=Paper=
{{Paper
|id=Vol-3741/paper22
|storemode=property
|title=Mitigating Unfairness in Machine Learning: A Taxonomy and an Evaluation Pipeline
|pdfUrl=https://ceur-ws.org/Vol-3741/paper22.pdf
|volume=Vol-3741
|authors=Chiara Criscuolo,Tommaso Dolci,Mattia Salnitri
|dblpUrl=https://dblp.org/rec/conf/sebd/CriscuoloDS24
}}
==Mitigating Unfairness in Machine Learning: A Taxonomy and an Evaluation Pipeline==
https://ceur-ws.org/Vol-3741/paper22.pdf
Mitigating Unfairness in Machine Learning: A
Taxonomy and an Evaluation Pipeline
Discussion Paper
Chiara Criscuolo1,* , Tommaso Dolci1 and Mattia Salnitri1
1
Politecnico di Milano – Department of Electronics, Information and Bioengineering
Abstract
Big data poses challenges in maintaining ethical standards for reliable outcomes in machine learning.
Data that inaccurately represent populations may result in biased algorithmic models, whose application
leads to unfair decisions in delicate fields such as medicine and industry. To address this issue, many
fairness mitigation techniques have been introduced, but the proliferation of overlapping methods
complicates decision-making for data scientists. This paper proposes a taxonomy to organize these
techniques and a pipeline for their evaluation, supporting practitioners in selecting the most suitable ones.
The taxonomy classifies and describes techniques qualitatively, while the pipeline offers a quantitative
framework for evaluation and comparison. The proposed approach supports data scientists in addressing
biased models and data effectively.
Keywords
fairness, mitigation, machine learning, taxonomy, pipeline
1. Introduction
One of the challenges of big data is assuring high ethical standards to obtain reliable and high-
quality results when machine learning algorithms are employed. Data that do not correctly
represent the population sooner or later lead to biased machine learning models and wrong
outcomes, with possible severe impacts on people and society. For example, a biased model
for evidence-based medicine may lead to wrong diagnoses, or a biased model for the industry
might lead to wrong business decisions. In both cases, consequences will be drastic, potentially
leading to life-threatening situations in the former case and to business failures in the latter. To
address this problem, fairness mitigation techniques can be used, to either modify the analyzed
data or tune the model, with the goal of reducing or removing bias. Because of the importance
of the issue, mitigation techniques are proliferating in the literature, frequently generating
overlapping techniques that make the choice of the the data scientist extremely difficult.
Therefore, we propose a taxonomy to organize the fairness mitigation techniques that can be
used to mitigate bias. Such a taxonomy will help data scientists navigate through the different
mitigation techniques and easily identify the ones that can be applied to the specific context.
To further help the selection of mitigation techniques, this paper proposes a pipeline for their
SEBD 2024: 32nd Symposium on Advanced Database Systems, June 23-26, 2024, Villasimius, Sardinia, Italy
*
Corresponding author.
$ chiara.criscuolo@polimi.it (C. Criscuolo); tommaso.dolci@polimi.it (T. Dolci); mattia.salnitri@polimi.it
(M. Salnitri)
© 2024 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR
ceur-ws.org
Workshop ISSN 1613-0073
Proceedings
evaluation, whose objective is to be used for the creation of a shared repository of evaluations
that can grow incrementally and become a reference knowledge-base.
The rest of the paper is structured as follows. Section 2 reports the theoretical foundations of
the research work, and Section 3 describes the state of the art on taxonomies for unfairness
mitigation techniques and pipelines for their evaluation. Section 4 introduces the proposed
taxonomy, with an example of a structured description focused on one unfairness mitigation
technique, while Section 5 illustrates the evaluation pipeline. Finally, Section 6 concludes the
paper and discusses future works.
2. Preliminaries
Fairness is “the absence of any prejudice or favoritism toward an individual or a group based
on their inherent or acquired characteristics” [1, p.100]. It is based on the idea of protected
or sensitive attribute. A protected attribute is a characteristic for which non-discrimination
should be established, such as religion, race, sex, and so on [2]. A protected group is a set of
individuals identified by the same value of a protected attribute (e.g., females, young people,
Hispanic people). Fairness can be measured with different metrics [2, 3, 4], each one expressing
a specific aspect of fairness. Fairness can be considered satisfied based on the values of the
aforementioned metrics.
