AI and Ethics
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I. INTRODUCTION
Artificial Intelligence (AI) and Machine Learning have
turned from being just technical innovations to causing
society to undergo a complete transformation right now.
Algorithmic systems often make the decisions in several
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process. After that, the paper shows how these processes
work in real-world cases like facial recognition and
recruitment tools. Lastly, we examine current political and
technical approaches, like the BiasAdv-Framework, to
develop fairer AI systems in the future.
domains. To improve efficiency, these systems are used not
only in human resource management and healthcare, but also
II. UNDERSTANDING ALGORITHMIC BIAS
in judicial sentencing. With the goal to enhance data-driven
A. AI and Algorithmic decision-making:Key Concepts and
Types of Bias
neutrality and objectivity, many companies have already
implemented AI systems into their daily processes.
Nevertheless, while the decision-making is left to the AI
algorithm, many critics view precisely this point as
problematic.
Anything but neutral. New data is often evaluated based on
existing datasets. A practice that not only reflects social
injustices but also perpetuates them. The algorithmic bias
exists because Ai technologies learn from historical data.
This data has been made by humans which leads to the point
that the algorithms adopt past prejudices or discrimination.
The outcome: the perpetuation of biases and the
disadvantaging of certain social groups. Especially women
and racial minorities often experience unfair treatment.
Starting in hiring processes, it could also affect people in
courtrooms while contributing to false accusations. AI
technologies have not only become one of the biggest
technical challenges but also require ethical regulations.
So that AI becomes a helpful tool and not a tool of oppression
it is important to understand how biases arise and can be
mitigated. This paper explains the challenges of ethics in AI
systems. At first, we display the several types of biases while
explaining the concept of algorithmic decision-making
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To understand how algorithmic biases are possible at all, it is
important to understand the decision-making process of any
Artificial Intelligence (AI). All of these processes consist of
data
analysis,
patternanalysis,
drawing
conclusions,
consideration of alternatives and ultimately forming a
recommendation which determines the final action. To learn
from large datasets in order to solve practical problems
specifically is an algorithm-based procedure called machine
learning (ML) (Nishant et al., 2024, p. 20).
A crucial difference between human and machine learning is
the artificial intelligence finding a mathematical formula
which, as soon as applied to training data, produces any
desired outputs. However, if the input is slightly altered, the
output would become flawed if not completely wrong, unlike
how learning in humans works. This is why ML is
categorized into supervised, unsupervised, and semisupervised learning. Meaning the data, which the algorithms
learn from, is either labeled and has been monitored, or
unlabeled, in order for the algorithm to identify patterns itself,
or a combination of both (Nishant et al., 2024, p. 21/22).
Moving on to algorithmic bias, it refers to a predisposition of
certain artificial intelligence systems to carry out decisions
poorly when it comes to marginalized or disadvantaged
groups. Entailing advantages or disadvantages for selected
In the judicial context, there is a documented cognitive
individuals or demographic groups without justification
tendency
(Aquino,
2025).
recommendations uncritically. This is a phenomenon
Therefore, since AI relies on data monitored by humans,
sometimes referred to as “rubberstamping,” meaning that
whatever biases exist in the “real” world are likely to be
even human-made systems may not offer correct oversight if
embedded in the provided data and thus incorporated into the
humans do not apply independent judgment (Krištofík,
system’s algorithm (Aquino, 2023; Fazil et al., 2024).
2025). In the medical context, algorithmic bias can lead to a
Types of Bias
compromise of patient safety and disparities in care and
Three primary categories of bias can be distinguished: data
outcomes. Practical implications including misdiagnosis,
bias (unbalanced training data), user interaction bias (caused
mismanagement, and delays in treatment - therefore, clinician
by selective user behaviour), and algorithm bias (introduced
awareness should be prioritized when employing AI-enabled
through optimization functions and model design) (Pagano et
tools (Aquino, 2023). A broader social danger is imposed by
al., 2023; Fazil et al., 2024). Additional differentiation ideas
what has been deemed a “vicious cycle:” a demographic
explore the concepts of pre-existent bias, which originates
group which is mistreated in everyday life will most likely be
independently of an algorithm and reflects societal
mistreated by the algorithm, as it reflects the historical
inequalities incorporated into training data; emergent bias,
injustices suffered by that particular group, thus revictimizing
which arises when a system is used in different contexts than
those affected and reinforcing structures of past oppression
originally intended or when social concepts change over time;
(Krištofík, 2025; Fazil et al., 2024).
2023;
Krištofík,
for
humans
to
accept
machine-generated
and technical bias, which occurs due to the algorithms
development and can be measured and fixed (Pagano et al.,
III. DISCRIMINAT0RY OUTCOMES IN PRACTICE
2023). Moreover, it is important to realize that biases are not
only copied by machine learning, but also actively amplified.
