=Paper=
{{Paper
|id=Vol-3839/paper1
|storemode=property
|title=A Comprehensive Strategy to Bias and Mitigation in Human Resource Decision Systems
|pdfUrl=https://ceur-ws.org/Vol-3839/paper1.pdf
|volume=Vol-3839
|authors=Silvia D’Amicantonio,Mishal Kizhakkam Kulangara,Het Darshan Mehta,Shalini Pal,Marco Levantesi,Marco Polignano,Erasmo Purificato,Ernesto William De Luca
|dblpUrl=https://dblp.org/rec/conf/xaiit/DAmicantonioKMP24
}}
==A Comprehensive Strategy to Bias and Mitigation in Human Resource Decision Systems==
https://ceur-ws.org/Vol-3839/paper1.pdf
A Comprehensive Strategy to Bias and Mitigation in
Human Resource Decision Systems
Silvia D’Amicantonio1,2,† , Mishal Kizhakkam Kulangara1,† , Het Darshan Mehta1,† ,
Shalini Pal1,† , Marco Levantesi1,3,* , Marco Polignano4 , Erasmo Purificato5,‡ and
Ernesto William De Luca1,3
1
Otto von Guericke University Magdeburg, Magdeburg, Germany
2
Polytechnic University of Milan, Milan, Italy
3
Leibniz Institute for Educational Media | Georg Eckert Institute, Brunswick, Germany
4
University of Bari Aldo Moro, Bari, Italy
5
Joint Research Centre, European Commission, Ispra, Italy
Abstract
In recent years, Machine Learning (ML) and Artificial Intelligence (AI) models have become integral to various
business operations, especially within Human Resource (HR) systems. These models are primarily used to
automate decision-making processes in recruitment, performance assessment, and employee management,
enhancing efficiency and streamlining tasks. However, the increasing use of these automated systems has raised
significant concerns about the presence of bias, which can lead to discriminatory practices. Such biases may
exclude qualified candidates and diminish opportunities, while also posing substantial risks to a company’s
reputation, with potential legal and ethical consequences. This paper addresses these challenges by exploring the
root causes of bias in HR-related ML models and proposing best practices for mitigation. It presents a thorough
examination of fairness concepts and definitions within the context of HR decision-making, emphasizing the
complex nature of selecting appropriate mitigation techniques based on the specific models and datasets used.
Through an empirical evaluation of various mitigation strategies, the study reveals that no single approach can
fully satisfy all fairness metrics, highlighting the inherent trade-offs between accuracy and fairness. The findings
offer valuable insights into optimizing these trade-offs and provide actionable recommendations for achieving
fairer, unbiased outcomes in automated HR systems. Additionally, this research underscores the ongoing need
for further study and discussion to enhance transparency and fairness in ML models, contributing to a more
equitable HR landscape.
Keywords
Machine Learning, Biases and Fairness, Human Resource Decision-Making, Mitigation Strategies
1. Introduction
The rise of Artificial Intelligence (AI) and Machine Learning (ML) has revolutionized numerous in-
dustries, with Human Resources (HR) being one of the most significantly impacted [2, 3, 4]. Globally,
companies are increasingly adopting these technologies to enhance decision-making capabilities and
boost efficiency [5]. A specific class of ML technologies, commonly referred to as Black Box mod-
XAI.it - 5th Italian Workshop on Explainable Artificial Intelligence, co-located with the 23rd International Conference of the Italian
Association for Artificial Intelligence, Bolzano, Italy, November 25-28, 2024 [1]
*
Corresponding author.
†
These authors contributed equally and are listed in alphabetical order.
‡
The author contributed to this work while affiliated with Otto von Guericke University Magdeburg, Germany. The view
expressed in this paper is purely that of the author and may not, under any circumstances, be regarded as an official position
of the European Commission.
$ silvia.damicantonio@mail.polimi.it (S. D’Amicantonio); mishal.kizhakkam@st.ovgu.de (M. K. Kulangara);
het.mehta@st.ovgu.de (H. D. Mehta); shalini.pal@st.ovgu.de (S. Pal); marco.levantesi@ovgu.de (M. Levantesi);
marco.polignano@uniba.it (M. Polignano); erasmo.purificato@acm.org (E. Purificato); deluca@ovgu.de (E. W. De Luca)
https://hcai.ovgu.de/Staff/Ph_D+Students/Marco+Levantesi.html (M. Levantesi); https://marcopoli.github.io/
(M. Polignano); https://erasmopurif.com/ (E. Purificato); https://ernestodeluca.eu/ (E. W. De Luca)
0009-0001-3740-7539 (M. Levantesi); 0000-0002-3939-0136 (M. Polignano); 0000-0002-5506-3020 (E. Purificato);
0000-0003-3621-4118 (E. W. De Luca)
© 2024 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR
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els [6, 7, 8], is characterized by the opacity of their internal workings as they take inputs and produce
output, but the decision-making process remains hidden. Major corporations such as Google, IBM,
SAP, and Microsoft are already utilizing these algorithmic systems for automated HR management [9].