The process to identify, measure and mitigate unfair behaviors of machine learning models
is composed of three steps [5]: (i) train a classification algorithm to predict the binary value
of the target class, that can be a positive or a negative outcome like not obtaining a loan or
having a low income; (ii) use fairness metrics to understand whether the prediction of this
model encompasses discrimination for the protected group; (iii) if the metrics results show
unfair behavior, we conclude that the model learned the bias from the dataset, and a fairness
mitigation technique is applied.
A fairness mitigation technique falls into one of the following three main categories [6, 7]:
• Pre-processing: tackle the fairness problem by removing the underlying discrimination
from the training dataset, before the model is trained;
• In-processing: modify state-of-art algorithms to address fairness during the learning
phase;
• Post-processing: act after the training phase has ended, by transforming the model
output to satisfy fairness.
In the rest of the paper, we adopt the following terminology:
• 𝐷: the original dataset composed by 𝑁 tuples;
• 𝑐: the trained algorithm classifier;
• 𝑔: the protected attribute;
• 𝑌 : the actual classification result, two possible values (or labels);
• 𝑌^ : the algorithm predicted decision;
• 𝐷′ , 𝑌^′ and 𝑐′ are the corresponding values after the application of a mitigation technique.
3. Related Work
In the recent years, numerous researchers have worked to provide a comprehensive overview
of unfairness mitigation techniques in computer science and machine learning.
In [8] the authors present the various types of bias with examples of real-world applica-
tions. They propose a taxonomy for fairness definitions from the literature to address bias
in machine-learning systems, categorizing them based on their downstream application (i.e.,
classification, regression, and other machine-learning methods). In [9] the authors give an
overview of the main concepts of fairness, considering identification, measurement, and im-
provement of fairness, focusing solely on classification tasks. They briefly present various
fairness-enhancing algorithms dividing them into pre-, in-, and post-processing techniques.
A similar approach is adopted in [7], with the difference that pre-, in-, and post-processing
algorithms are further sub-categorized into areas to provide a deeper insight. In [10], bias and
unfairness are analyzed in various domains, primarily focusing on data management approaches
to address unfairness. They cover the definitions of fairness metrics, how to identify unfair-
ness, and mitigation techniques. The author suggest a shift towards a data-centered approach,
by integrating bias constraints and curation methods into database management systems, to
discover and mitigate bias as early as possible. In [11], the authors provide an overview of
bias-mitigation research across various algorithmic systems, focusing on information retrieval,
human-computer interaction, recommender systems, and machine learning. Additionally, they
discuss the subjective nature of perceived fairness and the importance of explainability in
managing bias. The survey identifies two types of attributes affected by bias: those describing
the world and those describing information, suggesting different approaches for mitigation.
What emerges from this work is the need for an up-to-date and comprehensive taxonomy that
presents and describes unfairness mitigation techniques, highlighting, through specific selection
criteria, which ones are available off-the-shelf and readily usable. Our taxonomy addresses this
gap, focusing on the qualitative aspects necessary for understanding and choosing the right
technique to apply.
Finally, in [12] the authors present a data pre-processing pipeline to facilitate a direct compar-
ison of mitigation techniques: they suggest a standard dataset pre-processing method and study
the trade-off evaluation between fairness and accuracy. Our evaluation pipeline differs from
this approach in comparing, through fairness metrics, the impact of each mitigation strategy.
4. Taxonomy of Mitigation Techniques
The first part of this section presents the taxonomy structure, the selection criteria, and its
overall composition. The second part describes in detail the taxonomy elements. Finally, we
present a structured description of a specific mitigation technique included in the taxonomy.
4.1. Taxonomy Definition
We propose a taxonomy that classifies unfairness mitigation techniques based on the targeted
processing phase of machine learning models. Each technique is classified into pre-, in- and
post-processing classes. The usefulness of the proposed taxonomy is based on the amount of
mitigation techniques that are included. The taxonomy can be seen as the result of systematic
analyses of the state of the art, that can be cyclically performed by data scientists to include
more and more techniques. We, therefore, envision the taxonomy to be constantly updated
and extended. As a result, we designed it to be easily extensible, by following the classification
proposed and including the required information for the structured description of the technique.
