools
and applications.

As ML researchers and engineers, we believe that we have a shared
responsibility to consider both ethics and moral values when we choose what we
work on, for what organization, and whether the products we contribute to
directly or indirectly will be beneficial to humanity or more likely to end up
hurting more than helping. Unfortunately, very few of us have been trained to
think about these questions. Instead, most of us have focused from a very
young age on mathematics and computer science and not so much on philosophy
and other humanities. A good step towards learning about these issues is to
consult the documents proposing ethical guidelines for AI, which we will cover
in Section 2. Furthermore, in order to offer a guiding direction for such
debates and soul-searching within the scope of ML, we propose the following
self-directed questions:

  1. 1.

How is the technology that I am working on going to be used?

  2. 2.

Who will benefit or suffer from it?

  3. 3.

How much and what social impact will it have?

  4. 4.

How does my job fit with my values?

We are conscious that the questions listed above are subjective and the
answers will depend highly on the values and ethics of the individual
answering them. Nonetheless, we hope that work on some applications, such as
the design and deployment of lethal autonomous weapons and automatic
surveillance, will clearly be seen to contradict fundamental rights and
dignity, as defined in, among other places, the UN Declaration of Human Rights
(1948). Other applications of ML, such as those increasing the efficiency of
advertising or beating the stock market, are less clear-cut in their moral
value, and merit informed debate and discussion within the scientific
community and society at large. As some of us become more conscious of the
potential or definite social impact of ML, we have the opportunity, if not the
duty, to make our voices heard. A good example of this is a recent letter
signed by numerous scientists calling for an international treaty banning
lethal autonomous weapon systems, e.g., killer drones which can decide to
shoot at a person without human involvement, which would make it possible to
take the broad social, moral and psychological context into account and
potentially decide to abort the mission (for instance, when the target is in a
school or at a family dinner surrounded by women and children).

Finally, while the legal frameworks to oversee and limit research and
development violating these principles are often and unfortunately updated in
a reactive rather than a proactive manner, we believe that we should not wait
until all of the dots between ML and ethics are formally connected by
legislation and regulation. We believe that we have a responsibility to
educate ourselves, to think ahead about potential consequences, to use our
internal moral compasses and to consciously choose the direction of the
research or engineering that we practice. This is important because we believe
that we are faced with a _wisdom race_ : as technology becomes more powerful,
its impact can be proportionally greater, either positively or negatively.

To curb the negative impact, we need to become wiser individually (as
reflected in our personal decisions) and collectively (through social norms,
laws and regulations). Unfortunately, technological progress in AI has
accelerated faster than the current rate of progress of personal and social
wisdom, ultimately making it possible for unwise humans or organizations, even
those with good intentions and acting legally, to have large-scale, major
destructive effects. This is comparable to a world in which nuclear bombs
(i.e. very powerful technology) were accessible and usable by children (i.e.,
persons with insufficient maturity and wisdom), which could easily result in
global nuclear war. This highlights the importance of the discussions still to
be had by large numbers of ML practitioners about ethics and social impact, as
well as the safeguards that need to be put in place to protect especially the
most vulnerable members of our society. We will discuss some of the most
advanced efforts to introduce these safeguards in the next section, followed
by some examples of socially beneficial applications of ML.

##  2 Ethics and AI - Existing Initiatives

In recent years, there have been numerous initiatives which have taken one of
two major approaches to fostering the ethical practice of AI: (1) Proposing
principles guiding the socially responsible development of AI or (2) Raising
concerns about the social impact of AI. We will describe both approaches in
the current section, as well as giving examples of notable initiatives and
projects which have adopted either of the approaches.111For a more complete
overview of different global ML ethics initiatives, see a recent review in
Jobin et al. (2019)

###  2.1 Defining Principles for Practicing AI Responsibly

The topic of ethical research and practice in technology has been gaining
momentum in different corners of the computing community in recent years, and
the various initiatives that have been proposed are indicative of the interest
and the concern that many members share. For instance, in the United States,
the Association for Computing Machinery (ACM) has proposed a Code of Ethics
and Professional Conduct, to be followed by all members of the association and
to guide them in their usage of computer science (Gotterbarn et al., 2018). A
similar initiative was undertaken by the Royal Statistical Society (RSS) in
the United Kingdom, which has created a practical guide for practitioners
regarding the ethical use of mathematics (RSS, 2019). In the present section,
we will address the two most relevant and extensive initiatives to establish
ethical guidelines for AI research and practice: the Montreal Declaration for
Responsible Development of AI and the IEEE report for Ethically Aligned
Design.

####  2.1.1 The Montreal Declaration for a Responsible Development of
Artificial Intelligence

One of the most notable approaches to establishing guidelines for AI
deployment is the Montreal Declaration for a Responsible Development of
Artificial Intelligence, developed in 2017 and revised in 2018 based on public
feedback222 https://www.montrealdeclaration-responsibleai.com/. It was
elaborated under the premise that given the assumption that since AI will
eventually affect all sectors of society, it requires principles to guide its
development to ensure its adherence to human values and social progress. The
resulting Declaration has ten principles, ranging from protection of privacy
to equal representation, with some principles touching responsibility and
ethics directly; for instance, the principle of Prudence stipulates that
_“Every person involved in AI development must exercise caution by
anticipating, as far as possible, the adverse consequences of AIS [Artificial
Intelligent Systems] use and by taking the appropriate measures to avoid
them.”_ These principles were defined after extensive debates and dialogue
between both specialists and non-specialists from different domains and parts
of the world to ensure representability and cohesion. The overall aim of the
declaration was to spark public debate and to encourage a progressive and
inclusive orientation to the development of AI.