Black Box models in HR streamline key functions such as recruitment and performance evaluation. The
main drivers for their adoption include cost and time savings, increased productivity, and enhanced
certainty in decision-making [10, 11]. AI is widely used to evaluate employee engagement and retention
by analyzing feedback surveys and performance data. These insights are then applied to monitor
achievement, recommend personalized job opportunities, and set objectives. Additionally, AI tools
can assist in corrective actions for underperformance and inappropriate behaviours, and even support
training by identifying employees likely to make errors and suggesting relevant skill-improvement
programs. In recruitment, AI primarily contributes by screening resumes, identifying key terms in job
applications, and analyzing video interviews to evaluate job fit and match candidates to open positions.
Although AI is often viewed as providing fairer, more impartial decision-making than humans, recent
studies reveal a high risk of bias and discrimination in these systems [9, 12, 13, 14, 15]. Bias can manifest
in several ways during the implementation of decision-making algorithms. For instance, historical data
used for training models may reflect past societal imbalances, resulting in these biases being reproduced
in AI-driven decisions [16]. The opaque nature of Black Box models exacerbates this issue, making bias
identification and mitigation particularly challenging. The complexity of the underlying algorithms
and deep learning techniques makes these models difficult to interpret. This lack of trasparency poses
ethical and legal risks, potentially leading to discriminatory hiring practices and damaging a company’s
reputation [9]. In response, researchers and developers have proposed various strategies to address these
biases, including using more diverse training datasets, implementing fariness-aware algorithms, and
ensuring greater transparency and accountability in AI systems [17, 18]. While AI offers tremendous
potential to enhance HR processes, it is essential to recognize and mitigate the biases these systems
may introduce. Achieving fair and unbiased AI in HR requires a combination of better data practices,
increased transparency, regulation, and continuous scrutiny and adjustment of AI models. This paper
explores the inherent biases in HR-related Black Box models and outlines strategies for mitigating these
biases to ensure fair and equitable decision-making.
2. Related Work
2.1. Understanding Biases and Mitigation Techniques
To fully grasp the reasons behind bias algorithms, it is essential to first review the concept of bias.
We refer to Cognitive Bias as the type of bias that can be introduced in hiring processes supported by
AI [19]. When the latter is used in hiring, the lack of transparency and accountability can heighten the
risk of replicating social discrimination. The following subsections explore potential causes of bias and
propose strategies to mitigate them.
2.1.1. Source and Implication of Bias
Cognitive bias, a well-documented phenomenon in human decision-making, can also affect AI-driven
recruitment. Soleimani et al. [20] identify two primary sources of bias in AI: the training dataset and the
algorithm itself. Training datasets often contain historical data, which may include underrepresented
or overrepresented groups. Furthermore, these datasets may encode biases related to sensitive attributes
due to mislabeled data. This can result in the exclusion of highly qualified candidates or even lead
to legal issues as a consequence of violating anti-discrimination laws [12] [21]. Algorithmic biases
can arise when developers make subjective assumptions or use inappropriate selection criteria. For
example, including ethnicity, culture or gender in an algorithm can lead to wrong correlations between
these attributes and the target variable [22]. Finally, algorithms could fail to account for job-specific
requirements and produce decisions that are misaligned with the actual needs of the position [12].
Source of Bias Mitigation Best Practices
- Expand datasets sources
Training dataset non-representative
Dataset - Keep datasets up to date
Training dataset out of date
- Blind recruitment
Unable to formulate assumptions - Knowledge Sharing
Algorithm
Unable to account for context-specific requirements - Third parties Audits
Table 1
Sources of Bias and Mitigation techniques identified in our study. The sources are divided into arising from
polluted training dataset or improper algorithm [20]. The mitigation techniques are classified accordingly.
2.1.2. Mitigation Strategies
Bias mitigation can be addressed at multiple stages of AI tool development. First, ensuring that the
training dataset is representative of the population is crucial. Data should be sourced from diverse
demographic groups and regularly updated to prevent the perpetuation of historical biases. Vivek [23]
suggests that blind recruitment is an effective method for reducing unconscious bias. In the context
of AI, blind hiring involves masking potentially bias-inducing variables from resumes in order to let
the algorithm focus purely on skills and experience of candidates. Another key strategy for mitigating
algorithmic bias is knowledge sharing between AI developers and HR professionals. Soleimani
et al. [20] demonstrate that exchanging information at different stages of development improves
recruitment model performance. Finally, independent audits and periodic assessments are vital for
detecting biases and ensuring that the algorithm remains fair over time [12]. It is also suggested to
release audit results since it can build trust with consumers and ensure transparency. In Table 1 the
potential sources of bias as well as their mitigation techniques are summarized.
