IEEE Technology and Society Magazine - June 2021 - 69

a clear example of the use and misuse of fairness in
machine learning research. Consequently, methods
for auditing algorithms used in machine learning
research applied to medicine, transport, banking,
finance, or even as an AI4SG initiative have to be ethically
informed ex ante, durante actione, and ex post
to avoid building definitions of fairness from what
the needs of computer science are instead of true
and contextual fairness.
Transparency
Many ethical guidelines overestimate the principle
of transparency. They consider that many AI
models are very complex and often act as black
boxes, say, it is not very well known how machine
learning models obtain results. The real issue here is
that people are not much more transparent than AI
systems and that does not stop us from trusting people.
Nonetheless, transparency is important insofar
is related to the issue of explainability and when it
comes to AI systems applied for SDGs, it is necessary
to have an explanation of their operation in case
they end up doing something wrong.
Explainability
At the European level, the first regulations banning
the possibility to make certain decisions based
on algorithms are beginning to arrive [11]. Citizens
now have the right to be told how and why an algorithm
has made a certain decision: a right to explanation.
So, in this sense, explainability is the degree
in which a human understands a machine model.
For an ethically correct explanation of an artificial
system, there must be the possibility of generation
of explanations for algorithmic outputs but it is also
necessary to have tools and methodologies for conducting
algorithm audits. Because one of the biggest
problems are that machine learning or deep learning
algorithms sometimes fail, and we do not know why.
Global governance of algorithmic systems
Once you have an AI model ready to be used to
meet the SDGs and is fair, transparent, and explainable,
you must bear in mind that the governance of
AI technologies must be global. The international
competition for leadership in the digital revolution
tends to focus on acquiring new skills, much less on
the governance, control, and regulation of the social
and ethical impact of emerging AI technologies.
When we talk about collective interests or global
June 2021
governance of AI, we see that achieving milestones
is not a zero-sum game, because nothing is lost by
sharing new capabilities, algorithms, human capital,
infrastructures, etc. This is why the global governance
of AI must benefit all human beings, including future
generations. It is, therefore, necessary to ensure that
AI technologies are sustainable and environmentally
friendly. Furthermore, they must take into account
the environment [12], including other living beings,
and their social impact must be carefully considered.
Accountability is another major factor to take into
consideration. Mechanisms should be put in place
to ensure the responsibility and accountability of AI
systems and their results. Auditing, which allows the
evaluation of algorithms, data, and design processes,
plays a key role in this, especially in critical applications.
In addition, access and redress to those affected
by algorithmic decisions must be guaranteed. History
reveals that there are many forms of governance,
but AI and robotics pose a challenge to existing governance
models. It is important to understand that
because regulations exist, the world is safer and more
reliable. Regulations are instruments used to implement
social or policy objectives. In some sense, these
instruments encourage just and fair outcomes and
are implemented to correct externalities and other
failures. With the appropriate AI governance model
based on the HR framework, one can determine how
to use AI and robotics to advance SDGs.
HR as a normative framework for AI
technologies
Despite the large number of guidelines and
principle-based approaches to AI ethics, the only
theoretical and practical framework on which to
base a global governance of AI and, in particular,
to its application for the SDGs is HR. HR should
constrain any design or deployment of AI technologies.
Any use of AI must respect people's individual
and civil rights. Any belief that technology is
neutral must be eliminated. The supposed neutrality
of algorithms leads to hiding the asymmetries
of power between the ruling class and oppressed
minorities. The use of supposedly neutral and
entirely rational technology can be used as an
excuse to maintain unjust social hierarchies. The
way in which AI is used has implications that can
disenfranchise or empower humanity. If the use of
AI is made on the basis of taking into consideration
HR respect, and defense of individual experience
69

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