However, the Montreal declaration goes further than theoretical ethical
principles, proposing recommendations to accomplish an ethical digital
transition that includes all of the different levels of society, from
researchers to policy-makers. For instance, it includes a proposition for
auditing and validating the use of AIS using concrete frameworks and
certifications in order to prevent biases and discrimination. Specific steps
were also proposed for ensuring the protection of democracy and reducing the
environmental footprint of AI, all within the framework of a democratic and
citizen-led process. This is important given that the effects of AI will
permeate all levels of society, from the programmers and engineers who write
the code, to the leaders who will legislate it, and the businesses who will
make products with it that will be used by all. The process of creation of the
Montreal declaration was consequently the keystone to building a way of
including all of these different stakeholders in the elaboration of an ethical
AI, and paves the way for subsequent work on the topic.

####  2.1.2 IEEE Ethically Aligned Design

A more recent effort, initiated by the IEEE Global Initiative on Ethics of
Autonomous and Intelligent Systems, carried out an in-depth study on the issue
of the ethics surrounding the design of AI systems (IEEE, 2017). In
particular, aspects that are relevant to the topics covered in the present
paper include: the usage of A/IS [autonomous and intelligent systems] in
service to sustainable development for all, and more specifically for the
attainment of the United Nations Sustainable Development Goals (SDGs) (UNHCR,
2017). The authors of the study specifically underline the potential of AI to
contribute to resolving some of the world’s most urgent problems, such as
climate change and poverty, given the necessary will and orientation towards
these problems. Furthermore, they highlight the fact that despite their great
potential, current AI deployment and development is currently not aligned with
these goals and impacts (IEEE, 2017, p. 144), which is unsettling given the
myriad of ML project and initiatives worldwide.

The IEEE report also lays down principles to guide _“the ethical and values-
based design, development, and implementation of autonomous and intelligent
systems”_ , many of which are similar to those defined by the Montreal
Declaration: respect of human rights, data agency, transparency,
accountability, etc. They go further in proposing that _“A/IS creators shall
adopt increased human well-being as a primary success criterion for
development”_ instead of focusing on isolated metrics such as accuracy, and,
from a deployment perspective, offering alternative metrics to quantify
meaningful progress, for instance by evaluating social, economic and
environmental factors instead of profit and other common success metrics. The
report also includes propositions for policymakers, legislators and other
stakeholders from the extended AI community and, as such, represents the most
extensive effort of establishing ethical boundaries and guidelines for AI
research to date.

In a recent survey of the various global ethics guidelines proposed around AI,
the authors observed that despite a conceptual overlap between the many
existing guidelines, including the two mentioned above, there are major
differences regarding how the principles are interpreted (Jobin et al., 2019).
This underlines the complexity and nuance of applying theoretical,
philosophical principles in practice, and raises questions such as: what
aspects of the AI research and deployment pipeline do ethics principles
affect? how would it be possible to resolve conflicts between, for instance,
fairness and sustainability (i.e. training an algorithm longer and with more
data - thus potentially leading to more greenhouse gas emissions - to ensure
that it is not discriminatory and covers all demographic groups equally well)?
And, above all, how is it possible to translate ethical principles into a
programming language? In any case, the bridge between theory and practice has
yet to be built and there are different ways in which that can happen. This
underlines the necessity of involving actors from different levels of the AI
ecosystem (and neighboring ones) in order to ensure that experts in policy-
making work in tandem with experts in coding and engineering to create tools
and frameworks that are coherent and usable by all.

###  2.2 Identifying Ethical Concerns of AI Applications

There are several types of ethical concerns regarding AI applications and, in
this paper, we will focus more concretely on bias leading to potential
discrimination. While it is true that on the one hand, AI-infused technology
such as computer vision can enhance public security, for instance by
identifying crime in real-time based on CCTV cameras, but the trade-off is
that can also be abused to track individuals and to establish a surveillance
state where privacy is greatly threatened by those who control the technology.
On the military side, similar technology can be used to design autonomous
drones which use computer vision to identity their target, representing a
grave threat to global security and democracy due to the lack of human
oversight. In addition to the security risk, such weapons would be moral and
legal hazard: AI technology is not yet capable to comprehend and represent the
social and psychological context in which such a targeted attack could take
place in a manner that is coherent with international laws regarding war as
well as with human morality.

Unfortunately, the most common argument brought in favour of developing lethal
autonomous weapons is that they are needed as a precautionary measure (i.e.
since other countries are undeniably working on them, each country needs to do
the same). In reality, the weapons needed to defend against killer drones
would be very different from the drones themselves, and do not need to be
lethal autonomous weapons since they would be designed to destroy weapons
rather than to target people, similar to the Iron Dome used by Israel. Another
common argument is that an international treaty would be useless since some
countries will refuse to sign it. But we have seen in the past that even when
major powers do not sign a treaty (such as the one on anti-personnel mines,
signed by 133 countries, excluding the US, in 1997), the treaty can still be
used to create a moral stigma, as well as a decline in demand; in the case of
anti-personnel mines, the result has been that U.S. companies have stopped
building them, even though their government never signed the treaty. Another
flawed argument is that regulating lethal autonomous weapons could threaten
the innovation in AI, whereas in fact AI has been developed very successfully
in a civilian setting (mostly in academia and major technology companies) and
its continued development does not require neither data nor engineering which
would come from AI military development.