2.1.3. Regulatory and Ethical Consideration
The rapid expansion of automated decision-making systems has highlighted the need for government
regulation to ensure fairness for all individuals. Many countries have enacted laws to prevent discrimi-
nation based on ethnicity, gender, religion or nationality [24]. In the context of employment, the EU
AI Act (Annex III: Article 6(2)) classifies AI systems used in recruitment, employee management, and
termination as high-risk. As per the law, systems must be regulated to ensure fairness, transparency,
and non-discrimination in hiring and workplace decisions, thus minimizing bias to protect individual’s
rights [25]. Additionally, the U.S. Equal Employment Opportunity Commission (EEOC) has established
guidelines, like the usage of the four-fifths rule (Table 2), to promote equal employment opportunities and
prevent bias during the hiring process [26]. Generally speaking, three main theories of discrimination
are often used to analyze bias:
• Disparate Treatment: Refers to intentional discrimination based on protected characteristics [27,
26]. Using sensitive attributes to build the model can prevent unfairness but it could also violate
anti-discrimination laws and produce disparate treatment [27].
• Disparate Impact: Addresses unintentional discrimination, where proxy (not explicitly sensitive)
attributes lead to disproportionate negative outcomes for a protected group [27].
• Disparate Mistreatment: Focuses on differences in misclassification rates between groups
based on sensitive attributes, considering false positive and false negative rates when evaluating
fairness [28].
The distinction between disparate impact and disparate mistreatment is important. In cases where
ground truth data is unavailable and historical data is unreliable, disparate mistreatment may not
be suitable due to difficulty in distinguishing between correct and incorrect classifications. On the
other hand, when ground truth data is available, focusing on disparate impact may lead to reverse
discrimination [28].
Applicants Hired Selection Rate Percent Hired
80 White 48 48/80 60%
40 Black 12 12/40 30%
Table 2
Example from EEOC guidelines [26]. The four-fifths rule requires that the selection rate for any protected group
should be at least 80% of the highest selection rate among groups. In this case, the highest selection rate is 60%,
hence for the other group, i.e. Black, the selection rate should be at least 48%.
2.2. Fairness Metrics
Several fairness metrics have been proposed to assess the fairness of decision-making systems. This
section highlights some of the most widely discussed metrics, which are generally categorized into two
main types: Individual Fairness and Group Fairness [29, 30]. Individual Fairness refers to ensuring
that predictions are fair for each individual, whereas Group Fairness focuses on equal treatment of
groups with different values for sensitive attributes.
To define these metrics, the following notation is introduced:
• 𝑋 : Input feature vector of applicants, excluding sensitive attributes.
• 𝐴 : Sensitive attributes (e.g., race, gender).
• 𝐶 : Binary classifier mapping 𝑋 and 𝐴 to a prediction 𝐶.
• 𝑌 : The actual outcome of the model.
Hence, the probability to observe an event 𝐸 given that the attribute 𝐴 has assumed value 𝑎 is:
𝑃𝑎 (𝐸) = 𝑃 (𝐸|𝐴 = 𝑎) (1)
2.2.1. Individual Fairness
Fairness through unawareness: An algorithm can produce fair outcomes by excluding all sensitive
attributes from the input feature vector, preventing the system from relying on these attributes to make
a decision [17, 18]. Thus, the final outcome can be defined as
𝐶 = 𝐶(𝑋, 𝐴) = 𝐶(𝑋) (2)
potential issue −→ Attributes that are correlated with sensitive information (proxies) may still lead to
biased outcomes.
Fairness through awareness: In this approach, an algorithm is considered fair if it produces similar
outcomes for similar individuals. Specifically, if two applicants have similar feature vectors, the
probability distributions of their predicted outcomes should also be similar, assuming a small similarity
metric 𝑑(𝑖, 𝑗) [18, 30]:
𝐶(𝑋 𝑖 , 𝐴𝑖 ) ≈ 𝐶(𝑋 𝑗 , 𝐴𝑗 ) (3)
Where:
• 𝑋 𝑖 and 𝐴𝑖 are the feature vectors of applicant 𝑖.
• 𝑋 𝑗 and 𝐴𝑗 are the feature vectors of applicant 𝑗.
Counterfactual Fairness: A model is counterfactually fair if the prediction for an individual remains
the same in both the real world and in a counterfactual world where the individual belongs to a different
demographic group [30]. The causal relationship between 𝑋 and 𝐴 must be such that, if 𝐴 changes
from 𝑎 to 𝑎′ then 𝑋 changes from 𝑥 to 𝑥′ . The model is counterfactually fair if:
𝑃 (𝐶(𝑥, 𝑎) = 𝑐|𝑋 = 𝑥, 𝐴 = 𝑎) = 𝑃 (𝐶(𝑥, 𝑎′ ) = 𝑐|𝑋 = 𝑥, 𝐴 = 𝑎′ ) (4)
for all 𝑐 and any value of 𝑎′ attainable by 𝐴 [31].
