Questions about AI

1. Alikov Xenia, Huber Hannah, Seybold Anja, Wintersberger Hanna
Collaborating
What is AI?
To start this report we wanted to first explain what Artificial Intelligence is in General. This is very
important to understand the following report. There are a lot of different Definitions in the Internet
but we want to focus on one: "Artificial Intelligence is the science and engineering of making intelligent
machines, especially intelligent computer programs. It is related to the similar task of using computers
to understand human intelligence, but AI does not have to confine itself to methods that are
biologically observable." (McCarthy, 2004). That is one of the best definitions in our opinion because
it gives the best overview to this topic.
Nowadays more and more AI is used in our daily lives. Almost everyone is using it, consciously or
unconsciously. Researchers predict that 50% of enterprises will have platforms to operationalize AI by
2025 (IBM Cloud Education, 2020). This is a very big amount and shows the importance of AI. However,
while the Artificial General Intelligence remains a further long way off to be included everywhere,
more and more businesses and companies will adopt Artificial Intelligence in the short term to solve
a lot of specific challenges in a faster and better way than humans ever could (Burns, 2022).
AI is very important for us because it gives enterprises insights into their operations. Maybe they
would not be aware of this information previously and without the usage of AI. Also, in some cases,
AI can perform tasks way better than humans could. For instance, when talking about repetitive and
detail-oriented tasks, for example analyzing large numbers of important legal documents, it is
important to ensure relevant fields are filled in properly. Therefore, AI tools often complete their jobs
and tasks quickly and in a professional way with relatively few errors or to better say, better than most
humans (Burns, 2022). Using AI can save a lot of time. AI tools can complete tasks in a few minutes,
while humans would have to sit there for years to complete this task. But these were only a few
examples. Other examples where AI tools are used in our daily lives are: Speech recognition; customer
service, computer vision, Automated stock trading, fraud detection, recommendation engines
Artificial Intelligence is the worldwide leading component of the industrial transformation which
enables intelligent machines to execute tasks autonomously (IBM Cloud Education, 2020). One can
say in an easy way that AI are Systems that act and think like humans and that AI are Systems that act
and think rationally and therefore are unbiased.
In general, AI systems work like that: AI is absorbing a big amount of labeled (marked, so that it will
be noticed) training data, analyzing this data for correlations and patterns, and using these found
patterns to make predictions about the future and about future states. In this way, for instance a
chatbot who is being fed examples of possible text chats can learn how to have lifelike dialogues with
humans via chat, or another example is an image recognition program which can learn to identify and
also describe various objects in images just by going through millions of examples to practice (Burns,
2022).
There are three cognitive abilities on which AI programming focuses on: Learning, reasoning, and self-
correction (IBM Cloud Education, 2020). With these three cognitive abilities it can always improve
itself again and again. It never stops learning and is getting better and better by time. All in All one can

2. say that Artificial Intelligence is a field which combines computer science and a lot of robust datasets
to enable problem-solving which occurs in our daily life or in business circumstances (Burns, 2022).
Does artificial intelligence take over people's jobs?
The short answer to this question is yes, artificial intelligence takes over people’s jobs. That might
sound quite shocking at first, it is not the end of the story, however. When looking at the past few
years, it is astonishing to see that AI and robots actually create more new jobs than destroy. For
example, since the year 2000, around 1.7 million manufacturing jobs have been replaced by
automation and according to the World Economic Forum’s “The Future of Jobs Report 2020” around
85 million more jobs will be taken over by machines until the year 2025. (Thomas, 2022) Over 20
million of those are said to be automation jobs, which are quite easy to replace by robots. (Flynn,
2022) Logically, a lot of people are alarmed by this information. Nonetheless, it is crucial to take the
whole picture into account: While there are a lot of jobs that will very likely be replaced in the near
future, it is estimated that around 97 million new jobs will be created by 2025. (Thomas, 2022)
Neophobia is nothing new in regard to advancements in the technical sector, especially combined with
the job and future aspect. In the 18th century, the first industrial revolution took place in Europe and
North America: Since a lot of people chose to move to the cities and population grew in total, demand
for food and other items rose. (Fleming, 2020) The agricultural and textile industries advanced a lot.
There were also a few inventions like the steam engine or the water wheel which drove production
up. (Noble, 2020) That scared a lot of people, which led to riots and attacks on business owners. After
a while, a lot more jobs were created however. (Fleming, 2020) The same happened in the next
industrial revolutions as well: Jobs got replaced and people panicked, naturally. Yet, at the same time
a lot of new jobs were created, even more than there were to begin with.
The most important fact is that all the employees of the new jobs needed new skills than they had to
have before. And this will be the case in the fourth industrial revolution too: Since a lot of aspects in
life will become automated and replaced by machines, a set of technical skills will come in handy.
These can include computer programming, mathematical knowledge, coding skills or project- and
financial management. (Kaka, 2022) Apart from these new skills, that a lot of people will have to
acquire and learn in the future, there are some other important skills, which will be crucial just because

