Computer Science · Literature

Research papers on Algorithmic bias and fairness

Recent and highly-cited academic work on algorithmic bias and fairness, gathered from Semantic Scholar, CrossRef and OpenAlex.

1. Bias and Unfairness in Machine Learning Models: A Systematic Review on Datasets, Tools, Fairness Metrics, and Identification and Mitigation Methods

   T. P. Pagano, R. B. Loureiro, F. V. Lisboa, et al. · 2023 · Big Data Cogn. Comput. · 292 citations

   One of the difficulties of artificial intelligence is to ensure that model decisions are fair and free of bias. In research, datasets, metrics, techniques, and tools are applied to detect and mitigate algorithmic unfairness and bias. This study examines the current knowledge on bias and unfairness in machine learning models. The systematic review followed the PRISMA guidelines and is registered on OSF plataform. The search was carried out between 2021 and early 2022 in the Scopus, IEEE Xplore, Web of Science, and Google Scholar knowledge bases and found 128 articles published between 2017 and 2022, of which 45 were chosen based on search string optimization and inclusion and exclusion criter

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

   Jenny Yang, A. Soltan, D. Eyre, et al. · 2023 · Nature Machine Intelligence · 109 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

3. The Butterfly Effect in artificial intelligence systems: Implications for AI bias and fairness

   Emilio Ferrara · 2024 · Machine Learning with Applications · 100 citations

4. Algorithmic bias, data ethics, and governance: Ensuring fairness, transparency and compliance in AI-powered business analytics applications

   Julien Kiesse Bahangulu, Louis Owusu-Berko · 2025 · World Journal of Advanced Research and Reviews · 72 citations

   The widespread adoption of AI-powered business analytics applications has revolutionized decision-making, yet it has also introduced significant challenges related to algorithmic bias, data ethics, and governance. As organizations increasingly rely on machine learning and big data analytics for customer profiling, credit scoring, hiring decisions, and predictive analytics, concerns about fairness, transparency, and compliance have intensified. Algorithmic biases—often stemming from biased training data, flawed model assumptions, and insufficient diversity in datasets—can result in discriminatory outcomes, reinforcing societal inequalities and reputational risks for businesses. To address the

5. Data augmentation for fairness-aware machine learning: Preventing algorithmic bias in law enforcement systems

   Ioannis Pastaltzidis, N. Dimitriou, K. Quezada-Tavárez, et al. · 2022 · Proceedings of the 2022 ACM Conference on Fairness, Accountability, and Transparency · 48 citations

   Researchers and practitioners in the fairness community have highlighted the ethical and legal challenges of using biased datasets in data-driven systems, with algorithmic bias being a major concern. Despite the rapidly growing body of literature on fairness in algorithmic decision-making, there remains a paucity of fairness scholarship on machine learning algorithms for the real-time detection of crime. This contribution presents an approach for fairness-aware machine learning to mitigate the algorithmic bias / discrimination issues posed by the reliance on biased data when building law enforcement technology. Our analysis is based on RWF-2000, which has served as the basis for violent acti

6. Should Fairness be a Metric or a Model? A Model-based Framework for Assessing Bias in Machine Learning Pipelines

   John P. Lalor, Ahmed Abbasi, Kezia Oketch, et al. · 2024 · ACM Transactions on Information Systems · 41 citations

   Fairness measurement is crucial for assessing algorithmic bias in various types of machine learning (ML) models, including ones used for search relevance, recommendation, personalization, talent analytics, and natural language processing. However, the fairness measurement paradigm is currently dominated by fairness metrics that examine disparities in allocation and/or prediction error as univariate key performance indicators (KPIs) for a protected attribute or group. Although important and effective in assessing ML bias in certain contexts such as recidivism, existing metrics don’t work well in many real-world applications of ML characterized by imperfect models applied to an array of instan

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

   Nengfeng Zhou, Zach Zhang, V. Nair, et al. · 2022 · International Statistical Review · 39 citations

   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.

8. Mitigating machine learning bias between high income and low–middle income countries for enhanced model fairness and generalizability

   Jenny Yang, Lei A. Clifton, N. Dung, et al. · 2024 · Scientific Reports · 37 citations

   Collaborative efforts in artificial intelligence (AI) are increasingly common between high-income countries (HICs) and low- to middle-income countries (LMICs). Given the resource limitations often encountered by LMICs, collaboration becomes crucial for pooling resources, expertise, and knowledge. Despite the apparent advantages, ensuring the fairness and equity of these collaborative models is essential, especially considering the distinct differences between LMIC and HIC hospitals. In this study, we show that collaborative AI approaches can lead to divergent performance outcomes across HIC and LMIC settings, particularly in the presence of data imbalances. Through a real-world COVID-19 scre

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

   Damien Dablain, B. Krawczyk, N. Chawla · 2022 · Discover Data · 36 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

10. Addressing Algorithmic Bias in AI‐Driven HRM Systems: Implications for Strategic HRM Effectiveness

    Ruwan Bandara, Kumar Biswas, Shahriar Akter, et al. · 2025 · Human Resource Management Journal · 32 citations

