14.2 Can We Trust It?
295
surveillance such as being the friend of a friend of a friend of a suspected criminal,
where riding a train without a ticket might be enough to consider someone a criminal
or suspect.
Moreover, it is now basically impossible to use the Internet without agreeing to
Terms of Use beforehand, which typically forces you to agree with the collection
of personal data, even if you don’t like this—otherwise you will usually not get a
service. The personal data collected by companies, however, will often be aggregated
by secret services, as Edward Snowden’s revelations about the NSA have shown. In
other words, it seems that the GDPR, which claims to protect us from unwanted
collection and use of personal data, has actually enabled it. Consequently, there are
huge amounts of data about everyone, which can be used to create digital doubles.
Are our personal profiles reliable at least? How similar to us are our digital twins
really? Some skepticism is in place. We actually don’t know exactly how well the
learning algorithms, which are fed with our personal data, converge. Social networks
often have features similar to power laws. As a result, the convergence of learning
algorithms may not be guaranteed. Moreover, when measurements are noisy (which
is typically the case), chances that digital twins behave identical to us are not very
high. Hence, we may be easily misjudged. This does, of course, not necessarily
exclude that averages or distributions of behaviors may be rather accurate (but there
is no guarantee).
14.2.6 Scoring, Citizen Scores, Superscores
The approach of “scoring” goes a step further. It assesses people based on personal
(e.g. surveillance) data and attributes a certain economic or societal value to them.
People would be treated according to their score. Their lives would be “curated”. Only
people with a high enough score would get access to certain products or services,
while others would not even see them on their digital devices at all, or see a downgraded offer. Personalized prizing is just one example for the personalization of our
digital world.
According to my assessment, scoring is not compatible with human rights, particularly human dignity (see below).
42 However, you can imagine that there are currently
quite a lot of scores about you. Each company working with personal data may have
several of them.
43 You may have a consumer score, a health score, an environmental
footprint, a social media score, a Tinder score, and many more. These scores may
then be used to create a superscore, by aggregating different scores into an index.
44 In
42 Superscoring: Wie wertvoll sind Sie für die Gesellschaft? PC Welt (February 5, 2020) https://www.
pcwelt.de/ratgeber/Superscoring-Wie-wertvoll-sind-Sie-fuer-die-Gesellschaft-10633488.html.
43 Silicon Valley is building a Chinese-style social credit system, Fast Company (August 26,
2019) https://www.fastcompany.com/90394048/uh-oh-silicon-valley-is-building-a-chinese-stylesocial-credit-system.
44 Super-Scoring? https://www.superscoring.de.
295
surveillance such as being the friend of a friend of a friend of a suspected criminal,
where riding a train without a ticket might be enough to consider someone a criminal
or suspect.
Moreover, it is now basically impossible to use the Internet without agreeing to
Terms of Use beforehand, which typically forces you to agree with the collection
of personal data, even if you don’t like this—otherwise you will usually not get a
service. The personal data collected by companies, however, will often be aggregated
by secret services, as Edward Snowden’s revelations about the NSA have shown. In
other words, it seems that the GDPR, which claims to protect us from unwanted
collection and use of personal data, has actually enabled it. Consequently, there are
huge amounts of data about everyone, which can be used to create digital doubles.
Are our personal profiles reliable at least? How similar to us are our digital twins
really? Some skepticism is in place. We actually don’t know exactly how well the
learning algorithms, which are fed with our personal data, converge. Social networks
often have features similar to power laws. As a result, the convergence of learning
algorithms may not be guaranteed. Moreover, when measurements are noisy (which
is typically the case), chances that digital twins behave identical to us are not very
high. Hence, we may be easily misjudged. This does, of course, not necessarily
exclude that averages or distributions of behaviors may be rather accurate (but there
is no guarantee).
14.2.6 Scoring, Citizen Scores, Superscores
The approach of “scoring” goes a step further. It assesses people based on personal
(e.g. surveillance) data and attributes a certain economic or societal value to them.
People would be treated according to their score. Their lives would be “curated”. Only
people with a high enough score would get access to certain products or services,
while others would not even see them on their digital devices at all, or see a downgraded offer. Personalized prizing is just one example for the personalization of our
digital world.
According to my assessment, scoring is not compatible with human rights, particularly human dignity (see below).
42 However, you can imagine that there are currently
quite a lot of scores about you. Each company working with personal data may have
several of them.
43 You may have a consumer score, a health score, an environmental
footprint, a social media score, a Tinder score, and many more. These scores may
then be used to create a superscore, by aggregating different scores into an index.
44 In
42 Superscoring: Wie wertvoll sind Sie für die Gesellschaft? PC Welt (February 5, 2020) https://www.
pcwelt.de/ratgeber/Superscoring-Wie-wertvoll-sind-Sie-fuer-die-Gesellschaft-10633488.html.
43 Silicon Valley is building a Chinese-style social credit system, Fast Company (August 26,
2019) https://www.fastcompany.com/90394048/uh-oh-silicon-valley-is-building-a-chinese-stylesocial-credit-system.
44 Super-Scoring? https://www.superscoring.de.