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Research papers on Algorithmic bias and fairness

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  1. Greedy function approximation: A gradient boosting machine.

    Jerome H. Friedman · 2001 · The Annals of Statistics · 30,137 citations

    Function estimation/approximation is viewed from the perspective of numerical optimization in function space, rather than parameter space. A connection is made between stagewise additive expansions and steepest-descent minimization. A general gradient descent “boosting” paradigm is developed for additive expansions based on any fitting criterion.Specific algorithms are presented for least-squares, least absolute deviation, and Huber-M loss functions for regression, and multiclass logistic likelihood for classification. Special enhancements are derived for the particular case where the individual additive components are regression trees, and tools for interpreting such “TreeBoost” models are

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  2. Deep Reinforcement Learning with Double Q-Learning

    Hado van Hasselt, Arthur Guez, David Silver · 2016 · AAAI Publications (The Association for the Advancement of Artificial Intelligence (AAAI)) · 6,271 citations

    The popular Q-learning algorithm is known to overestimate action values under certain conditions. It was not previously known whether, in practice, such overestimations are common, whether they harm performance, and whether they can generally be prevented. In this paper, we answer all these questions affirmatively. In particular, we first show that the recent DQN algorithm, which combines Q-learning with a deep neural network, suffers from substantial overestimations in some games in the Atari 2600 domain. We then show that the idea behind the Double Q-learning algorithm, which was introduced in a tabular setting, can be generalized to work with large-scale function approximation. We propose

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  3. Advances and Open Problems in Federated Learning

    Peter Kairouz, H. Brendan McMahan, Brendan Avent, et al. · 2020 · Foundations and Trends® in Machine Learning · 5,383 citations

    Federated learning (FL) is a machine learning setting where many clients (e.g., mobile devices or whole organizations) collaboratively train a model under the orchestration of a central server (e.g., service provider), while keeping the training data decentralized. FL embodies the principles of focused data collection and minimization, and can mitigate many of the systemic privacy risks and costs resulting from traditional, centralized machine learning and data science approaches. Motivated by the explosive growth in FL research, this monograph discusses recent advances and presents an extensive collection of open problems and challenges.

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  4. Towards A Rigorous Science of Interpretable Machine Learning

    Finale Doshi‐Velez, Been Kim · 2017 · arXiv (Cornell University) · 3,190 citations

    As machine learning systems become ubiquitous, there has been a surge of interest in interpretable machine learning: systems that provide explanation for their outputs. These explanations are often used to qualitatively assess other criteria such as safety or non-discrimination. However, despite the interest in interpretability, there is very little consensus on what interpretable machine learning is and how it should be measured. In this position paper, we first define interpretability and describe when interpretability is needed (and when it is not). Next, we suggest a taxonomy for rigorous evaluation and expose open questions towards a more rigorous science of interpretable machine learni

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  6. AI Fairness 360: An extensible toolkit for detecting and mitigating algorithmic bias

    Rachel Bellamy, Kuntal Dey, Michael Hind, et al. · 2019 · IBM Journal of Research and Development · 865 citations

    Fairness is an increasingly important concern as machine learning models are used to support decision making in high-stakes applications such as mortgage lending, hiring, and prison sentencing. This article introduces a new open-source Python toolkit for algorithmic fairness, AI Fairness 360 (AIF360), released under an Apache v2.0 license (https://github.com/ibm/aif360). The main objectives of this toolkit are to help facilitate the transition of fairness research algorithms for use in an industrial setting and to provide a common framework for fairness researchers to share and evaluate algorithms. The package includes a comprehensive set of fairness metrics for datasets and models, explanat

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  7. Algorithmic Bias in Education

    Ryan S. Baker, Aaron Hawn · 2021 · International Journal of Artificial Intelligence in Education · 789 citations

    In this paper, we review algorithmic bias in education, discussing the causes of that bias and reviewing the empirical literature on the specific ways that algorithmic bias is known to have manifested in education. While other recent work has reviewed mathematical definitions of fairness and expanded algorithmic approaches to reducing bias, our review focuses instead on solidifying the current understanding of the concrete impacts of algorithmic bias in education—which groups are known to be impacted and which stages and agents in the development and deployment of educational algorithms are implicated. We discuss theoretical and formal perspectives on algorithmic bias, connect those perspect

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  9. A Review on Fairness in Machine Learning

    Dana Pessach, Erez Shmueli · 2022 · ACM Computing Surveys · 576 citations

    An increasing number of decisions regarding the daily lives of human beings are being controlled by artificial intelligence and machine learning (ML) algorithms in spheres ranging from healthcare, transportation, and education to college admissions, recruitment, provision of loans, and many more realms. Since they now touch on many aspects of our lives, it is crucial to develop ML algorithms that are not only accurate but also objective and fair. Recent studies have shown that algorithmic decision making may be inherently prone to unfairness, even when there is no intention for it. This article presents an overview of the main concepts of identifying, measuring, and improving algorithmic fai

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

    Benedetta Giovanola, Simona Tiribelli · 2022 · AI & Society · 185 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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  11. 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 · 131 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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  13. 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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  14. Towards a holistic view of bias in machine learning: bridging algorithmic fairness and imbalanced learning

    Damien Dablain, Bartosz Krawczyk, Nitesh Chawla · 2024 · Discover Data · 17 citations

    AbstractMachine 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 grou

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  15. 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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  17. 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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  18. 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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  19. 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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  20. 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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  23. 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

    Abstract 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.

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  24. 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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