Another potential threat to democracy stemming from AI could come not simply
from the increased ability to monitor and to target individuals, but also from
the more subtle power to influence them, e.g. via AI-driven advertising,
automated online trolls and other psychological manipulations via the internet
and social media. The recent use of AI to influence political campaigns such
as the 2016 US election or Brexit is just the beginning of what can be done
when machines learn how to “press our buttons” in a personalized way. This is
due to the fact that micro-targeting makes it possible for ads to be truly
bespoke depending on your political views, network of friends and personal
history. While we may not mind being influenced when it comes to choosing a
brand of soft drinks, when the profit or power motives of a corporation or
political organization go against our individual and collective interests, it
becomes important to establish social norms, laws and regulations to protect
us from such psychological manipulation. But where should the line be drawn
between, for example, manipulation and education? These are difficult
questions but there are clues which can be used (like whether the organization
that stands to profit is paying for the advertisement or social network
influence), so human judgement remains key for judging the ethical aspect,
e.g. in balancing different values (like autonomy vs well-being, when
considering an ad campaign against cigarettes, for example). In the case of
advertising, what is interesting is that in addition to the moral hazard
associated with psychological manipulation, it is not even clear that
advertising is beneficial to society from a purely economic perspective, as it
tends to favour established brands and thus slow down innovation.

Closely related to the political misuse and manipulation with AI is also
increasing concern about AI-generated false images, videos and news. Thanks to
rapid progress in generative neural networks such as the GANs (Goodfellow et
al., 2014), it is becoming possible to synthesize images and sounds in a
controlled way, e.g., using “deep fakes” for making a video of a president
declaring war, or with the face of a celebrity seamlessly integrated on the
body and behavior of a pornography actor. Other commonly discussed concerns of
AI deployment include the effect on the job market (Perisic, 2018), which
means that governments and communities must prepare, e.g. by adapting the
education system and the social safety net, which can take decades, as well as
the potential concentration of power which it may lead to in specific
individuals, corporations and countries, and the bias and discrimination it
may contribute to increase, as we discuss next.

####  2.2.1 Identifying and Mitigating Bias

In recent years, we have been confronted numerous times with the fact that
biased algorithmic systems can perpetuate injustice and discrimination,
whether we are aware of it or not. There are many different ways that this
kind of bias can creep into algorithms: it can be from the data itself, or the
implicit bias that the creator programmed into the system, and even the way
the problem is framed333For a more hands-on presentation of bias and fairness
in AI, we suggest Google’s Online course designed specifically for ML
practitioners. Therefore, in order to ensure that the models that we develop
and the systems that they are later used in are as fair and ethical as
possible, there are steps to take to identify bias and to reduce it as much as
possible.

##### Numerical Bias

A major challenge in designing ML systems is understanding how they work
during training and deployment, and what factors and features they use to make
decisions. However, diagnosing the presence of bias in these systems is not a
straightforward task, since it is not always obvious during a model’s
construction what the downstream impacts of design choices may be; therefore,
upstream efforts are needed to reduce this risk as much as possible. To this
end, there have been several proposals to help practitioners identify and
mitigate bias in ML models, some of which we will describe in the current
section.

More concretely, exploring, analyzing and visualizing the data used for
training a model is a key part of the ML process. But it is not
straightforward to identify bias simply by looking at the data; often, more
in-depth probing is needed to figure out what features and implicit
information is present and, once a model is developed, how this will influence
the model’s behavior. For instance, it was recently found that the COMPAS
system, a criminal risk assessment tool developed widely used in the United
States, is often biased with respect to race (Angwin et al., 2016). Whereas
the bias in the COMPAS system was identified after its deployment, once the
data was made public, this bias is an aspect of the model that should have
been identified much earlier, during development and certainly before
deployment. Similarly, off-the-shelf facial recognition technology used by
police forces has been shown to perform much worse on racial and gender
minorities, with a difference of up to 34.4% in error rate between lighter-
skinned males and darker-skinned females, mostly due to the lack of reliable
training data (Buolamwini and Gebru, 2018).

To address these types of issues, several approaches exist: for instance,
researchers have recently released a tool called ‘What-If’, an open-source
application that lets practitioners not only visualize their data, but also
test the performance of their ML model in hypothetical situations, for
instance modifying some characteristics of data points and analyzing
subsequent model behavior, by measuring fairness metrics such as Equal
Opportunity and Demographic Parity (Wexler et al., 2019). Other approaches
address bias by changing the training procedure or the structure of ML models
themselves, for instance by transforming the raw data in a space in which
discriminatory information cannot be found (Zemel et al., 2013) or using a
variational autoencoder to learn the latent structure from the dataset and
using this structure to re-weight the importance of specific data points
during model training (Ribeiro et al., 2016). Whatever the approach chosen,
using these kinds of tools during ML model development and deployment can
change the life of individual people, who could go from unfairly spending
decades in prison to having the chance of a better life – an immensely
important difference when multiplied by the thousands of people whose lives
can be affected by the deployment of these tools. This multiplication of bias
is especially important to consider since ML is being used more and more, and
therefore even edge cases and small minorities can be amplified in real-world
applications.