The choice of the mitigation techniques that are included in the taxonomy is based on the
following selection criteria.
• Applicability: the mitigation technique should be usable off-the-shelf, with parametric
customization only. Many prototypical mitigation techniques are available, yet their
application is limited to specific cases, or they need heavy customization. Such an effort
is not feasible for data scientists in most scenarios.
• Tabular data: tabular data is the most used type of data, especially in data science
applications. The selected mitigation techniques are required to operate on tabular data.
In some cases (e.g., waveforms) unstructured data can be transformed into tabular data.
• Binary classification: the selected mitigation techniques are required to mitigate bias
for binary classification models. In particular, we restrict the scope to techniques that
apply to prescriptive models, and guarantee fair, unbiased predictions.
Each element of the taxonomy is a mitigation technique, that is detailed using a predefined
description structure that will guide the inclusion of new mitigation techniques, and ease the
analysis of the taxonomy and of the included techniques.
General Description. This is a general description of the mitigation technique, that is used to
provide the data scientists with the context of application of the technique.
Objectives. This field defines the objective and the scope of the mitigation technique. We
describe and summarize the main steps of each technique, to make the technique more under-
standable.
Input Required. This specifies the type of input required by the mitigation technique: some
techniques require specific information or pre-processing steps to make the dataset compliant
with the requirements of the mitigation technique, e.g., if the dataset contains only categorical
values. This field specifies the pre-processing actions possibly required by the technique.
Expected Output. The expected results are specified in this field. For instance, some techniques
return a modified version 𝐷′ of the input dataset, others an improved classifier 𝑐′ .
Parameters. This field contains the number and types of parameters that need be specified
for the mitigation techniques. A short description of each parameter is provided, including
procedures for the identification of the best parameter values and instructions on fine-tuning.
If the parameters are context-dependent, this is also reported.
Issues and Limitations. This field specifies the known limitations of the mitigation technique
and also possible issues experienced in its application. Possible issues relate to: (i) machine
learning support, i.e., any constraints with machine learning models, cross-validation or hold-
out support; (ii) required post-processing of the mitigation output to compare it with the original
dataset; (iii) the possibility of retrieving (or not) the modified dataset (if any).
Demonstration of Application. This provides a link to a working demonstration, if any. A
working demonstration is central to understand how the technique is applied and can operate.
Table 1
This table reports the taxonomy composed by 12 mitigation techniques classified into the three categories:
pre-processing, in-processing and post-processing. For each technique, we describe input, output,
corresponding reference, i.e., the original paper in which the technique was presented, and the tool that
implements the technique.
Category Technique Input Output Ref. Tool
′
Reweighing 𝐷, 𝑔, 𝑌 𝐷 ,𝑊 [13] AIF360
′
Disparate Impact Remover 𝐷, 𝑔, 𝑌 𝐷 [14] AIF360
Pre-proc.
Learning Fair Representation 𝐷, 𝑔, 𝑌 𝐷′ [15] AIF360
′
Optimized Pre-processing 𝐷, 𝑔, 𝑌 𝐷 [16] AIF360
′
Correlation Remover 𝐷, 𝑔, 𝑌 𝐷 [17] FL
Adversarial Debiasing 𝐷, 𝑔, 𝑌 𝑐′ [18] AIF360, FL
′
In-proc. Prejudice Remover 𝐷, 𝑔, 𝑌 𝑐 [19] AIF360
′
Reductions Exponential Gradient 𝐷, 𝑔, 𝑌 𝑐 [20] FL, AIF360
Calibrated Equalized Post-Processing 𝐷, 𝑔, 𝑌, 𝑌^ 𝑌^′ [21] AIF360
Post-proc. Reject Option Classification 𝐷, 𝑔, 𝑌, 𝑌^ 𝑌^′ [22] AIF360
Equalized Odds Post-Processing 𝐷, 𝑔, 𝑌, 𝑌^ 𝑌^′ [21, 23] FL, AIF360
Threshold Optimizer 𝐷, 𝑔, 𝑌, 𝑌^ 𝑌^′ [23] FL
Source Code. This specifies the link to the source code, if available.
