IEEE Technology and Society Magazine - September 2023 - 113

United States, the United Kingdom, Germany, and
China, Steinacker et al. [27] found that the trust in
governmental institutions varies greatly in the four
countries. For the United States, the percentage of
respondents who had very little, or no trust was 40%;
in the United Kingdom, it was 48%; in Germany, the
figure was 37%. On the other hand, in China, only 7%
have no or little trust in governmental institutions,
that is, there is a high degree of trust in the government.
Therefore, it was not surprising that the same
study noted that half (50%) of the respondents from
China somewhat and strongly accept the public use
of facial recognition technologies. Such percentages
for the United States, the United Kingdom, and
Germany were found to be lower than that for China.
Context interdependencies
It is worth noting that the five contexts (technological,
cultural, legal, political, and situational) are not
necessarily independent of each other. For instance,
political and legal contexts overlap as a democratic
political system is likely to have substantial legal
frameworks to protect citizens. Whereas such protections
are mostly lacking in autocratic nations.
Also, technological context can be influenced by
political and cultural contexts. As discussed earlier,
some societies (e.g., China) place restrictions
on the use of certain technologies and so do some
cultures (e.g., Amish). Similar overlaps may occur
among other contexts. Therefore, any investigation
of the positive/negative impact of data on societies
needs to account for the interdependencies among
the contexts.
Quantitative measures of the directions and
strengths of associations among the five contexts
could help shed additional light on the interdependencies.
As an example, we calculated the correlation
coefficient between the democracy index [24]
and the global innovation index [28] of nations. A
positive (negative) correlation would indicate that
nations with fuller democratic political systems are
more (less) innovative compared to nations with
less democratic systems. A correlation analysis of
the 127 countries for which data was available for
both democracy index scores and global innovation
index scores reveals a statistically significant correlation
(r = 0.667, p < 0.01). This supports that innovation
is higher in more democratic nations.
Here, we briefly discuss two innovations and the
contexts that influenced how they impacted society.
September 2023
The first innovation we consider is the introduction
of textile machines in Britain, which precipitated protests
in 1812 by textile workers employed in knitting
and weaving professions (protestors are referred to as
the Luddites) [1]. The workers were concerned about
unskilled workers replacing them. Since the textile
industry was labor-intensive, the workers were concerned
that the machinery introduced would be used
to further exploit workers and create unsafe working
environments. Therefore, both the technological and
situational contexts played a role. Over time, textile
industry automation resulted in many societal benefits
(e.g., lower costs and higher productivity), but
also resulted in displacement of workers.
Social media networks (SMN) are another innovation
where technological, legal, political, and cultural
contexts are all at play. Technology continues to play
a central role as advances in new technologies such as
AI and machine learning are adding more advanced
features to the SMN applications. Also, with widespread
social media use, legal, and political issues
have emerged. Increasingly, many governments are
becoming concerned about the SMN use in the proliferation
of fake news and illegal activities. Consequently,
new laws and regulations are being enacted
to protect citizens against such misuses. Furthermore,
culture plays a role in regulating the SMN use as, for
example, conservative cultures may find some content
objectionable while it may be completely acceptable
in more liberal cultures. Therefore, it is advisable
to examine all such contexts in assessing the societal
consequences of SMNs.
There is agreement that data and algorithms
improve efficiencies, enhance decision making
capabilities, and provide competitive advantages. If
applied properly, data can be a harbinger of societal
good. But the same uses of data that benefit
societies can have negative consequences and in
fact be harmful to individuals, business enterprises,
and societies. Activities with negative consequences
include identity thefts, hacking, ransomware attacks,
and involuntary tracking and monitoring. While the
values that are necessary for a good society can be
used to assess the impact of data, the context in
which the data is used must be considered.
This research proposes a contextual framework
that can provide guidance to researchers and policymakers
in organizing and investigating impacts of
data/algorithms. The framework consists of five contexts
germane to examining the positive and negative
113
IEEE Technology and Society Magazine - September 2023

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