##### Textual Bias

Bias is not always in numbers, it can also manifest itself in the words that
we use to describe the world around us. For instance, in 2018, Reuters
reported that Amazon was forced to decommission an ML-powered recruiting
engine when it was discovered that it penalized any mention of female-related
vocabulary, including applicants who attended all-women colleges (Dastin,
2018). This is not surprising given the gender disparity that exists in the
technology sector and since the data used to develop this tool was comprised
of resumes submitted (and accepted) to Amazon over a 10-year period. It is
nonetheless disturbing in terms of algorithmic fairness, especially if
algorithms such as this one make filtering or hiring decisions that can
ultimately affect an entire gender’s lives and careers. This can potentially
create a negative feedback loop, as such a system would reduce the number of
female workers and thus the number of positive role models for girls
interested in technology. A similar type of gender bias was also found in
pretrained word embedding models, which were found to exhibit gender
stereotypes in terms of higher cosine similarity between, for instance,
‘woman’ and ‘homemaker’ or ‘receptionist’ as opposed to ‘woman’ and ‘doctor’
or ‘lawyer’, notably due to these biases existing in the corpus that they were
trained on, which consisted of mainstream news articles (Bolukbasi et al.,
2016).

In order to reduce and eventually remove gender bias in written text,
researchers have proposed approaches such as identifying the gender subspace
of vectors and adjusting the dimensions in a way that either neutralizes or
entirely removes gender bias (Bolukbasi et al., 2016). Others have defined a
formal gender bias taxonomy in order to capture gender bias and to train ML
models to later identify this bias in texts (Hitti et al., 2019). Debiasing
the computational representation of language, notably word embedding models,
is especially important because of the extent of their usage; pretrained
embedding models trained on corpora such as Google News and the Common Crawl
are used in a variety of applications and systems, and can therefore continue
perpetuating gender bias in downstream usages in Natural Language Processing
(NLP) applications such as dialogue systems. This is a challenge given the
complex and sub-symbolic nature of modern NLP, which makes it difficult to
analyze specific features and aspects of data and identify latent connections
and bias between words and concepts. Therefore, more work is needed to explore
and analyse these issues, which constitutes an interesting research direction
in itself, and one that is important to pursue and to integrate into
mainstream ML research.

Despite the research initiatives described above to carve appropriate social
norms about AI, there remains a noticeable gap between the recommendations
they make and ways to ensure that these are respected. Legislation of AI is
still catching up to the progress made in research and practice, and there
have not yet been any country-level laws governing AI research specifically.
However, there have been, on the one hand, more high-level legislative
frameworks such as the European Union (EU) General Data Protection Regulation
(GDPR), which aims to ensure data privacy and protection and, on the other
hand, more local initiatives such as San Francisco’s Facial Recognition
Software Ban. Nonetheless, more complete legal frameworks are needed to
control nefarious use of AI and to ensure that the principles defined in
theory are applied and enforced in practice.

##  3 AI for Good Initiatives

Whereas the profit motive is the main driver behind much of the commercial
deployment of AI today, there are nonetheless many projects going on in
academia, government organizations, civil society and industry labs motivated
by more noble objectives, often called AI for Social Good (AISG) projects. In
addition to the specific projects being undertaken in areas such as
healthcare, education or the environment, it is interesting to highlight
higher-level efforts which aim to foster and facilitate these projects. For
example, the  AI Commons projec aims to construct a hub where different kinds
of actors can connect and collaborate on AISG projects, e.g., ML graduate
students or engineers, problem owners in NGOs or local governments,
philanthropy organizations, or startups which could deploy the ML solutions.
Their interaction is to be facilitated by online tools and datasets as well as
a standardized description of the status, progress and expected impact of each
project. We hope that initiatives like this will help solidify and amplify the
impact of AISG; in the meantime, there are also many profoundly positive uses
of AI that are emerging and we would like to highlight and applaud such
efforts in the present section.

###  3.1 AI in Healthcare

Achieving universal health coverage is one of the 17 UN Sustainable
Development Goals (UNHCR, 2017) and although major progress has been made in
numerous domains, such as maternal health as well as HIV/AIDS reduction, there
are still many problems that are far from being solved. While ML is not a
cure-all, there are many challenges that it can help with such as personalized
medicine, diagnosis of medical imagery, and improved drug discovery (Ghassemi
et al., 2018). ML in the health sector is in fact a thriving domain of
research, with its own workshops at major ML conferences and research
published in major medical journals read by practitioners worldwide. In the
last five years alone, groundbreaking work has been done in improving the
diagnosis of diabetic retinopathy from a single visit (Arcadu et al., 2019),
detecting breast cancer in lymph nodes (Golden, 2017) and large-scale
discovery of diseases based on health records (Pivovarov et al., 2015). There
is also an increasing number of startups and companies working in the space,
either by commercializing research done in academia or by developing products
specifically catered to the medical sector, with the most advanced
applications harnessing the power of deep learning for analyzing and
classifying medical imagery.

Despite the many exciting advances that are being made, there are many hurdles
in ML research in healthcare, starting from data privacy and control (who owns
the data? Can patients share their own data, or should the process be
centralized? How to find the right balance between privacy and the lives which
will be saved by applying ML on the aggregated health records from many
different sources?), to the manner in which medical data should be processed
(Should it contain information such as race and postal/zip code, which can
impact diagnoses, be included in electronic heath records, or does that open
the door to discrimination and bias?) and how should such systems be deployed
(human-in-the-loop or fully automated?)444For a more extensive overview of the
opportunities and challenges of using ML in healthcare, see Ghassemi et al.
(2018). There are also often questions of responsibility and interpretability
that arise, given the high stakes of deploying ML systems in situations of
life and death. In order to make meaningful progress in this sector, it is
therefore important to continue existing research on fair and ethical usage of
ML in healthcare (Wiens et al., 2019) and to ensure that Hippocratic
principles are a solid part of the research and development process, as well
as working with stakeholders of the domain (e.g. radiologists, clinicians,
patient organizations and hospital administrators) to propose solutions to the
hurdles proposed above.