Documentation. This specifies the link to the official documentation or any other useful
documentation of the technique.
4.2. An Overview of the Proposed Taxonomy
Table 1 shows a general overview of the taxonomy, considering some mitigation techniques,
presenting: the name of each technique, its category, the required input, and the expected output.
The selected techniques satisfy all the criteria mentioned at the beginning of Section 4.1, i.e., the
possibility of applying this mitigation technique to tabular data in a binary classification task.
The two additional columns Ref. and Tool report the original paper in which the mitigation
technique was first proposed, and which tool provides a library/source code available to use
the specific mitigation technique. Two main tools are considered: AIF360 [24], the acronym for
AI Fairness 360 tool implemented by IBM, and FL standing for FairLearn [17], implemented by
Microsoft. Other state-of-the-art tools were discarded because they offered only methodologies
for measuring fairness and not for its mitigation, such as Aequitas tool [3], Google What-if
tool [25].
The following part reports, as an example, the structured description of Reweighing [13], a
mitigation technique included in the taxonomy described in Table 1. This technique fits the
selection criteria of the presented taxonomy: (i) it is applicable and off-the-shelf, (ii) it handles
tabular data, and (iii) it can be used to mitigate unfairness also for binary classification tasks.
General Description The intuition behind this strategy is that new weights are assigned to
data objects (tuples) to make the dataset discrimination-free. [13, p.14].
Objectives The goal of Reweighing is to assign a weight to the tuple in the dataset, in a way
′
that the new dataset 𝐷 is bias-free, i.e., 𝑔 the protected attribute and 𝑌 the target variable, are
statistically independent [13]. More specifically, the proposed equation is:
𝑃𝑒𝑥𝑝 (𝑔 = 𝑋(𝑔) ∧ 𝑌 = 𝑋(𝑌 ))
𝑊 (𝑋) = ,
𝑃𝑜𝑏𝑠 (𝑔 = 𝑋(𝑔) ∧ 𝑌 = 𝑋(𝑌 ))
where 𝑔 is a protected attribute, 𝑌 is the target variable and 𝑋 is a random unlabelled data
object. If the expected probability is higher than the observed probability value, it shows the bias
toward class, thus the technique assigns lower weights to individuals who have been deprived
or favored. If we multiply the frequency of every tuple by its weight, the new dataset will be
bias-free.
Required Input The required input is composed by the original dataset 𝐷, the protected
attribute 𝑔, and the target class 𝑌 . There are no further requirements for the input.
Output Expected The expected output is composed of a mitigated dataset 𝐷′ and a weight
vector 𝑊 .
Parameters This technique has no parameters to set.
Issues and Limitations The main issue of this technique is the output: the mitigated dataset
𝐷′ is equal to the original one 𝐷, but every tuple will have a different weight based on the
protected attribute and the classification label. To apply the technique, the researcher should
use the weight vector 𝑊 . This vector should be used in the training and testing phases of the
new model, in order to apply 𝑊 to 𝐷′ to obtain a mitigated solution.
Demonstration of Application A demonstration of Reweighing can be found here: https:
//github.com/Trusted-AI/AIF360/blob/master/examples/demo_reweighing_preproc.ipynb.
Source Code The source code of Reweighing is available here: https://github.com/Trusted-AI/
AIF360/blob/main/aif360/algorithms/preprocessing/reweighing.py.
Documentation The documentation of Reweighing is available here: https://aif360.readthedocs.
io/en/stable/modules/generated/aif360.algorithms.preprocessing.Reweighing.html.
5. Evaluation Pipeline
This section presents an innovative approach to systematically test and compare various fairness
mitigation techniques from the literature. The proposed solution is designed to work with
any tabular dataset. Currently, research work of this kind focuses on a few standard datasets
from the literature (e.g., Adult Census Income, German Credit Risk Score, Compas Recidivism
Score), and the considered fairness measures are typically limited. For example, many papers
exclusively employ mitigation techniques implemented by AIF360, evaluating the results using
only the predefined fairness metrics provided by the tool. The pipeline proposed in this paper
allows to perform an evaluation using a larger set of metrics, each one derived from a different
fairness definition, not bounded to the specific technique under analysis.