3. machines cannot learn them: Active listening, coordination with other people, creativity, negotiation
skills and a few other abilities will also be quite essential in the future. (Chidera, 2022)
In our group meeting, we all decided that artificial intelligence most definitely will take over a lot of
jobs in the future. We do not see this as a negative thing though, as so many new jobs will be created
along the way. Also, we agreed that there are a lot of jobs that cannot be replaced by AIs and
machines. These include professions like therapists, artists, musicians and teachers. All of these jobs
and many more irreplaceable ones require a set of skills that AIs cannot learn. These can be creativity,
empathy, social intelligence or complex hand-to-eye coordination. Machines do not have ethics and
are emotionless, which are a big reason why humans can work together so well and have achieved so
much in the past. Furthermore, AI can only grow when programmed to do so, which has to be done
by humans. So as a result, it is clear that robots and machines can help humans make everyday life
easier, but it will never eradicate the purpose and necessity of humans. (Duggal, 2022)
Is it ethically correct to have business decisions be made by an artificial intelligence?
In our discussion about this complex question, we all agreed that it depends on the business decision.
If you for example want to send a cancellation to an employee, we think that this decision should not
be made by AI. It is necessary to include many factors like the team spirit of the employee,
communication skills, etc. These can hardly be measured by computers, and you need a basic human
understanding to evaluate them. If a company on the other hand thinks about buying a new machine,
including AI could be beneficial as the decision is based on financial statistics and numbers.
There are many positive aspects of Artificial Intelligence which will be discussed in the following. First
of all, it has an overview over all the data of a company. Everything is stored on servers and AI can
easily find the information it needs. Humans on the contrary could easily overlook something.
Furthermore, in some situations a machine could be more objective than a person. If a manager is for
example racist or prefers men over women, their decision would be biased.
But there are also many problems that occur when you use Artificial Intelligence for your business
decisions. The first one is that it is hard to rationalize ethics. Ethics is often subjective; it can be