A. Facial recognition and Criminal Justice
While algorithmic systems are often promoted as objective
Previous biased decisions are acknowledged as patterns and
and efficient decision-making tools, real-world applications
as a result they are applied at a vaster, more automated scale
have demonstrated that they can produce discriminatory
than would previously have been possible. This process is
outcomes. Particularly in high-stakes domains such as
known as bias amplification (Krištofík, 2025).
criminal justice and employment, algorithmic bias can
reinforce existing social inequalities and lead to unfair
B. Fairness and Ethical Implications
treatment of certain demographic groups. Facial recognition
Fundamentally, fairness is a social concept of value
systems convert facial characteristics into biometric
judgment, meaning it is subjective in nature. It varies among
templates and compare them through algorithmic matching.
nations, cultures and institutions such as hospitals or
While this technology can support criminal investigations, it
companies, ultimately erasing the possibility of a universal
is vulnerable to errors, particularly false positives and false
standard. This makes it innately difficult to implement into
negatives. In criminal justice settings, these mistakes may
fixed algorithmic rules (Pagano et al., 2023). However, the
lead to severe consequences, including wrongful accusations
most commonly used fairness metrics include Demographic
or arrests. Importantly, research indicates that such errors are
Parity (involving equal rates of positive outcomes regardless
not distributed equally but disproportionately affect certain
of demographic group belongingness); Equalized Odds
groups (Johnson et al., 2022).
(requiring equal true positive and false positive rates across
A key concern is algorithmic bias. Research demonstrates
groups); and Equality of Opportunity (demanding equal true
that facial recognition systems often perform less accurately
positive rates) - each of them covering a different aspect of
for women, younger individuals, and people with darker skin
what fairness means in practice (Pagano et al., 2023).
tones. According to findings from the National Institute of
Standards and Technology (NIST), racial minorities were
many organizations perceive AI recruitment as a strategic
significantly more likely to be falsely identified than White
advantage in modern human resource management (Chen,
males in some facial recognition systems. One explanation
2023).
for these disparities lies in biased training datasets, as many
Supporters of AI-driven recruitment argue that algorithmic
systems have historically been trained predominantly on
systems may improve recruitment quality by reducing
images of White male faces. As a result, algorithms tend to
unconscious human bias and evaluating candidates based on
perform
while
standardized criteria rather than subjective judgments. AI
underperforming for marginalized populations (Barocas,
tools can automatically screen resumes, rank applicants
Hardt and Narayanan, 2023).
according to predefined qualifications, and help recruiters
However, demographic bias cannot be explained solely by
process large applicant pools in a shorter period. In addition,
unequal datasets. Recent research suggests that algorithmic
some organizations use AI in video interviews to assess
design choices and broader social contexts also influence
candidates’ communication style, facial expressions, and
system performance. For example, developers’ assumptions
behavioural patterns (Chen, 2023). As a result, AI
and regional differences in data collection may shape how
recruitment is often promoted as a more efficient and
effectively systems recognize different demographic groups.
objective alternative to traditional hiring practices.
This demonstrates that AI systems are not entirely neutral
Despite these advantages, concerns about algorithmic
technologies but are influenced by the environments in which
discrimination in recruitment have grown considerably.
they are created. Consequently, facial recognition may
Scholars emphasize that AI systems are not inherently neutral
unintentionally reinforce existing social inequalities (Angwin
because they rely on historical data, which may already
et al., 2016).
contain social inequalities and biased hiring patterns. If
These concerns become particularly problematic in
criminal justice, where algorithmic decisions can directly
affect individual freedom. Studies indicate that the use of
facial recognition technology may contribute to racial
disparities in policing and arrests, especially when authorities
overly rely on automated recommendations. Furthermore,
predictive systems such as COMPAS, which estimate
reoffending risks, have been criticized for reproducing
historical biases embedded in criminal justice data. The lack
of transparency in such “black-box” systems raises additional
concerns regarding fairness and accountability (Kleinberg,
Mullainathan and Raghavan, 2016). Overall, while facial
recognition technologies offer potential benefits in efficiency
and crime prevention, they also raise serious ethical concerns.
Without transparent governance, fair training data, and human
oversight, these systems risk reinforcing discrimination rather
than improving justice outcomes.
training datasets reflect historical preferences for particular
B. Hiring and Recruitment Tools
result, resumes containing terms such as “female” were
Artificial intelligence (AI) has increasingly transformed
downgraded, creating gender bias in the selection process.
recruitment processes by enabling organizations to automate
Due to concerns about discrimination, Amazon eventually
and optimize hiring decisions. AI-supported recruitment
abandoned the system. This case illustrates how historical
systems are used in several stages of the hiring process,
inequalities embedded in training data can unintentionally
including
shape algorithmic hiring decisions (Chen, 2023).
better
for
candidate
overrepresented
sourcing,
groups
resume
screening,
groups, algorithms may unintentionally reproduce these
patterns in future recruitment decisions. This phenomenon is
often described as “bias in, bias out,” meaning that biased
input data leads to discriminatory outcomes. Consequently,
AI recruitment may reinforce labor market inequalities
instead of reducing them.
A prominent example is Amazon’s AI recruitment tool,
introduced in 2014. Since the system was trained on historical
resumes from a male-dominated technology sector, it learned
to favor characteristics associated with male applicants. As a
interviewing, and final selection. By analysing large amounts
Beyond gender bias, algorithmic discrimination may also
of applicant data, these systems aim to improve efficiency,
occur regarding race, skin colour, and personality traits. For
reduce administrative workload, and support recruiters in
example, recruitment systems trained on unrepresentative
identifying suitable candidates more quickly. Consequently,
datasets
may
disadvantage
minority
applicants
or
misinterpret behavioural indicators during AI-supported
interviews. Since many systems operate as “black boxes,”
applicants often do not understand how decisions are made,
which raises concerns about transparency and accountability.
Therefore, scholars increasingly argue that organizations
should combine technological innovation with ethical
safeguards, such as representative datasets, algorithmic
transparency, and human oversight, to ensure fairer hiring
outcomes.
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