IEEE Technology and Society Magazine - March 2021 - 51

and comprehensibility (basic but comprehensive
documentation).

Public data infrastructures
The deployment of AI requires the computation
of large volumes of data that is currently done in
data processing centers. Administrations need to
have a public custody system for data processing.
We believe that institutions and social organizations should also have access to data for public use.
Open data implies interoperability and interconnection of diverse data sources. It also requires designing services that optimize the usage and accessibility
of data while ensuring adequate security and privacy. The EC's strategy for data [37] recognizes the
need to build new next-generation data-processing
infrastructures to reduce technological dependence, since currently European cloud service providers represent only a small share of the cloud market.
Finally, the question of data ownership must be
addressed. It should not simply be assumed that citizen-generated data belong to the companies that
capture it; this fact has not been actively accepted
by society (see [38]). This question acquires particular relevance when the data is to be used for a
public purpose. There needs to be a move toward
data-sharing models that facilitate the use of data for
public purposes, especially in the case of humanitarian emergencies.

Other neglected risks
To ensure that AI really contributes to " human
welfare " requires a prudent multidisciplinary and
ecocentric approach (in harmony with an authentic
anthropocentrism that recognizes the essence of the
human being and his or her interests) to AI research,
as opposed to an excessively techno-optimistic and
technocratic approach.
Although the optimistic view of technology predicts a generally positive contribution of AI to the
ability to live a happy and fulfilling life, there are
also important risks in this area for individuals, their
values, and their personal development. There are
important problems associated with the use of this
technology such as the alteration of the concept of
identity and the nature of human interactions, the
difficulty of distinguishing between the real and the
virtual, escapism toward virtual worlds, the replacement and deterioration of human bonds, cognitive
overload, the loss of meaning and purpose due to

March 2021

being replaced by intelligent machines, and so on.
There is a need for a discussion of the potential loss
of human values: wisdom, creativity, empathy, affection, social skills, etc.; also be aware of the possibilities of control, manipulation, attack on autonomy,
and so on that the field of affective computing opens
up, given the susceptibility of humans to emotional
influence. Experimental work with social science
experts should be promoted with a view to assessing
these risks.
The list of high-risk areas mentioned in the document (health, transport, energy, and parts of the
public sector such as asylum, migration, border control and justice, social security, and employment services) is not very exhaustive. Applications that may
seem harmless a priori [in marketing or financial or
insurance services, or in social assistance provided
by nongovernmental organizations (NGOs), etc.]
may bring about threats to rights if they produce discriminatory, biased results, and so on. Other salient
risks are commercial and political manipulation,
and intensive coercion and surveillance by governments and large corporations, which can damage
social cohesion and contravene democratic principles and human rights. In general, AI requires a
proactive approach to risk management, involving
continual risk identification and handling.
The guidelines of the EC's White Paper advocate
for individual commitment and self-regulation and
consider that regulatory intervention could limit
potential innovative capacity; in addition, they advocate the assumption of responsibilities based on the
design of the algorithms but not on the social effects
and impact of their use; finally, they are in favor of
accountability with respect to a series of commitments on technological principles (e.g., transparency) but not on the preservation of values (e.g.,
equity). All this assumes an AI conceptual framework that is reductionist. The danger is that, by using
notions of regulation, self-regulation and ethics in an
imprecise and interchangeable way, we are closing
the way to the possibility of true spaces in which to
define a common standpoint. AI is a technology with
a strong transformative potential; it is an opportunity
for greater European cohesion if it is viewed from the
perspective of promoting the common good of the
peoples of Europe. Moreover, the greater the potential of AI, the larger the scale of application.

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IEEE Technology and Society Magazine - March 2021

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