IEEE Technology and Society Magazine - March 2021 - 22

Special Issue Introduction

behavior or attitudes for their own goals without
coercion, can be used to improve mental health, a
part of the SDGs. This article focuses on stress, anxiety, and depression and examines why mental health
is a considerable barrier to equality and why people
with mental health issues have problems accessing
health care. This article presents such systems with
a brief overview of the field and offers general, technical, and critical thoughts on the implementation
as well as impact. The authors think that such technology can complement existing mental healthcare
solutions to reduce inequalities in access as well as
inequalities resulting from the lack of it.
In " SIAMES: Social Impact Advisor and MEasurement System, " Daniel Hernández and Marta
Solórzano present the third SDG-oriented application included in our compilation. The authors highlight the importance of social impact measurement
and the lack of generally agreed-upon indicators for
such measurement and illustrate the potential contributions of AI to creating objective and empirically
based measures that capture the social impact of an
organization, with a goal of increasing standardization, verifiability, and accountability. They briefly
describe SIAMES, a prototype recommender system
of social impact indicators that extracts structured
information from a corpus of impact measurement,
reports through ontology-based semantic text mining and retrieves appropriate indicators by applying
case-based reasoning.
In the third part of the Special Issue, which will
be appear in a subsequent issue of the Magazine,
the articles illustrate legislative, methodological,
and technological proposals for the promotion and
support of an inclusive AI and equitable access to
its benefits.
In " A Wide Human-Rights Approach to Artificial Intelligence Regulation in Europe, " Jesús Salgado-Criado and Celia Fernández Aller propose
human rights as the basic framework for a future
AI regulation. The authors argue that three European Commission's White Paper on AI is focused
mainly on risk and some individual rights, such as
privacy, whereas the collective dimension of society
as a whole is overlooked. They highlight the importance of following a human rights-based approach
in the regulatory efforts as it is necessary to establish a universal governance model and a general
normative framework for AI. Human rights should
replace ethics as the dominant framework for a

debate. A description of the main principles of the
rights approach is offered. Another key element of
the article is the need to develop a sound technical
framework within the regulation, as any regulation
on a technical matter should encompass an architectural model on how the overall system functions
and interacts. Finally, the authors point out that an
auditing system is also required to allow accountability in the algorithmic process.
In the article " AI Ethics for Sustainable Development
Goals, " Monasterio Astobiza et al. show how AI technologies can be used to meet the 17 SDGs and its 169
targets. This article clarifies what people really mean
by " ethics " in AI ethics and elucidates a road map to
implement " ethics by design " standards to establish
satisfactory measures of fairness, transparency, and
explainability of algorithms when used for social good
as, for example, in the promotion of the SDGs.
In " Bias and Discrimination in AI: A Cross-Disciplinary Perspective, " the authors critically survey
relevant literature about bias and discrimination in
AI from an interdisciplinary perspective that embeds
technical, legal, social, and ethical dimensions. The
authors show that finding solutions for attesting
and avoiding discrimination in AI requires robust
cross-disciplinary collaborations and highlight a
number of interdisciplinary challenges to address in
this area.
Finally, in the article " Explaining the Principles
to Practices Gap in AI, " Schiff et al. review the gap
between high-level principles for responsible uses of
AI and the effective application of those principles
in practice. The authors outline five explanations
for this gap ranging from a disciplinary divide to an
overabundance of tools and argue that an impact-assessment framework which is broad, operationalizable, flexible, iterative, guided, and participatory is a
promising approach to closing the principles-to-practices gap.
We hope that this selection of articles will illustrate the path toward an AI for a fair, just, and
equitable world, and motivate researchers and practitioners to travel along it.


 References
[1] B. Lord. (2020). Inequality in America: Far Beyond
Extreme. [Online]. Available: https://inequality.org/
great-divide/inequality-in-america-far-beyond-extreme/

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IEEE Technology and Society Magazine - March 2021

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