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Papers on “algorithmic bias fairness machine learning”

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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 cites

   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 cites

   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 cites

   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 · 29 cites

   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 cites

   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 cites

   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 cites

   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 cite

   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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