###  3.2 AI for Education

The promise of using adaptive intelligent systems and agents for education has
been around since the 1960s (Suppes and Morningstar, 1969), but access to
personalized digital education tools has yet to become a reality in most
countries, especially in the developing world, where it could have the most
impact to democratize education and knowledge (Nye, 2015). In recent years,
given the increasing global shortage of qualified teachers along with the
increasing number of students, the issue of access to education has become a
global one, a fact highlighted by its presence among the UN SDGs. And yet, the
usage of ML in the education sector has been limited to specific, narrow
applications such as predicting the probability of learner attrition (Chaplot
et al., 2015) or improving learner evaluation (Abbott, 2006). There are many
reasons for this, starting from the difficulty in representing learning
content in a domain-agnostic way to facilitate scalability, to overcoming
cultural and linguistic barriers to deploying tutors worldwide, but also more
fundamental issues such as the lack of large-scale educational datasets and
the inherent technological constraints in developing countries.

Despite these hurdles, there are many new and longstanding efforts to create
intelligent tutors, be it using symbolic AI approaches such as ontologies and
knowledge modeling (Nkambou et al., 2010), educational data mining (Dutt et
al., 2017) or, more recently, ML-driven approaches (Conati et al., 2018).
However, there are very high stakes in the field, since technological
interventions have the potential to make considerable, long-term impact on
human livelihoods, for example lifting people out of poverty by endowing them
with linguistic and numerical literacy, but these can be hindered by bias and
technological constraints. We therefore agree with recent proposals to improve
and support human learning at scale and believe that ML has a key role to play
in this endeavour. This can be done, for instance, by partnering up with
existing education initiatives and organizations in order to learn what their
specific needs are and how ML can be used to meet them, or else by
collaborating with Massive Open Online Course (MOOC) creators in order to
gather data and make it available to the ML community, and finally by sharing
learning materials and activities used in local education initiatives (e.g.
university courses in Machine Learning) so that they profit learners in places
where access to high-quality technical education is limited.

###  3.3 AI for the Environment

Climate change is, without a doubt, one of the biggest challenges that
humanity has faced, and we are at an important point in history when we are
both aware of the issue and still have the possibility to change its course.
Climate change has been described as a ‘wicked’ problem, due to features such
as the difficulty in defining the problem itself and in developing and
deploying solutions to it, the lack of central authority that can solve it,
the incentives for individual countries or companies to not do their share,
and the cognitive biases that discount the future impacts of our actions (Head
et al., 2008; Levin et al., 2012). Furthermore, while we do not know of any
single technological silver bullet as solution to climate change, there are
nonetheless numerous technical challenges for which ML can be helpful, and
which can be combined to make a significant impact on the overall issue. These
challenges and the ongoing ML approaches to tackle them were presented in a
recent survey paper (Rolnick et al., 2019). We will not go into all of these
at length, but we will focus on a few examples that are particularly salient
and that we hope will give an idea of both the relevance of deploying ML in
environmental applications and the opportunities that this can generate.

##### Energy and Transportation

Together, electricity and transportation systems are estimated to produce
close to half of anthropogenic greenhouse gas (GHG) emissions (Allen et al.,
2019) and both sectors have their own unique challenges for decarbonization.
For instance, one of the major obstacles to building and using renewable
energy sources such as solar and wind is the variability of their output,
which is inherently problematic since the power generated by an energy grid
must equal the power used by its consumers at any given moment. Currently,
this means that despite the existence of solar panels and wind turbines, these
must be complemented by controllable but highly polluting energy sources such
as coal and natural gas plants. ML methods that are appropriate for time-
series predictions, such as Recurrent Neural Networks are particularly suited
for these types of tasks (Voyant et al., 2017) and can dramatically lower the
barrier to entry for renewable energy globally. Furthermore, even in cases
where controllable energy sources are used, demand on the energy grid will
still fluctuate based on usage; in this case, ML techniques such as
Reinforcement Learning and Dynamic Scheduling can be used to balance the grid
in real time (Vázquez-Canteli and Nagy, 2019).

In transportation, reducing activity is a key part in reducing GHG emissions;
however, given the highly regional nature of transportation methods (i.e.
high-speed trains are only an option in Europe, whereas many major US cities
have limited public transportation), custom solutions are needed to make a
significant impact. ML can be of particular help in estimating and predicting
vehicle flow to minimize it, for example by helping to optimize the design of
new roads and hubs (Sommer et al., 2017) and monitoring traffic (Kaack et al.,
2019), as well as estimating carbon emissions in real-time (Nocera et al.,
2018). ML can also be used for designing more energy-efficient batteries
(Hoffmann et al., 2019) which will become an increasingly important concern as
more people switch to electric vehicles. In both cases of energy and
transportation, ML can be used to make systems more efficient and to improve
predictions of complex phenomena based on large amounts of data; nonetheless,
it remains only one part of the solution, and as tempting as it is to halt
research projects once a theoretically plausible solution has been found (and
a research paper published), what is key here is working with domain experts
to bring projects towards deployment, where concrete impact can be made.
Transversal connections between disciplines are therefore key, and must be
established and fostered for projects to flourish.