Figure 1 illustrates a causal graph in a hiring scenario. The sensitive attribute, Gender (G), is derived from
Years of Experience (proxy), which directly influences the outcome (Hired/Not Hired). This setup would
not be counterfactually fair, as the proxy influences the outcome [32]. To avoid proxy discrimination,
there should be no proxy connections between the sensitive attribute and the outcome [33].
Race
GPA Graduated
Hired/ Not
Years of
Hired experience
Figure 1: A causal graph representing employee recruitment with G as a sensitive attribute, Years of Experience
as a proxy attribute and GPA as a resolving attribute.
2.2.2. Group Fairness
Demographic parity: Also called statistical parity, this metric ensures that the acceptance probability
𝑝
is the same (or within a given percentage) across groups [26, 32]. For some tolerance 𝜖 = 100 ∈ [0, 1],
|𝑃𝑎 (𝐶 = 1) − 𝑃𝑏 (𝐶 = 1)| ≤ 𝜖 (5)
Equalized Odds: This metric requires that both protected and unprotected groups have equal True
Positive and False Positive rates [29, 34]:
𝑃 (𝐶 = 1 | 𝐴 = 0, 𝑌 = 𝑦) = 𝑃 (𝐶 = 1 | 𝐴 = 1, 𝑌 = 𝑦), 𝑦 ∈ {0, 1} (6)
Here, 𝐶 and 𝐴 are independent conditional on 𝑌 .
Equal Opportunity: This requires that protected and unprotected groups have equal True
Positive Rates, focusing on fair positive outcomes [29, 34]:
𝑃𝑎 (𝐶 = 1 | 𝑌 = 1) = 𝑃𝑏 (𝐶 = 1 | 𝑌 = 1) (7)
Overall Accuracy Equality: This metric ensures that the prediction accuracy is the same across
groups. In an HR context, it ensures that highly qualified and underqualified applicants are treated
equally in both protected and unprotected groups [32, 34]:
𝑃𝑎 (𝐶 = 𝑌 ) = 𝑃𝑏 (𝐶 = 𝑌 ) (8)
Predictive Rate Parity (Sufficiency): This condition is met when the Positive Predictive Value (PPV)
and Negative Predictive Value (NPV) are equal for both protected and unprotected groups. It helps
prevent disparate mistreatment and promotes fairness [28]. Specifically, it ensures:
𝑃𝑎 (𝐶 = 1|𝑌 = 1) = 𝑃𝑏 (𝐶 = 1|𝑌 = 1) (1) (9)
And for Negative Predictive Value:
𝑃𝑎 (𝐶 = 1|𝑌 = 0) = 𝑃𝑏 (𝐶 = 1|𝑌 = 0) (2) (10)
A classifier satisfies Predictive Rate Parity if both conditions (1) and (2) are met [26].
Treatment Equality: A classifier satisfies this condition when the ratio of False Positives
and False Negatives is equal across groups [32]:
𝑃𝑎 (𝐶 = 1 | 𝑌 = 0) 𝑃𝑏 (𝐶 = 1 | 𝑌 = 0)
= (11)
𝑃𝑎 (𝐶 = 0 | 𝑌 = 1) 𝑃𝑏 (𝐶 = 0 | 𝑌 = 1)
Fairness Metrics Advantages Disadvantages
Straightforward solution by avoiding ex- Does not consider the correlation between
Fairness through plicit use of sensitive attributes sensitive and non-sensitive attributes
unawareness
Considers both the similarity of individ- Choice of distance metrics can impact re-
Fairness through uals and the similarity of outcome distri- sults and may require fine-tuning. Sensitiv-
awareness butions. Flexible similarity definition for ity to the definition of similarity, which can
different scenarios. vary across scenarios.
Considers the impact of changes in sensi- Requires prior knowledge of causal relation-
Counterfactual tive attributes on both non-sensitive fea- ships between sensitive and non-sensitive
fairness tures and predicted outcomes. attributes. Practical implementation may be
challenging,when causal relationships are
complex.
Promotes fair representation of all demo- Rules out an accurate classifier (𝐶 = 𝑌 ),
Demographic Par- graphic groups. considering it unfair when the base rates
ity of the two groups are significantly different.
𝑃𝑎 (𝐶 = 1) ̸= 𝑃𝑏 (𝐶 = 1).
Ensures overall accuracy is consistent Heavily dependent on the error type. It al-
Overall Accuracy across different groups. Easy to imple- lows you to make up for rejecting qualified
Equality ment. members of one group by accepting unqual-
ified members of another group.
Equalized odds Considers both positive and negative pre- May be sensitive to class imbalances and
dictive performance. Addresses potential prevalence differences.
disparities in error rates between groups.
