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Research papers on Algorithmic bias and fairness

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  1. Beyond bias and discrimination: redefining the AI ethics principle of fairness in healthcare machine-learning algorithms

    Benedetta Giovanola, Simona Tiribelli · 2022 · AI & Society · 191 citations

    The increasing implementation of and reliance on machine-learning (ML) algorithms to perform tasks, deliver services and make decisions in health and healthcare have made the need for fairness in ML, and more specifically in healthcare ML algorithms (HMLA), a very important and urgent task. However, while the debate on fairness in the ethics of artificial intelligence (AI) and in HMLA has grown significantly over the last decade, the very concept of fairness as an ethical value has not yet been sufficiently explored. Our paper aims to fill this gap and address the AI ethics principle of fairness from a conceptual standpoint, drawing insights from accounts of fairness elaborated in moral phil

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  2. Algorithmic fairness and bias mitigation for clinical machine learning with deep reinforcement learning

    Jenny Yang, Andrew A. S. Soltan, David W. Eyre, et al. · 2023 · Nature Machine Intelligence · 141 citations

    As models based on machine learning continue to be developed for healthcare applications, greater effort is needed to ensure that these technologies do not reflect or exacerbate any unwanted or discriminatory biases that may be present in the data. Here we introduce a reinforcement learning framework capable of mitigating biases that may have been acquired during data collection. In particular, we evaluated our model for the task of rapidly predicting COVID-19 for patients presenting to hospital emergency departments and aimed to mitigate any site (hospital)-specific and ethnicity-based biases present in the data. Using a specialized reward function and training procedure, we show that our m

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  3. Bias, Fairness and Accountability with Artificial Intelligence and Machine Learning Algorithms

    Nengfeng Zhou, Zach Zhang, Vijayan N. Nair, et al. · 2022 · International Statistical Review · 57 citations

    Summary The advent of artificial intelligence (AI) and machine learning algorithms has led to opportunities as well as challenges in their use. In this overview paper, we begin with a discussion of bias and fairness issues that arise with the use of AI techniques, with a focus on supervised machine learning algorithms. We then describe the types and sources of data bias and discuss the nature of algorithmic unfairness. In addition, we provide a review of fairness metrics in the literature, discuss their limitations, and describe de‐biasing (or mitigation) techniques in the model life cycle.

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

    Damien A. Dablain, Bartosz Krawczyk, Nitesh V. Chawla · 2024 · Discover Data · 28 citations

    Abstract 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 gro

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  5. Addressing Bias in Machine Learning Algorithms: Promoting Fairness and Ethical Design

    Dharmesh Dhabliya, Sukhvinder Singh Dari, Anishkumar Dhablia, et al. · 2024 · E3S Web of Conferences · 23 citations

    Machine learning algorithms have quickly risen to the top of several fields' decision-making processes in recent years. However, it is simple for these algorithms to confirm already present prejudices in data, leading to biassed and unfair choices. In this work, we examine bias in machine learning in great detail and offer strategies for promoting fair and moral algorithm design. The paper then emphasises the value of fairnessaware machine learning algorithms, which aim to lessen bias by including fairness constraints into the training and evaluation procedures. Reweighting, adversarial training, and resampling are a few strategies that could be used to overcome prejudice. Machine learning s

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  6. Algorithmic Fairness and Bias in Machine Learning Systems

    Rushil Chandra, Karun Sanjaya, AR Aravind, et al. · 2023 · E3S Web of Conferences · 6 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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  7. Exploring Bias and Fairness in Machine Learning Algorithms

    Thota Venkat Narayana Rao, M. Stephen, E. Manoj, et al. · 2025 · Advances in computational intelligence and robotics book series · 2 citations

    When developing and implementing machine learning algorithms, bias and fairness are essential factors to consider. Systematic mistakes or inconsistencies in the data or the algorithmic decision-making process are the root cause of bias in machine learning algorithms. In algorithmic systems, ensuring fairness is crucial to preventing harm and advancing equity and justice. In order to assess the fairness of machine learning algorithms, several measures and criteria have been put forth, such as differential impact, equal opportunity, and demographic parity. This study reviews how these algorithms work and their applications in real-world scenarios such as healthcare, hiring and recruitment, fin

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  8. AI for Robustness and Fairness: Addressing bias, fairness and robustness in machine learning algorithms

    Gaurav Kashyap · 2025 · Journal of Artificial Intelligence Machine Learning and Data Science · 1 citations

    Although artificial intelligence (AI) and machine learning (ML) have revolutionized a number of industries, their application in vital fields like healthcare, finance and criminal justice has sparked questions about robustness, bias and fairness.The significance of addressing these issues in AI and ML systems is examined in this paper.We look at how bias occurs, different ways to make things more equitable and ways to make machine learning algorithms more resilient.The study suggests frameworks for incorporating robustness and fairness into AI systems while guaranteeing social impact and ethical considerations.We offer a comprehensive overview of the opportunities and difficulties in develop

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  9. Bias Detection and Fairness Optimization in Machine Learning Algorithms

    Rachna · 2025 · International Journal of Artificial Intelligence, Computer Science, Management and Technology

    There are serious worries about prejudice and unfairness with the growing use of machine learning algorithms in decision-making systems. This is especially true in politically charged areas like healthcare, banking, hiring, and law enforcement. Machine learning models can be biased due to historical or imbalanced datasets, poor feature selection, or algorithmic design decisions. This bias can result in discriminating outcomes, which can worsen socioeconomic disparities. examines current methods for detecting bias and evaluating fairness, as well as the kinds and origins of bias in machine learning algorithms. Methods for reducing bias in models without sacrificing performance during pre-, in

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

    Tooba Fatima, Kashifa Khanam, Abhishek Kumar 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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  11. 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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  13. 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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  14. 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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  17. 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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  19. 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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