##### Individuals and Societies

While changes in our climate can be abstract, quantified in degrees of warming
or tons of CO2, climate change will also have very concrete impacts on
society, for instance by decreasing crop yield, increasing the frequency of
extreme weather events such as hurricanes and storms, and impacting
biodiversity. There are a myriad of ways in which ML can help face these,
whether it be by analyzing real-time images and recordings of ecosystems to
detect species (Duhart et al., 2018) and deforestation (McDowell et al.,
2015), improving disaster preparation and response by generating real-time
maps from satellite imagery (Voigt et al., 2007) and even setting an optimal
price on carbon to accelerate the transition to a low-carbon energy economy
(Wei et al., 2018). Finally, while we are far from being able to predict the
exact impact that increasing the carbon tax will have on the different levels
of society and industry (i.e. federal and regional governments, local and
international companies, and individuals), this is a worthwhile area of
research and exploration, with potentially huge consequences in helping
political leaders make more informed choices in addressing the climate crisis.
It is therefore useful to continue gathering data and building trust between
members of the political ecosystem and ML practitioners to learn from each
other and to facilitate the deployment of technological solutions in setting
government policies.

On an individual level, there are many reasons why individuals cannot, or will
not, act on climate change, either common misconceptions regarding the fact
that individuals cannot make meaningful impact on a global problem, or
cognitive biases that increase an individual’s psychological distance to
climate change. In the first case, ML-infused tools to estimate the carbon
footprint of individuals and households (Jones and Kammen, 2011) and to model
individual behavior with regards to sustainable lifestyle choices and
technologies (Carr-Cornish et al., 2011) can be very useful if they are
sufficiently accurate and deployed on a large scale. Finally, minimizing
psychological distance to the future effects of climate change is a promising
way to reduce cognitive bias – in this regard, it is possible to use images
generated using Generative Adversarial Networks (GANs) which represent the
impacts of extreme events on locations that have personal value to the viewer
(Schmidt et al., 2019). A crucial part of developing ML tools for individuals
is, once again, working with multidisciplinary experts in psychology,
scientific communication, and user design to ensure that the tools created
reach the largest possible audience and maximize their positive impact.

##  4 Conclusion

Technology in general, and ML more specifically, carries a great potential for
change and disruption. While neither of these is guaranteed to make the world
a better place, this potential can most definitely be used to have a positive
impact on the world. In the present article, we have illustrated some
inspiring projects that aim to make the world a better place and by using the
powerful techniques and approaches that ML has brought forward. We believe
that as ML researchers and practitioners, we have the responsibility to
leverage our (super)powers to contribute to these efforts. This can be done by
connecting with established actors from industry and policy or experts from
other relevant disciplines, by learning from their past experiences, and by
working together to propose innovative solutions to major problems, deployed
in places where they will have a positive impact.

We live in a world with many global and local challenges and issues that are
in constant evolution, and it is easy to be overwhelmed by this flux of
information and focus on a small sandbox in which we feel safe and in control,
in order to develop and study the aspects of ML that interest us most. But it
is naive to believe that our sandbox is an isolated isle that is not connected
to the rest of the world – since even in the case of theoretical work,
communication and cross-pollination are unavoidable – and each of us is also a
citizen who is concerned collective debates, while many of us could worry
about the world in which our descendants will live. We believe that there are
thought processes that should take place in the head of every ML practitioner
regarding the nature of the work they are doing and the potential pitfalls and
impacts of this work in the world around them, some of which we have listed in
the first part of the current paper. And while we do not claim to have all the
answers to all of these tough questions, we hope that we can start a
conversation that will accompany ML research and practice throughout its
infancy towards its tumultuous teenage years in the coming decades, and
eventually towards mature adulthood beyond that.

