IEEE Technology and Society Magazine - March 2021 - 40

AI for a Fair, Just, and Equitable World

Taking into account the legislative and
political environment
As stated in [22] experience has shown that successful implementation of AI4Eq projects must take
into account the legislative and political environment and the importance of institutional support.
Loss of privacy is of particular concern in LMICs,
where laws concerning data privacy and security are
often weak. The right to privacy is sometimes viewed
as a luxury that LMICs cannot afford whereas, on
the contrary, it may be of even more importance
there than in HIC since in countries characterized by
conflict, crisis, and weak law enforcement, privacy
risks may become security risks (i.e., threats to life
and liberty).
Taking into account the ethical, psychological,
and philosophical considerations that arise in
A/IS development and deployment
Other issues concerning A/IS are also relevant,
particularly in regard to how they affect vulnerable
populations, among others: privacy, explicability,
transparency and traceability, openness, robustness, safety, affordability, culture-awareness, and
nondiscrimination.

Some relevant AI4Eq tools and paradigms
There is general agreement in A/IS ethics forums
that ethics must be embedded in research and production processes [8]. This requires proposals of
methodological and technical tools at all levels of
research and at all levels of development processes
(analysis, design, implementation, deployment, and
evaluation), focused on guaranteeing the properties
of ethical AI in compliance with laws, regulations,
and policies. Ethical A/IS should address the five
basic ethical principles: 1) beneficence; 2) nonmaleficence; 3) autonomy; 4) justice; and 5) explainability. For the reasons set out in the previous sections,
we consider that these principles are of particular
import and have a special character in the case of
R&D that is focused on LMICs and pockets of poverty
in HICs, that is to say, on protecting and empowering
the most vulnerable and marginalized. It would be
advantageous if the different actors of the multidisciplinary AI4Eq field, and even society as a whole,
be involved in the evaluation of the aforementioned
R&D methodologies and tools.
Promoting designs that facilitate transparency
and auditing, and enabling a participatory approach

will require a combination of tools and techniques.
We note that this participation also serves as an
education in A/IS competencies, combatting A/IS
illiteracy and raising awareness of the ethical risks
involved. The participation of A/IS students in AI4Eq
R&D projects would also contribute to the ethical
and socio-political awareness of future professionals.
Although the area of tool support is still in an
immature state, there are already some that, despite
their limited scope, are effective. For example, in
the area of methodological tools, the chapter on
Ethical Guidance for a Reliable AI from the European Commission's expert group provides a guide
to the implementation of intelligent systems, laying
out requirements for satisfying different ethical principles. Morley et al. [8] compile a set of tools that
support the development of ethical A/IS. Some of
these tools could be particularly useful in the AI4Eq
field. In addition, methodological and technological
tools used in other fields could also be very useful in
AI4Eq. Below we discuss tools of both types.
Development intervention tools
AI4Eq should take advantage of existing frameworks and methodological tools for the conception,
design, implementation, deployment, monitoring, and impact assessment of development interventions-in particular, those used in the ICT4D
field-by adapting them to A/IS projects, and then
integrating them into A/IS tools. Among other things,
such frameworks and tools are important to achieving long-term operation and maintenance.
Techniques that require few energy resources
As has been pointed out in the literature, A/IS sustainability requires more efficient data centers using
renewable energy [21]. Conditions in which energy
resources are scarce, oblige the use of sustainable,
low-cost, " appropriate " energy solutions. Sustainability criteria also suggest returning to the expert
model-based paradigm (i.e., classic ESs) instead of
the big data paradigm. As stated in [21], " the human
brain consumes much less energy than is required
to train AI models with massive amounts of data. "
In many cases, this intensive training is not really
necessary to have models that work with more than
acceptable accuracy. The investigation of models
that integrate both paradigms in an acceptable compromise imposed by sustainability requirements is of
significant interest to the AI4Eq field.

IEEE Technology and Society Magazine

IEEE Technology and Society Magazine - March 2021

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