Input
Taxonomy
Dataset
Evaluation Pipeline
data,
protected attribute, fairness metrics mitigation fairness metrics
target class technique
Model Training Initial After Mitigation
Mitigation
and Testing Measurement Measurement
metrics predictions after
predictions metrics values
values mitigation
Comparison
Figure 1: Proposed evaluation pipeline for testing and comparing fairness mitigation techniques.
Our pipeline stands out by considering a wide range of the most used fairness metrics,
distinguishing this approach from available off-the-shelf tools. Similarly, we encompass all
the mitigation techniques described in our taxonomy, not confining the evaluation to those
offered by a single system. The overall goal is to fill a gap in the literature by providing a
systematic evaluation of these techniques. This contribution is in addition to the systematic
screening provided by the taxonomy, which is a key first step in better understanding unfairness
mitigation techniques.
Figure 1 illustrates the proposed pipeline to quantitatively assess and compare the effective-
ness of the mitigation techniques outlined in the taxonomy. Our pipeline, inspired by, and
enriched from [5], starts with the input dataset 𝐷, in which a protected attribute 𝑔 is identified.
We assume the dataset to be pre-processed by the researcher, to ensure data quality.
The initial stage is Model Training and Testing, where a classification model 𝑐 is trained on an
input dataset 𝐷 with target class 𝑌 . During testing, the model’s predictions 𝑌^ are computed.
𝐷, 𝑌 and 𝑌^ represent the input to the Initial Measurement step to assess fairness. During
this step, the identified fairness metrics are evaluated and analyzed. If any unfair behavior is
detected, the Mitigation step is executed. Fairness mitigation techniques, selected on the basis
of the taxonomy specified in Section 4, are applied. The result is a new set of predictions 𝑌^′ for
each technique applied. Finally, a second measurement step (After Mitigation Measurement) is
executed to compute the new values for each fairness metric.
An analysis of the fairness metrics values before and after the mitigation step allows to
compare the outcomes of the mitigation techniques. The analysis can be accompanied by the
generation of plots and graphs. These visualizations provide researchers with a comprehensive
understanding of the impact of each mitigation technique on the input dataset.
6. Conclusions and Future Work
This paper proposed an approach to help data scientists for the selection of the most suitable
unfairness mitigation techniques when biased datasets are employed. In particular, we proposed
two contributions: a taxonomy and an evaluation pipeline. The taxonomy allows the classifica-
tion and qualitative structured description of mitigation techniques. The evaluation pipeline
allows the quantitative evaluation and comparison of the effectiveness of the techniques. This
paper lays the basis for a more extensive method for the evaluation of fairness mitigation
techniques.
Future work consists in considering how the techniques’ effectiveness is impacted by the
type of data used and the type of machine learning model applied. The consideration of
these variables is key for a deeper understanding of the efficacy of the fairness mitigation
techniques. Additionally, the selection of the fairness metrics used in Initial Measurement and
After Mitigation Measurement is central for a sound analysis. Unfortunately, off-the-shelf tools
provide a limited, and heterogeneous, amount of metrics. To overcome this limit, we intend to
implement a comprehensive set of metrics based on different fairness definitions.
Finally, the pipeline presented in this paper defines a general approach, and therefore can
be extended and further detailed in each of the presented steps. For instance, the Mitigation
step is a complex process that varies according to the mitigation technique applied, while the
Measurement steps require a thorough analysis of each fairness metric. The Comparison step
is critical since it requires a complex analysis by the data scientists. As a future work, all the
mentioned steps can be improved to provide the scientists with: (i) support for the execution
of the mitigation techniques, (ii) guidelines for the selection of the fairness metrics, and (iii)
guidelines for the comparison of mitigation results. Overall, the evaluation pipeline can be
automated for a faster and more effective measurement.
The contribution of this paper allows for an informed choice of fairness mitigation techniques.
It will guide data scientists on the selection process obviating the existing theoretical and
methodological gaps that, until today, lead to unacceptable errors and inaccuracies of machine
learning models caused by biased datasets.
Acknowledgments
Thanks to professor Letizia Tanca for her contribution: her precious comments and feedback
have significantly improved this research work.
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