IEEE Technology and Society Magazine - September 2023 - 114

Feature
consequences of data when the impact is dependent
upon the circumstances of the situation. The proposed
contexts are the technological, the cultural, the
legal, the political, and the situational. In the technological
context, it is technology itself playing a central
role in influencing societies. The societal impact of a
technology's application tends to become clear with
an increase in its adoption since in the early stages of
its adoption, there is not sufficient data to assess its
positives and negatives. Also, the rate at which members
of a society adopt the innovation is crucial in
assessing this impact. In the cultural context, in individualistic
societies, citizens resist control and value
their individual freedoms and thus reject applications
of data and algorithms that violate their privacy. People
in collectivistic societies, however, are more tolerant
of monitoring if they see it as benefitting society.
The legal context entails the degree to which the laws
and the legal system provide protection to a nation's
citizens from the misuse of data. Laws concerning
the use and transfer of data differ from country to
country. In the political context, democratic countries
value the freedoms and rights of their citizens
and provide appropriate legal protections to their
citizens. To reduce abuses and safeguard citizens'
rights, many countries have proposed regulations and
laws. The situational context refers to scenarios where
people acquiesce to the use of specific data applications
in one situation but not in another. Three main
drivers characterize the situational context: event-,
preference- or task-driven.
Furthermore, it is worth emphasizing that there
are technologies that are not intended to be bad
per se, but sometimes they may end up damaging
certain " good society " values such as inclusion and
equity. Research has shown that the contexts in
which the technology is used influence social phenomena
such as equity, justice, and inclusion. To
minimize unintended negative consequences, several
frameworks have been proposed which address
fairness, privacy, transparency, explainability, and
accountability when developing algorithms.
Future research
This article has provided a framework to assess
whether data and algorithms are used in ways that
have positive or negative impacts on society. We
highlight that there are many circumstances in which
the impact of data depends on the context. Our
framework can serve as a foundation in conducting
114
further research in this important area. One avenue
for additional research is to conduct empirical studies
that provide further insights and assist in identifying
refinements to the contexts under which an
application of data/algorithms is considered to have
positive or negative impacts. Other topics worthy of
investigation involve comparing user perceptions
regarding the use of data/algorithms in different
legal and cultural contexts (e.g., for legal: EU versus
the United States; for cultural: the United States versus
China). Yet another set of studies could examine
the changing attitudes of people with respect to their
privacy violations and the degree to which they are
willing to share personal data in exchange for free
digital services.
Although globalization has made it possible to
emphasize universal values, which include respect
for the privacy and individuality of all people, there is
still progress to be made in assuring the proper use of
data. Continuing research will help to better understand
the proper use of data in different cultures,
legal systems, and political
structures especially
when technological-driven innovations accelerate.
A comprehensive seT of research and insights
would help shed additional light on the key issues
surrounding the acceptable and unacceptable use
of data and algorithms. Additional research would
also assist policymakers in crafting laws, regulations,
and strategies that safeguard their citizens' privacy
and civil liberties. We believe that these actions will
lead to a more just information society.

Acknowledgments
The authors thank the anonymous reviewer(s) for
their insightful comments which resulted in substantial
improvements to this manuscript.
 References
[1] D. H. Autor, " Why are there still so many jobs? The
history and future of workplace automation, " J. Econ.
Perspect., vol. 29, no. 3, pp. 3-30, 2015, doi: 10.1257/
jep.29.3.3.
[2] M. Garcia, " Racist in the machine: The disturbing
implications of algorithmic bias, " World Policy J., vol.
33, no. 4, pp. 111-117, 2016, doi: 10.1215/074027753813015.
[3]
Good Society Forum. (2022). What is the Good
Society? Accessed: May 12, 2022. [Online]. Available:
https://www.goodsocietyforum.com/about-us
IEEE Technology and Society Magazine
https://www.goodsocietyforum.com/about-us
IEEE Technology and Society Magazine - September 2023

Table of Contents for the Digital Edition of IEEE Technology and Society Magazine - September 2023