Emphasizes equal opportunities for posi- Does not consider false positive rates, po-
Equal opportunity tive outcomes. tentially overlooking negative consequences.
Similar to equalized odds, may face chal-
lenges in practical implementation.
Table 3
Advantages and Disadvantages of Different Fairness Definitions [18, 29]
2.3. Mitigation Mechanisms
Selecting the appropriate fairness metric for a model requires careful consideration of the legal, ethical,
and social implications [35]. As discussed earlier, different fairness metrics offer distinct advantages
and disadvantages (see Table 3). A recent research has demonstrated that it is impossible to satisfy
multiple fairness notions simultaneously, creating a challenge in achieving balanced outcomes [24].
A key issue is the trade-off between fairness and accuracy. Incorporating fairness as an objective can
reduce accuracy, as the focus shifts from purely optimizing prediction accuracy to balancing it with
fairness concerns [34]. This creates a need for an established trade-off. Bias mitigation algorithms
are designed to balance the dual objectives of maintaining model accuracy and ensuring fairness.
These strategies can be applied at different stages of model development. Several methods for bias
mitigation have been examined, including the work of Calders and Verwer (2010) [36], Chouldechova
(2017) [37], Feldman et al. (2015) [38], Hardy et al. (2016) [39], Kamiran and Calders (2012) [40], Zafar
et al. (2017) [41], and Zhang et al. (2018) [42]. These approaches can generally be classified into three
categories: pre-processing, in-processing, and post-processing techniques.
Pre-Processing Mechanisms: Pre-processing techniques involve altering the input data to eliminate
bias before training the classifier. This approach is useful when the algorithm is allowed to modify the
training data [29]. Strategies include removing sensitive attributes, adjusting the labels of instances
near decision boundaries (as these are more susceptible to discrimination), and applying reweighing
techniques to correct imbalances. Recent approaches suggest altering feature representations in a way
that reduces bias without altering the core model [35].
In-Processing Mechanisms: In-processing techniques aim to modify the learning algorithm itself to
reduce discrimination during model training, while keeping the original training data unchanged [29].
This can be achieved by introducing a regularization term to the objective function, which penalizes
the mutual information between sensitive attributes and predicted outcomes. Alternatively, constraints
can be added to ensure that the model satisfies fairness metrics like equalized odds or reduces disparate
impact [28, 35].
Post-Processing Mechanisms: Post-processing methods adjust the predictions of a trained model
to meet fairness criteria, without modifying the model or the training data [29]. These approaches
are useful when the algorithm can only manipulate the learned model. For instance, some methods
adjust the labels predicted by the black-box model using a fairness-driven function. Various studies
propose techniques that improve equalized odds or equal opportunity by modifying the outcomes after
training [34]. Additionally, it is often suggested to set different thresholds for different groups in a way
that both maximizes accuracy and minimizes demographic disparities [35, 43].
3. Study Methodology
In this section, we address the bias methodologies used to answer our study’s research aim. Our goal is to
represent an HR decision system as a graph architecture to empirically evaluate the fairness notions
of the predicted outcomes. Additionally, we explore the different standard mitigation techniques to
generate results with optimum fairness and accuracy.
In the HR systems, decision-making is inherently comparative, requiring the evaluation of multiple
candidates to identify the best fit. Given this complexity, Graph Neural Network (GNN) models are
well-suited for such tasks. GNNs excel in scenarios where features exhibit intricate relationships and
varying degrees of correlation, which significantly influence prediction outcomes. Indeed, our method
involved the evaluation of the effectiveness of three GNN architectures: Graph Convolutional Networks
(GCN), Graph Attention Networks (GAT) and Graph Isomorphism Networks (GIN).
Each GNN model underwent all three phases of bias mitigation: pre-processing techniques, in-
processing, and post-processing. Bias was mitigated using appropriate algorithms at each stage. The
model outcomes were evaluated against four key fairness metrics: statistical parity difference (SPD),
equal opportunity difference (EOD), overall accuracy equality difference (OAED), and treatment equality
difference (TED)1 . By applying these methods to a relevant dataset, we aim to address key experimental
questions - how different GNN architectures perform in reducing bias while maintaining predictive
accuracy, and what trade-offs arise between fairness and accuracy when various bias mitigation strate-
gies are applied. The findings are discussed in the next section and they disclose how various GNN
designs manage and influence such trade-off.
3.1. Data Collection and Pre-Processing
For this study, the Adult dataset [44] from the UCI repository has been used. It predicts whether a
person’s annual income exceeds $50,000. It is a widely recognised dataset containing around 40,000
instances and 16 attributes, plus a target variable (income), collected on the 1994 United States Census.