## References

  * Abbott [2006] Robert G Abbott.  Automated expert modeling for automated student evaluation.  In _International Conference on Intelligent Tutoring Systems_ , pages 1–10. Springer, 2006.
  * Allen et al. [2019] M Allen, P Antwi-Agyei, F Aragon-Durand, M Babiker, P Bertoldi, M Bind, S Brown, M Buckeridge, I Camilloni, A Cartwright, et al.  Technical summary: Global warming of 1.5c. an ipcc special report on the impacts of global warming of 1.5c above pre-industrial levels and related global greenhouse gas emission pathways, in the context of strengthening the global response to the threat of climate change, sustainable development, and efforts to eradicate poverty, 2019.
  * Angwin et al. [2016] Julia Angwin, Jeff Larson, Surya Mattu, and Lauren Kirchner.  Machine bias, propublica.  https://www.propublica.org/article/machine-bias-risk-assessments-in-criminal-sentencing, 2016\.  Accessed: 2019-11-25.
  * Arcadu et al. [2019] Filippo Arcadu, Fethallah Benmansour, Andreas Maunz, Jeff Willis, Zdenka Haskova, and Marco Prunotto.  Deep learning algorithm predicts diabetic retinopathy progression in individual patients.  _NPJ digital medicine_ , 2(1):1–9, 2019.
  * Assembly [1948] UN General Assembly.  Universal declaration of human rights.  _UN General Assembly_ , 302(2), 1948.
  * Bolukbasi et al. [2016] Tolga Bolukbasi, Kai-Wei Chang, James Y Zou, Venkatesh Saligrama, and Adam T Kalai.  Man is to computer programmer as woman is to homemaker? debiasing word embeddings.  In _Advances in neural information processing systems_ , pages 4349–4357, 2016.
  * Buolamwini and Gebru [2018] Joy Buolamwini and Timnit Gebru.  Gender shades: Intersectional accuracy disparities in commercial gender classification.  In _Conference on fairness, accountability and transparency_ , pages 77–91, 2018.
  * Carr-Cornish et al. [2011] Simone Carr-Cornish, Peta Ashworth, John Gardner, and Stephen J Fraser.  Exploring the orientations which characterise the likely public acceptance of low emission energy technologies.  _Climatic change_ , 107(3-4):549–565, 2011.
  * Chaplot et al. [2015] Devendra Singh Chaplot, Eunhee Rhim, and Jihie Kim.  Predicting student attrition in moocs using sentiment analysis and neural networks.  In _AIED Workshops_ , volume 53, pages 54–57, 2015.
  * Conati et al. [2018] Cristina Conati, Kaska Porayska-Pomsta, and Manolis Mavrikis.  Ai in education needs interpretable machine learning: Lessons from open learner modelling.  _arXiv preprint arXiv:1807.00154_ , 2018.
  * Dastin [2018] Jeffrey Dastin.  Amazon scraps secret ai recruiting tool that showed bias against women, reuters business news.  https://www.reuters.com/article/us-amazon-com-jobs-automation-insight/amazon-scraps-secret-ai-recruiting-tool-that-showed-bias-against-women-idUSKCN1MK08G, 2018\.  Accessed: 2019-11-25.
  * Deng et al. [2009] Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei.  Imagenet: A large-scale hierarchical image database.  In _2009 IEEE conference on computer vision and pattern recognition_ , pages 248–255. Ieee, 2009.
  * Duhart et al. [2018] Clement Duhart, Gershon Dublon, Brian Mayton, and Joseph Paradiso.  Deep learning locally trained wildlife sensing in real acoustic wetland environment.  In _International Symposium on Signal Processing and Intelligent Recognition Systems_ , pages 3–14. Springer, 2018.
  * Dutt et al. [2017] Ashish Dutt, Maizatul Akmar Ismail, and Tutut Herawan.  A systematic review on educational data mining.  _IEEE Access_ , 5:15991–16005, 2017.
  * Ghassemi et al. [2018] Marzyeh Ghassemi, Tristan Naumann, Peter Schulam, Andrew L Beam, and Rajesh Ranganath.  Opportunities in machine learning for healthcare.  _arXiv preprint arXiv:1806.00388_ , 2018.
  * Golden [2017] Jeffrey Alan Golden.  Deep learning algorithms for detection of lymph node metastases from breast cancer: helping artificial intelligence be seen.  _Jama_ , 318(22):2184–2186, 2017.
  * Goodfellow et al. [2014] Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio.  Generative adversarial nets.  In _Advances in neural information processing systems_ , pages 2672–2680, 2014.
  * Gotterbarn et al. [2018] Don W Gotterbarn, Amy Bruckman, Catherine Flick, Keith Miller, and Marty J Wolf.  Acm code of ethics: a guide for positive action, 2018.
  * Head et al. [2008] Brian W Head et al.  Wicked problems in public policy.  _Public policy_ , 3(2):101, 2008.
  * Hitti et al. [2019] Yasmeen Hitti, Eunbee Jang, Ines Moreno, and Carolyne Pelletier.  Proposed taxonomy for gender bias in text; a filtering methodology for the gender generalization subtype.  In _Proceedings of the First Workshop on Gender Bias in Natural Language Processing_ , pages 8–17, 2019.
  * Hoffmann et al. [2019] Jordan Hoffmann, Louis Maestrati, Yoshihide Sawada, Jian Tang, Jean Michel Sellier, and Yoshua Bengio.  Data-driven approach to encoding and decoding 3-d crystal structures.  _arXiv preprint arXiv:1909.00949_ , 2019.
  * IEEE [2017] IEEE.  Ieee standard review — ethically aligned design: A vision for prioritizing human wellbeing with artificial intelligence and autonomous systems.  In _2017 IEEE Canada International Humanitarian Technology Conference (IHTC)_ , pages 197–201. IEEE, 2017.
  * Jobin et al. [2019] Anna Jobin, Marcello Ienca, and Effy Vayena.  Artificial intelligence: the global landscape of ethics guidelines.  _arXiv preprint arXiv:1906.11668_ , 2019.
  * Jones and Kammen [2011] Christopher M Jones and Daniel M Kammen.  Quantifying carbon footprint reduction opportunities for us households and communities.  _Environmental science & technology_, 45(9):4088–4095, 2011.
