Uncovering Bias - Exploring Machine Learning Techniques for Detecting and Mitigating Bias in Data - A Literature Review

Aug 3, 2026

Bias in machine learning models remains a critical challenge affecting fairness and equity in decision-making. This literature review analyzes how to uncover bias in data and how to detect and mitigate it using established techniques. It examines pre-processing methods (e.g., reweighting, relabeling, feature engineering, sampling), post-pre-processing approaches (bias detection and output modification), and fairness constraints such as demographic parity, equalized odds, and predictive parity. Effectiveness, limitations, and trade-offs between fairness and accuracy are evaluated through a structured pipeline spanning data preparation, model training, and fairness-focused evaluation.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages6

Reading Time15 min

AccessFree to download

Introduction

Background and motivation

Review objectives

Methodology

Data preparation and bias analysis

Uncovering Bias: Exploring Machine Learning Techniques for Detecting and Mitigating Bias in Data – A Literature Review Arun Padmanabhan1, Dr. K. Devasenapathy2 1Department of Computer Science Karpagam Academy of Higher Education Coimbatore, India [arun1986.p@gmail.com](arun1986.p@gmail.com) 2Department of Computer Science Karpagam Academy of Higher Education Coimbatore, India [drdevasenapathy.k@kahedu.edu.in](drdevasenapathy.k@kahedu.edu.in) Abstract— The presence of Bias in models developed using machine learning algorithms has emerged as a critical issue. This literature review explores the topic of uncovering the existence of bias in data and the application of techniques for detecting and mitigating Bias. Thereview provides a comprehensive analysis of the existing literature, focusing on pre-processing techniques, post-pre-processing techniques, and fairness constraints employed to uncover and address the existence of Bias in machine learning models. The effectiveness, limitations, and tradeoffs of these techniques are examined, highlighting their impact on advocating fairness and equity in decision-making processes. The methodology consists of two key steps: data preparation and bias analysis, followed by machine learning model development and evaluation. In the data preparation phase, the dataset is analyzed for biases and pre-processed using techniques like reweighting or relabeling to reduce bias. In the model development phase, suitable algorithms are selected, and fairness metrics are defined and optimized during the training process. The models are then evaluated using performance and fairness measures and the best-performing model is chosen. The methodology ensures a systematic exploration of machine learning techniques to detect and mitigate bias, leading to more equitable decision-making. The review begins by examining the techniques of pre-processing, which involve cleaning the data, selecting the features, feature engineering, and sampling. These techniques play an important role in preparing the data to reduce bias and promote fairness in machine learning models. The analysis highlights various studies that have explored the effectiveness of these techniques in uncovering and mitigating bias in data, contributing to the development of more equitable and unbiased machine learning models. Next, the review delves into post-preprocessing techniques that focus on detecting and mitigating bias after the initial data preparation steps. These techniques include bias detection methods that assess the disparate impact or disparate treatment in model predictions, as well as bias mitigation techniques that modify model outputs to achieve fairness across different groups. The evaluation of these techniques, their performance metrics, and potential trade-offs between fairness and accuracy are discussed, providing insights into the challenges and advancements in bias mitigation. Lastly, the review examines fairness constraints, which involve the imposition of rules or guidelines on machine learning algorithms to ensure fairness in predictions or decision-making processes. The analysis explores different fairness constraints, such as demographic parity, equalized odds, and predictive parity, and their effectiveness in reducing bias and advocating fairness in machine learning models. Overall, this literature review provides a comprehensive understanding of the techniques employed to uncover and mitigate the existence of bias in machine learning models. By examining pre-processing techniques, post-pre-processing techniques, and fairness constraints, the review contributes to the development of more fair and unbiased machine learning models, fostering equity and ethical decision-making in various domains. By examining relevant studies, this review provides insights into the effectiveness and limitations of various pre-processing techniques for bias detection and mitigation via Pre-processing, Adve

FAQs

What is the main purpose of this literature review?

It provides an in-depth analysis of machine-learning techniques for uncovering and mitigating bias in data. It focuses on pre-processing, post-pre-processing, and fairness constraints and evaluates their effectiveness and trade-offs.

How does the review propose detecting bias before model training?

It emphasizes data preparation and bias analysis, including identifying bias-relevant variables and applying pre-processing methods such as reweighting or relabeling, along with feature selection, feature engineering, and sampling.

What fairness strategies are discussed to reduce bias after prediction?

The review covers post-pre-processing techniques that assess disparate impact or disparate treatment and modify outputs to achieve fairness across groups. It also discusses fairness constraints like demographic parity, equalized odds, and predictive parity during the algorithmic decision process.

Aug 3, 2026

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