The sensitive attribute in this study was gender, and the target variable was annual income. The income
data was converted into binary values as follows:
𝑖𝑛𝑐𝑜𝑚𝑒 > 50𝐾 −→ 𝑖𝑛𝑐𝑜𝑚𝑒 = 1
𝑖𝑛𝑐𝑜𝑚𝑒 ≤ 50𝐾 −→ 𝑖𝑛𝑐𝑜𝑚𝑒 = 0
The data was sourced from the Center for Machine Learning and Intelligent Systems at the University
of California, Irvine, and is available as a comma-separated values CSV file.
Since GNNs require data to be in graph form, the K-Nearest Neighbors Graph (K-NNG) method was
employed to convert the dataset into a graph structure. K-NNG connects each entity with its 𝑘 most
1
Here the GIT repository with the source code: https://github.com/het28/Bias
similar neighbors based on a similarity metric. This technique was chosen due to the high density of
connections in the dataset, allowing the K-NNG to produce sparse graphs with fewer edges, thereby
improving computational efficiency compared to fully connected graphs [45].
3.2. Mitigation Mechanisms: A Comparative Analysis
As previously mentioned, bias mitigation can occur at various stages in model development. These
stages include pre-processing (modifying data before training), in-processing (adjusting the training
process), and post-processing (modifying outcomes post-training). Each mitigation strategy presents
distinct advantages and disadvantages, which are summarized in Table 4. In our experimental protocol
Mechanism Advantages Disadvantages
Pre-processed data can be used for any Mostly used for optimizing before train-
Pre-Processing downstream task.No need to modify classi- ing.May not be able to support all fair-
Mechanisms fier. No need to access sensitive attributes ness metrics (Statistical Parity or Individ-
at test time. ual Fairness) due to unavailability of label
𝑌 . Compared to the other two methods
does not perform well on accuracy and fair-
ness measures.
Good performance on accuracy and fair- Methods are task-specific. Do not general-
In-Processing ness measures. Higher flexibility to choose ize well across scenarios. Modification of
Mechanisms the trade-off between accuracy and fair- the classifier might not always be feasible.
ness measures (depends on specific algo-
rithm). No need to access sensitive at-
tributes at test time.
Highly adaptable as can be applied after Need to access protected attributes during
Post-Processing training the classifier. Results in relatively the testing phase. Lack the flexibility of
Mechanisms good performance on fairness measures. picking any accuracy-fairness trade-off.
No need to modify classifier, simplifying
implementation.
Table 4
Beside selecting the criterion to measure fairness, it is also need to choose the step in the workflow of a
machine learning process in which to apply bias mitigation algorithms. In the table fairness mechanisms are
classified conventionally into three categories pre-processing, in-processing and post-processing. Their respective
advantages and disadvantages are also outlined [35, 46].
we employed various algorithms to mitigate bias, each corresponding to a different mitigation phase as
detailed in Table 5.
Mitigation Mechanisms Algorithms
Pre-processing Reweighing
Prejudice Remover Regularizer
In-processing
Rich Subgroup Fairness
Post-processing Reject Option Classification
Table 5
Algorithms used for each mitigation phase in this study.
3.3. Mitigation Algorithms
Reweighing - Reweighing is a pre-processing technique designed to adjust the weights of instances in
the dataset to mitigate bias. It does so without relabeling the data. For example, features where sensitive
attribute 𝑎 is in the positive class receive higher weights than those in the negative class, and vice versa
for attribute 𝑏 [47]. By adjusting the weights, this method seeks to achieve fairness between protected
and unprotected groups [40]. It is the most ideal algorithm for skewed and imbalanced datasets, which is
one of the main causes of bias in HR domain. So, Reweighing was an obvious choice to readjust the repre-
sentation to balance the different groups in the data, increasing the learning opportunity for the models.
Prejudice Remover Regularizer (PRR) - Prejudice Remover Regularizer is an in-processing
technique that introduces a regularization term into the log-likelihood loss function of a classifier. This
term penalises discrimination based on sensitive attributes [48]. For HR decision systems, along with
fairness, accurate decisions are of primordial importance. Thus, this regularisation term is leveraged as
a hyperparameter, which is used to control the degree of penalisation, allowing the model to balance
accuracy with fairness [49].
Rich Subgroup Fairness (RSF) - Rich Subgroup Fairness aims to go beyond traditional fair-
ness metrics, which may only evaluate fairness across broad categories such as gender. These broader
metrics may overlook biases affecting specific subgroups, such as certain gender-ethnicity intersections.
RSF mitigates this by considering finer-grained intersections of various attributes, thereby identifying
and addressing biases against more specific protected subgroups. Using this algorithm, it is evaluated
if the prediction accuracy across all groups is equal, enforcing the matching representation of false
positives and false negatives across all groups [50].
Reject Option Classification (ROC) - Reject Option Classification is a post-processing method that
works by adjusting predictions in the low-confidence regions of a probabilistic classifier. This approach
reduces discrimination by selectively changing the classification of instances from both protected
and unprotected groups. The ROC algorithm uses a variety of parameters, including classification
thresholds and fairness metrics, to improve fairness [51]. The process involves swapping predictions
(e.g., changing false negatives to true positives) to minimise unfair treatment of different demographic
groups. It finds the best confidence bound by itself [47]. While it is effective at reducing bias, ROC
is computationally expensive due to the complexity of tuning multiple parameters. Moreover, the
algorithm can slightly reduce the accuracy of the unprotected group while increasing it for the
protected group [47].