  * Kaack et al. [2019] Lynn H Kaack, George H Chen, and M Granger Morgan.  Truck traffic monitoring with satellite images.  In _Proceedings of the Conference on Computing & Sustainable Societies_, pages 155–164. ACM, 2019.
  * LeCun et al. [1998] Yann LeCun, Léon Bottou, Yoshua Bengio, Patrick Haffner, et al.  Gradient-based learning applied to document recognition.  _Proceedings of the IEEE_ , 86(11):2278–2324, 1998.
  * Levin et al. [2012] Kelly Levin, Benjamin Cashore, Steven Bernstein, and Graeme Auld.  Overcoming the tragedy of super wicked problems: constraining our future selves to ameliorate global climate change.  _Policy sciences_ , 45(2):123–152, 2012.
  * McDowell et al. [2015] Nate G McDowell, Nicholas C Coops, Pieter SA Beck, Jeffrey Q Chambers, Chandana Gangodagamage, Jeffrey A Hicke, Cho-ying Huang, Robert Kennedy, Dan J Krofcheck, Marcy Litvak, et al.  Global satellite monitoring of climate-induced vegetation disturbances.  _Trends in plant science_ , 20(2):114–123, 2015\.
  * Nkambou et al. [2010] Roger Nkambou, Riichiro Mizoguchi, and Jacqueline Bourdeau.  _Advances in intelligent tutoring systems_ , volume 308.  Springer Science & Business Media, 2010.
  * Nocera et al. [2018] Silvio Nocera, Cayetano Ruiz-Alarcón-Quintero, and Federico Cavallaro.  Assessing carbon emissions from road transport through traffic flow estimators.  _Transportation Research Part C: Emerging Technologies_ , 95:125–148, 2018.
  * Nye [2015] Benjamin D Nye.  Intelligent tutoring systems by and for the developing world: A review of trends and approaches for educational technology in a global context.  _International Journal of Artificial Intelligence in Education_ , 25(2):177–203, 2015.
  * Perisic [2018] I Perisic.  How artificial intelligence is shaking up the job market.  https://www.weforum.org/agenda/2018/09/artificial-intelligence-shaking-up-job-market/, 2018\.  Accessed: 2019-11-25.
  * Pivovarov et al. [2015] Rimma Pivovarov, Adler J Perotte, Edouard Grave, John Angiolillo, Chris H Wiggins, and Noémie Elhadad.  Learning probabilistic phenotypes from heterogeneous ehr data.  _Journal of biomedical informatics_ , 58:156–165, 2015\.
  * Ribeiro et al. [2016] Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin.  Why should i trust you?: Explaining the predictions of any classifier.  In _Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining_ , pages 1135–1144. ACM, 2016.
  * Rolnick et al. [2019] David Rolnick, Priya L Donti, Lynn H Kaack, Kelly Kochanski, Alexandre Lacoste, Kris Sankaran, Andrew Slavin Ross, Nikola Milojevic-Dupont, Natasha Jaques, Anna Waldman-Brown, Alexandra Luccioni, et al.  Tackling climate change with machine learning.  _arXiv preprint arXiv:1906.05433_ , 2019.
  * RSS [2019] RSS.  A guide for ethical data science.  https://www.actuaries.org.uk/system/files/field/document/An%20Ethical%20Charter%20for%20Date%20Science%20WEB%20FINAL.PDF., 2019\.  Accessed: 2019-11-25.
  * Schmidt et al. [2019] Victor Schmidt, Alexandra Luccioni, S Karthik Mukkavilli, Narmada Balasooriya, Kris Sankaran, Jennifer Chayes, and Yoshua Bengio.  Visualizing the consequences of climate change using cycle-consistent adversarial networks.  _arXiv preprint arXiv:1905.03709_ , 2019.
  * Sommer et al. [2017] Lars Wilko Sommer, Tobias Schuchert, and Jürgen Beyerer.  Fast deep vehicle detection in aerial images.  In _2017 IEEE Winter Conference on Applications of Computer Vision (WACV)_ , pages 311–319. IEEE, 2017.
  * Suppes and Morningstar [1969] Patrick Suppes and Mona Morningstar.  Computer-assisted instruction.  _Science_ , 166(3903):343–350, 1969.
  * Szczepański [2019] M Szczepański.  Economic impacts of artificial intelligence (ai), european parliamentary research service, pe 637.967.  http://europarl.europa.eu/RegData/etudes/BRIE/2019/637967/EPRS_BRI(2019)637967_EN.pdf, 2019\.  Accessed: 2019-11-25.
  * UNHCR [2017] UNHCR.  The sustainable development goals and addressing statelessness.  https://www.refworld.org/docid/58b6e3364.html, 2017.  Accessed: 2019-11-25.
  * Vázquez-Canteli and Nagy [2019] José R Vázquez-Canteli and Zoltán Nagy.  Reinforcement learning for demand response: A review of algorithms and modeling techniques.  _Applied energy_ , 235:1072–1089, 2019.
  * Voigt et al. [2007] Stefan Voigt, Thomas Kemper, Torsten Riedlinger, Ralph Kiefl, Klaas Scholte, and Harald Mehl.  Satellite image analysis for disaster and crisis-management support.  _IEEE transactions on geoscience and remote sensing_ , 45(6):1520–1528, 2007.
  * Voyant et al. [2017] Cyril Voyant, Gilles Notton, Soteris Kalogirou, Marie-Laure Nivet, Christophe Paoli, Fabrice Motte, and Alexis Fouilloy.  Machine learning methods for solar radiation forecasting: A review.  _Renewable Energy_ , 105:569–582, 2017.
  * Wei et al. [2018] Sun Wei, Zhang Chongchong, and Sun Cuiping.  Carbon pricing prediction based on wavelet transform and k-elm optimized by bat optimization algorithm in china ets: the case of shanghai and hubei carbon markets.  _Carbon Management_ , 9(6):605–617, 2018.
  * Wexler et al. [2019] James Wexler, Mahima Pushkarna, Tolga Bolukbasi, Martin Wattenberg, Fernanda Viégas, and Jimbo Wilson.  The what-if tool: Interactive probing of machine learning models.  _IEEE transactions on visualization and computer graphics_ , 26(1):56–65, 2019.
  * Wiens et al. [2019] Jenna Wiens, Suchi Saria, Mark Sendak, Marzyeh Ghassemi, Vincent X Liu, Finale Doshi-Velez, Kenneth Jung, Katherine Heller, David Kale, Mohammed Saeed, et al.  Do no harm: a roadmap for responsible machine learning for health care.  _Nature medicine_ , 25(9):1337–1340, 2019.
  * Zemel et al. [2013] Rich Zemel, Yu Wu, Kevin Swersky, Toni Pitassi, and Cynthia Dwork.  Learning fair representations.  In _International Conference on Machine Learning_ , pages 325–333, 2013.