IEEE Technology and Society Magazine - June 2023 - 43

already at hand is 2.4 million workers, projected to
be up to 10 million by 2030 when the labor supply
in Germany is expected to shrink from around 43
million people today to 37 million. Germany sees
already a shortage not only of engineers and information-technology
(IT) specialists, but also of care
workers, teachers, and other professionals [3], [4].
Elsewhere in the world, the problem is also beginning
to emerge, for instance, the United States alone
needs to hire additional half a million cooks in restaurants
in the next decade. The shortage of nurses
is a pervasive problem across countries, and the situation
worsens when we consider the prospect of
needing to hire hundreds or thousands more of them
in the next 10 years [8]. Initially, it was believed that
the pandemic caused a shortage of workers due to
the lack of mobility and immigration. However, the
situation has only worsened postpandemic [7].
The true challenge is that the workers' mobility
that helps mitigate the issue in some countries will
simultaneously jeopardize the perspectives of the
labor market in others. Developing countries are
already losing some of their valuable workers to
richer countries. For example, most of the medical
doctors trained in Mozambique emigrate and work
abroad [5]. Unfortunately, we are not currently
heading in the right direction to solve this issue, and
it is a situation that eerily echoes the environmental
crisis of the past.
Clearly, there is no one immediate mitigating
solution to address the multiple societal factors that
contribute to the crisis. While political solutions are
still in progress, technological solutions, such as telemedicine,
can boost productivity and enable remote
collaboration, thereby preventing the situation from
escalating into a global security issue. Furthermore,
technology can optimize the recruiting process
and improve the participation of underrepresented
groups in the labor force.
For instance, AI can optimize recruiting, ensuring
that individuals find jobs that fit them well. AI applications
in question include programmatic advertising
for the optimized impact of job ads and timely
bringing together job offers and suitable applicants;
effective communication with applicants through
AI-based chatbot systems; and augmented writing
for bias-free and gender-neutral wording for job
advertisements. Indeed, it is becoming increasingly
important that job postings are formulated and distributed
in such a way that they have the greatest
June 2023
possible impact, and no group of suitable applicants
feels excluded.
Observing the interaction of job seekers with
web-based job advertisements proved to be an invaluable
tool to better understand their habits and tendencies.
We show in Figure 1 a snapshot of the graph
for the relevant interactions of visitors to the job portal
Jobstairs [15] with the job advertisements. The
combination of our analysis and the statistics data in
Germany [4] reveals that it can take over half a year
for a company to fill an IT job vacancy, while job
seekers with IT-related qualifications may still spend
months searching for a job that matches their skills.
This situation is unfortunate for both the companies
and the candidates.
By using natural language processing to analyze
text-based communication, we can optimize the
matching process. Transforming words and sentences
into mathematical objects enables computers
to understand language and match candidates with
job requirements based on qualifications [9] without
considering other, personal information. The purpose
of the prematching procedure based solely on
skills is to reduce possible biases in the recruitment
process. However, for the algorithm to be accurate, it
is crucial that the computer understands whole sentences,
not just single words. This can be achieved,
for instance, by applying the transformer-based
language models trained on domain-specific data.
Optimizing the recruitment process in this way not
only prevents some unintended bias, but also significantly
reduces the duration of the process, saving
time and money for both job seekers and employers.
Our language models are designed to understand
the nuances of words within the context of a job
advertisement. To illustrate this, let us demonstrate an
example from our matching algorithms [9], designed
for application in the German language space. The
job advertisement states: " Wir suchen einen Bäcker
mit Informatikstudium und Erfahrung als Bäcker. Wir
bieten Leistungen wie frisches Brot vom Bäcker. Ihr
Ansprechpartner ist John Bäcker. " The word " Bäcker "
is used in various contexts within this text, and our
program can differentiate between them. It can determine
whether " Bäcker " is used as a skill, a benefit, or
as a name. This differentiation is crucial for accurately
matching candidates with job requirements. Additionally,
we are using a data-driven ontology in contrast
to the expert-knowledge systems like, for instance,
the ESCO-the European Skills, Competences, and
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IEEE Technology and Society Magazine - June 2023

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