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Research papers on Algorithmic bias and fairness

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  1. Towards a holistic view of bias in machine learning: bridging algorithmic fairness and imbalanced learning

    Damien Dablain, Bartosz Krawczyk, Nitesh Chawla · 2024 · Discover Data · 20 citations

    Machine learning (ML) is playing an increasingly important role in rendering decisions that affect a broad range of groups in society. This posits the requirement of algorithmic fairness, which holds that automated decisions should be equitable with respect to protected features (e.g., gender, race). Training datasets can contain both class imbalance and protected feature bias. We postulate that, to be effective, both class and protected feature bias should be reduced—which allows for an increase in model accuracy and fairness. Our method, Fair OverSampling (FOS), uses SMOTE (Chawla in J Artif Intell Res 16:321–357, 2002) to reduce class imbalance and feature blurring to enhance group fairne

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  2. Algorithmic Fairness and Bias in Machine Learning Systems

    Rushil Chandra, Karun Sanjaya, AR Aravind, et al. · 2023 · E3S Web of Conferences · 3 citations

    In recent years, research into and concern over algorithmic fairness and bias in machine learning systems has grown significantly. It is vital to make sure that these systems are fair, impartial, and do not support discrimination or social injustices since machine learning algorithms are becoming more and more prevalent in decision-making processes across a variety of disciplines. This abstract gives a general explanation of the idea of algorithmic fairness, the difficulties posed by bias in machine learning systems, and different solutions to these problems. Algorithmic bias and fairness in machine learning systems are crucial issues in this regard that demand the attention of academics, pr

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  4. A Fairness-Aware Machine Learning Framework for Sexual and Reproductive Health: Evaluating Algorithmic Bias Across Models

    Efosa Osagie, Shemi Ayo-Ogbor, Rebecca Balasundaram · 2026 · Journal of Data Science and Intelligent Systems

    Advances in computational infrastructure and the widespread adoption of electronic health record (EHR) systems have accelerated the integration of artificial intelligence (AI) and machine learning (ML) into sexual and reproductive health (SRH) services. These technologies enhance diagnostic accuracy, support clinical decision-making, and enable predictive analytics using diverse healthcare data. However, biases within training datasets can produce unfair outcomes, particularly for underrepresented groups. This study proposes a fairness-aware ML framework designed to detect and mitigate algorithmic bias in SRH services. The framework is evaluated using two open-source datasets: a large SRH da

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  5. Algorithmic Bias and Fairness in Machine Learning Systems: A Review

    Tooba Fatima, Kashifa Khanam, Abhishek Jaiswal, et al. · 2026 · DMPedia Lecture Notes in Multidisciplinary Research

    Machine learning (ML) systems are increasingly deployed in sensitive domains such as healthcare, finance, and criminal justice. Despite their benefits, these systems often exhibit algorithmic bias, raising concerns about fairness, accountability, and trust. Bias may emerge from historical inequalities in training data, model design, or deployment practices, resulting in disparate impacts on marginalized groups. Over the years, researchers have proposed multiple fairness definitions and mitigation strategies, ranging from data preprocessing and in-processing adversarial learning to post-processing adjustments. Toolkits like AI Fairness 360 and Fairlearn support practical implementation, while

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  6. ISLAMIC ETHICS OF ALGORITHMIC BIAS: A FRAMEWORK FOR FAIRNESS IN MACHINE LEARNING APPLICATIONS

    Rustiyana Rustiyana, Fatimah Al Rashid, Fatima Malik · 2025 · International Jornal of Noesantara Islamic Studies

    The expanding use of machine learning systems in socially consequential domains has intensified concerns about algorithmic bias, particularly in contexts where ethical legitimacy must align with religious and cultural values. The absence of frameworks that integrate computational fairness with Islamic moral philosophy presents a critical gap in current AI ethics discourse. This study aims to construct an Islamic ethical framework for evaluating and mitigating algorithmic bias by systematically mapping core Islamic principles—justice, harm prevention, accountability, and human dignity—onto established fairness methodologies in machine learning. A qualitative conceptual design was employed, ut

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  7. Algorithmic Bias and Fairness in Machine Learning: Two Sides of the Same Coin?

    Merugu Bhuvana Naga Priya, Godavarthi Srujana, Angara Navya Sri Alekhya, et al. · 2022 · Journal of Soft Computing Paradigm

    The importance of counting for fairness has increased significantly in the design and engineering of those systems because of the rapid rise and widespread use of Artificial Intelligence (AI) systems and its applications in our daily lives. It is crucial to guarantee that the opinions formed by AI systems do not represent discrimination against particular groups or populations because these systems have the potential to be employed in a variety of sensitive contexts to form significant and life-changing judgments. Recent advances in traditional machine learning and deep learning have addressed these issues in a variety of subfields. Scientists are striving to overcome the biases that these p

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  10. 0503 Estimating Algorithmic Fairness and Bias in Predicting Sleeping Difficulties Using Artificial Intelligence and Machine Learning

    Sonal Yadav, Yanjia Zhang, Zhenwei Zhang, et al. · 2025 · SLEEP

    Introduction: Sleeping difficulties are associated with several adverse outcomes such as cardiovascular disorders, metabolic syndrome, and higher levels of road traffic accidents. Better predictive abilities could help in early detection and improved control over these adverse outcomes. However, identifying and accounting for algorithmic biases in predictive models across different ethnic groups remains a challenge. We used advanced machine learning techniques and fairness metrics to investigate for potential disparities in predicting sleeping difficulties between different racial and ethnic groups. Methods: We analyzed a cohort of participants from NHANES with sleeping difficulties between

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  12. Detecting and Mitigating Algorithmic Bias in AI-Based Student Performance Prediction Systems: A Fairness-Aware Deep Learning Approach

    Sheetal Shevkari, Sharon Manmothe · 2026 · Vidhyayana

    The rapid integration of Artificial Intelligence (AI) and deep learning in higher education has enabled automated student performance prediction systems; however, these systems frequently inherit and amplify biases present in historical educational data, leading to inequitable outcomes for students across gender, socioeconomic, geographic, and linguistic dimensions. Existing machine learning approaches for academic performance prediction predominantly optimize for accuracy, largely disregarding fairness constraints, which risks systematically disadvantaging already marginalized learner groups. This paper proposes FairEduNet, a fairness-aware deep learning framework that integrates adversaria

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