4. Experimental Protocol and Discussion of Results
We organize our experimental runs as following:
1. As first step we evaluate GCN, GAT, and GIN neural networks on Adult dataset before applying
any mitigation technique.
2. Then we evaluate GCN, GAT, and GIN neural networks on Adult dataset after applying mitigation
techniques (Reweighing, Prejudice Remover Regularizer, Rich Subgroup Fairness, and Reject
Option Classifier).
3. Finally we compared the obtained results and we infer some consideration about their efficacy
considering four fairness metrics: Statistical Parity Difference (SPD), Equal Opportunity Difference
(EOD), Overall Accuracy Equality Difference (OAED), and Treatment Equality Difference (TED).
Baseline Performance (Pre-Mitigation) As it is possible to observe in Table 6, all three GNN
models showed high accuracy, with GCN achieving slightly better results (approx 85%) than GAT
and GIN (approx 84%). This aligns with existing literature that emphasizes GCN’s superior ability to
aggregate neighborhood information effectively, thus enhancing predictive accuracy [52, 53].
However, despite the strong overall accuracy, fairness metrics shown a different picture. GIN
exhibited the largest Equal Opportunity Difference (EOD), highlighting its significant bias in terms of
true positive rates across protected and unprotected groups. GCN showed a relatively high Treatment
Equality Difference (TED), indicating a bias in error rates between demographic groups. These
preliminary results reveal a critical insight: while GNNs perform well in terms of accuracy, they exhibit
inherent biases, thus necessitating bias mitigation techniques.
Model Test Accuracy (↑) SPD (↓) EOD (↓) OAED (↓) TED (↓)
GCN 0.8449 0.0098 0.0040 0.0184 0.0303
GAT 0.8293 0.0020 0.0028 0.0067 0.0064
GIN 0.8429 0.0079 0.0341 0.0349 0.0083
Table 6
Results before applying Mitigation Techniques. Upward facing arrow (↑) indicates that higher values are better,
whereas downward facing arrows (↓) indicate lower values are better.
Model Mitigation Technique Test Accuracy (↑) SPD (↓) EOD (↓) OAED (↓) TED (↓)
Reweighing 0.7850 0.0090 0.0212 0.0167 0.1010
Prejudice Remover Regularizer 0.8452 0.0160 0.0484 0.0312 0.0170
GCN
Rich Subgroup Fairness 0.8451 0.0128 0.0334 0.0472 0.0177
Reject Option Classification 0.8826 0.0012 0.0081 0.0301 0.0134
Reweighing 0.7020 0.0044 0.0081 0.0055 0.0259
Prejudice Remover Regularizer 0.8285 0.0066 0.0052 0.0023 0.0146
GAT
Rich Subgroup Fairness 0.8277 0.0050 0.0062 0.0011 0.0096
Reject Option Classification 0.8833 0.0011 0.0071 0.0098 0.0044
Reweighing 0.7797 0.0011 0.0343 0.0471 0.1100
Prejudice Remover Regularizer 0.8457 0.0023 0.0405 0.0505 0.0095
GIN
Rich Subgroup Fairness 0.8428 0.0016 0.0082 0.0134 0.0087
Reject Option Classification 0.8968 0.0148 0.0409 0.0348 0.0271
Table 7
Results after applying Mitigation Techniques. Upward facing arrow (↑) indicates that higher values are better,
whereas downward facing arrows (↓) indicate lower values are better.
(a) (b)
(c) (d)
Figure 2: Graph with Left y-axis: fairness evaluation for different models and different techniques. Right y-axis:
accuracy of the models. Statistical Parity (2a), Equal Opportunity (2b), Overall Accuracy Equality (2c) and
Treatment Equality (2d).
The effectiveness of the bias mitigation strategies, measured in terms of their ability to address
fairness concerns without excessively compromising accuracy, is summarized in Table 7 and Figures 2a
and 2d. The following paragraphs analyze each technique’s impact.
Reweighing This pre-processing method, demonstrated notable improvements in fairness metrics
but at the cost of a drop in predictive accuracy. Focusing on the GCN architecture, the model’s accuracy
fell from approx 85% to 78.50%. This reduction is expected, as reweighing tends to penalize the majority
class to promote fairness, reducing overall accuracy. On the contrary, SPD seems to be improved fom
0.0098 to 0.0090, reflecting a more balanced distribution of outcomes across groups, and OAED also
decreased from 0.0184 to 0.0167, suggesting reduced disparities in identifying positive cases. However,
as shown in Figure 2c, Treatment Equality Difference (TED) increased to 0.1010, suggesting a trade-off.