4. described as a “human feeling”. Everybody perceives morals in a different way and that is difficult to
teach AI (Catlin, 2017). Another issue is biases. When you for example type in “schoolgirls” on the
Internet, sexualized pictures of girls wearing short uniforms show up. This does not happen when you
type in “schoolboys” (unesco, 2022). “IBM's Watson for Oncology” is a project that was initiated in
order to diagnose cancer. The problem is that its advice is biased towards American patients and
treatments of the disease (Catlin, 2017). This is only one incident when a bias was discovered, there
are many more. AI was also introduced in the court, but the decisions were not always neutral and
transparent. To avoid that this happens, it is important that AI gets objective data from the beginning
and over the whole process of its creation. When for example creating a database, the focus should
be on balancing the data. The ImageNet database is a negative example as the creators did not pay
attention to this matter. Way mor white-faces then non-white-faces were featured. Another problem
is that when you want to include a human in the decision-making process, you will notice that it is not
always possible. Self-driving cars for instance, have to decide in split-seconds what their next action
will be. When a child runs on the road, they have to decide whether to use the break, drive in another
direction, etc. In this short amount of time it would not be possible for a human to step in and it is
completely up to the AI to act. The same applies to high-frequency trading, where about 90 percent
of all financial trades are algorithmic and there is no room for humans (Marr, 2021).
An interesting area where AI is used to make decisions is the court. It can evaluate cases, increase the
efficiency of lawyers and make the decisions. This is called the “atomization of justice”. It sounds very
easy but there are the same disadvantages as in businesses. Transparency and neutrality are not
always guaranteed, and Human Rights could be endangered when a “wrong” judgment is made.
We saw many cases in which lives, privacy or fundamental rights could be in danger if Artificial
Intelligence does not work properly. This is why we need to find a solution for all these problems if we
want to keep working with AI and increase the responsibilities we give away. There are many
organizations that already deal with this topic. One is the Institute of Electrical and Electronics
Engineers (IEEE). It published a report in 2016 which is called “Ethically Aligned Design: A Vision for
Prioritizing Human Well-being with Artificial Intelligence and Autonomous Systems”. Their mission is
quite interesting, they state that their goal is “to ensure every stakeholder involved in the design and
development of autonomous and intelligent systems is educated, trained, and empowered to
prioritize ethical considerations so that these technologies are advanced for the benefit of humanity.”
Their Model Process is called P7000. It is an approach that considers ethical issues at each state of the
development of Artificial Intelligence. They also have other systems, to provide transparency, manage
privacy issues, avoid biases, etc. The Institute of Electrical Engineers believes that customers will
demand higher ethical standards and more transparency in the future because the skepticism towards
the safety of the consumer’s data is already increasing (Faggella, 2019).
In Conclusion, you can say that AI could make decision processes in companies so much easier as it
saves time and includes a lot of data. Nevertheless, we should be careful right now because the
technology is still not perfect. Biases are the biggest problem at the moment. We need to do research
and improve Artificial Intelligence. Then, a test period would be useful, so we can see whether there
are still problems or if the AI helps.
Is the use of artificial intelligence compilable with the data protection basic regulation?
Before answering the question, there should be taken a look at the General Data Protection Regulation
(GDPR). It is a “legal framework that sets guidelines for the collection and processing of personal
information from individuals who live and outside of the European Union (EU)” (Frankenfield, 2020).
It affects all people that process and own intimate data that came from data subjects which reside in
the European union – no matter the location of the company. (Day, n.d.) When talking about the

5. policies of artificial intelligence, there is almost automatically a connection to the GDPR. It has had the
biggest impact of any global laws regarding a better arranged data market.
AI usually has a connection to sensitive information such as online purchasing records that represent
the habits and preferences of customers but also images for facial recognition systems. So it comes as
no surprise that people get concerned about those private information being exposed or stolen. The
first point that confirms these concerns, is that collected data sets are very sensible. They are not
blurred and normally stored in centralized places which are known to be vulnerable to data breaches.
Second of all, there is the possibility to use reverse engineering e.g. find out if there are one or more
data points in the data set that had the purpose to develop the AI model. An even more alarming
factor is that attackers in some cases have the possibility to recover data that has been removed in
the past. However, there are different kinds of methods to improve internet security such as learning
mechanisms with encrypted data but unfortunately they are still in their infancy. But there is hope for
the future and for a good trade-off between data privacy protection and the utility of AI systems will
be possible (Spyridaki, n.d.).
According to GDPR, every person collecting personal data is obligated to state what they are using it
for and can under no circumstance use it differently and it is required to always minimize the data that
has been held. There is also a need for a limit on how long sets are being held. On the other hand,
machine learning always wants as much data as possible. It can improve spotting patterns when
getting more data and wants to resort to historic patterns to make better decisions. Furthermore,
customers of a business need to be informed on what kind of data is collected and what it is used for.
In most cases, if there is a request to delete certain data a company must remove it. Therefore, it has
to be accessible and identifiable for the specific individual.
Additionally, there is another problem surrounding the transparency under GDPR. A traditional
algorithm is rule-based. Logic regulations that are based on predicted in- and outputs. For these
algorithms, the process is transparent. However, that is not the case for all the machine learning ones.
Some of them create rules swiftly and are therefore more challenging to make transparent. This can
be called “blackbox AI” and comes with the issue that there is no way to explain how the decision was
being made.
However, there are not only negative factors regarding machine learning. It can also be compliant to
GDPR by e.g. spotting intimate data that was not supposed to be collected. When a company thinks
about using AI in this context, there is much to consider. Nonetheless, when following all the principles
the person is making a huge step towards being compliant to GDPR (Day, n.d.).

6. => Conclusion:
> Screenshot proof:

7. > Sources:
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- Catlin, J. (2017, december 21). The Role Of Artificial Intelligence In Ethical Decision Making.
Retrieved from Forbes: https://www.forbes.com/sites/forbestechcouncil/2017/12/21/the-
role-of-artificial-intelligence-in-ethical-decision-making/?sh=3d6d5fe521dc
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