This is a typical side-effect of reweighing, where fairness in positive outcomes can lead to greater
disparities in misclassification rates, particularly in error rates across demographic groups. Similar
consideration can be made for GAT, although in this case SPD registered a higher value after applying
the mitigation technique. Finally, for GIN, only SPD showed a decrease from the raw model suggesting
that Reweighing might not be the best technique to use with this type of GNN. Overall this result
underscores the well-documented tension between fairness and accuracy: while reweighing can improve
outcome parity, it does so at the expense of treatment equality and overall accuracy.
Prejudice Remover Regularizer (PRR) Prejudice Remover Regularizer, an in-processing tech-
nique, showed small variations in accuracy with two models (GCN and GAT) actually performing better,
demonstrating its ability to maintain predictive performance. However, every model registered an
improvement in only one technique each. For instance, GCN successfully managed to reduce TED
(from 0.03 to 0.017) but failed at satisfying the other metrics. The same holds for GAT and GIN which
were able to improve the value of OAED and SPD respectively but didn’t succeed at enhancing the
rest of the metrics. Although PRR did reduce some of the bias present in the model’s predictions, its
performance, with respect to the four metrics, strongly depended on the type of GNN used. Moreover,
it was not effective in addressing outcome disparities measured by EOD.
Rich Subgroup Fairness (RSF) Rich Subgroup Fairness leads to different impact on model per-
formance and fairness metrics due to its specific approach for handling subgroup disparities. The test
accuracy of 84.51 % is nearly identical to the original GCN model’s accuracy, which is 84.49 %,same
pattern is observed for GAT and GIN maintaining the accuracy at 82.77 % and 84.28 % respectively. It
can be observed that SPD increases slightly to 0.0128 for GCN and 0.0050 for GAT, indicating a minor
increase in bias regarding the distribution of favorable outcomes across genders. We can observe that
RSF aims to be fair across subgroups but not fully able to eliminate all the biases for the GCN and
GAT model. On the other hand, RSF worked the better when applied to GIN model when compared
with GCN and GAT, since we observed decrease in value for SPD, EOD and OAED, while TED remains
almost the same suggesting that RSF has maintained overall predictive performance while addressing
fairness.
Reject Option Classification (ROC) The Reject Option Classification emerged as one of the
most effective post-processing technique in improving accuracy to almost 89% for all three models.
SPD dropped to 0.0012 and 0.0011 for GCN and GAT respectively, indicating near-perfect balance in the
distribution of favorable outcomes between groups. TED also improved, decreasing to 0.0134 for GCN
and 0.0044 for GAT, hence reflecting more balanced error rates across groups.
Also, OAED was successfully improved for GIN while ROC failed at mitigating bias arising from
differences in the true positive rates for protected and unprotected groups.
5. Conclusion
The results presented in this section confirm the hypothesis posed in our research question: bias
mitigation techniques do help reduce bias in GNN architectures, but the trade-off between fairness and
accuracy is inevitable. Each technique exhibited distinct strengths and weaknesses, depending on the
GNN model it was applied to.
The GAT architecture, combined with the ROC algorithm, produces the best results, offering an
optimized balance between accuracy and fairness. This outcome is expected, as it involves prediction
swapping and numerous parameters, which also makes it computationally intensive.
While not the top performer, PRR and RSF consistently maintain accuracy across all GNN models,
achieving an effective trade-off, particularly when used with GAT.
Among the fairness metrics, Treatment Equality showed the most improvement following the appli-
cation of mitigation techniques, promoting equal error rates across all groups.
PRR was the least effective in enhancing fairness metrics, indicating that a standard approach like this,
which adjusts representation, struggles to improve fairness in complex real-world data.
These findings suggest that no single mitigation technique universally outperforms the others in all
fairness metrics, and that careful consideration must be given when selecting the appropriate technique
based on the specific fairness requirements and constraints of the task at hand.
Future Directions The growing body of research in fairness and bias mitigation within machine
learning underscores the importance of continued investigation, especially as AI systems increasingly
influence social and organizational decision-making. Future work should focus on:
• Exploring more complex and realistic datasets that encompass multiple sensitive attributes,
offering a richer and more representative testing environment.
• Expanding the use of alternative GNN models, as variations in model architectures may
yield better performance in fairness optimization.
• Improving model transparency and interpretability, which will be crucial for building trust
in AI-driven HR systems and ensuring these systems are accountable for their decisions.
Such advancements will enable more refined bias mitigation techniques and foster collaboration between
researchers and practitioners to create fairer, more equitable machine learning systems for real-world
applications.
Acknowledgments
This research is partially funded by PNRR project FAIR - Future AI Research (PE00000013), Spoke 6 -
Symbiotic AI (CUP H97G22000210007) under the NRRP MUR program funded by the NextGenerationEU.
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