    AI and machine learning algorithms are revolutionising the modern workplace by transforming HR functions to deliver superior outcomes for both employees and organisations. However, research shows that these algorithms often fail to deliver optimal HR solutions, primarily due to inherent biases. Developing capabilities to overcome algorithmic biases is critical for firms, as these biases present significant challenges to fairness and inclusivity in HR decision‐making, ultimately impacting the effectiveness of HR practices. To address this challenge, our study, grounded in the dynamic capability perspective, presents a model to address algorithmic biases in people management and achieve superi

11. Exploring Bias and Prediction Metrics to Characterise the Fairness of Machine Learning for Equity-Centered Public Health Decision-Making: A Narrative Review

    Shaina Raza, Arash Shaban-Nejad, Elham Dolatabadi, et al. · 2024 · IEEE Access · 22 citations

    The rapid advancement of Machine Learning (ML) represents novel opportunities to enhance public health research, surveillance, and decision-making. However, there is a lack of comprehensive understanding of algorithmic bias — systematic errors in predicted population health outcomes — resulting from the public health application of ML. The objective of this narrative review is to explore the types of bias generated by ML and quantitative metrics to assess these biases. We performed search on PubMed, MEDLINE, IEEE (Institute of Electrical and Electronics Engineers), ACM (Association for Computing Machinery) Digital Library, Science Direct, and Springer Nature. We used keywords to identify stu

12. Algorithmic Fairness and Bias Mitigation for Clinical Machine Learning: Insights from Rapid COVID-19 Diagnosis by Adversarial Learning

    Jenny Yang, A. Soltan, Yang Yang, et al. · 2022 · 13 citations

    Machine learning is becoming increasingly promi- nent in healthcare. Although its benefits are clear, growing attention is being given to how machine learning may exacerbate existing biases and disparities. In this study, we introduce an adversarial training framework that is capable of mitigating biases that may have been acquired through data collection or magnified during model development. For example, if one class is over-presented or errors/inconsistencies in practice are reflected in the training data, then a model can be biased by these. To evaluate our adversarial training framework, we used the statistical definition of equalized odds. We evaluated our model for the task of rapidly

13. Using Pareto simulated annealing to address algorithmic bias in machine learning

    William Blanzeisky, Padraig Cunningham · 2021 · The Knowledge Engineering Review · 10 citations

    Abstract Algorithmic bias arises in machine learning when models that may have reasonable overall accuracy are biased in favor of ‘good’ outcomes for one side of a sensitive category, for example gender or race. The bias will manifest as an underestimation of good outcomes for the under-represented minority. In a sense, we should not be surprised that a model might be biased when it has not been ‘asked’ not to be; reasonable accuracy can be achieved by ignoring the under-represented minority. A common strategy to address this issue is to include fairness as a component in the learning objective. In this paper, we consider including fairness as an additional criterion in model training and pr

14. Algorithmic Fairness and Bias Mitigation for Clinical Machine Learning: A New Utility for Deep Reinforcement Learning

    Jenny Yang, A. Soltan, David A. Clifton · 2022 · 7 citations

    As machine learning-based models continue to be developed for healthcare applications, greater effort is needed in ensuring that these technologies do not reflect or exacerbate any unwanted or discriminatory biases that may be present in the data. In this study, 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-specific (hospital) and ethnicity-based biases present in the data. Using a specialized reward function and training procedure, we show

15. Evaluating the impact of data biases on algorithmic fairness and clinical utility of machine learning models for prolonged opioid use prediction

    Behzad Naderalvojoud, Catherine M. Curtin, Steven M. Asch, et al. · 2025 · JAMIA Open · 7 citations

    Abstract Objectives The growing use of machine learning (ML) in healthcare raises concerns about how data biases affect real-world model performance. While existing frameworks evaluate algorithmic fairness, they often overlook the impact of bias on generalizability and clinical utility, which are critical for safe deployment. Building on prior methods, this study extends bias analysis to include clinical utility, addressing a key gap between fairness evaluation and decision-making. Materials and Methods We applied a 3-phase evaluation to a previously developed model predicting prolonged opioid use (POU), validated on Veterans Health Administration (VHA) data. The analysis included internal a

16. The Dark Side of Machine Learning Algorithms: How and Why They Can Leverage Bias, and What Can Be Done to Pursue Algorithmic Fairness

    Mariya I. Vasileva · 2020 · Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining · 6 citations

    Machine learning and access to big data are revolutionizing the way many industries operate, providing analytics and automation to many aspects of real-world practical tasks that were previously thought to be necessarily manual. With the pervasiveness of artificial intelligence and machine learning over the past decade, and their epidemic spread in a variety of applications, algorithmic fairness has become a prominent open research problem. For instance, machine learning is used in courts to assess the probability that a defendant recommits a crime; in the medical domain to assist with diagnosis or predict predisposition to certain diseases; in social welfare systems; and autonomous vehicles

17. Framework for Fairness in Machine Learning Using Detecting and Mitigating Bias in AI Algorithms

    Vincent Koc · 2025 · 2025 3rd International Conference on Business Analytics for Technology and Security (ICBATS) · 5 citations

    The importance of artificial intelligence (AI) and machine learning (ML) is on the rise as they are increasingly being used in critical decision-making in areas including healthcare, finance, and criminal justice. Their effectiveness, however, is often compromised as these systems replicate and even worsen pre-existing biases from historical data or from the designs of models, ultimately causing unfair and undesirable results. This paper describes a systematic analysis for bias detection and mitigation in machine learning algorithms. The approach includes preprocessing of data, algorithmic changes, and changes during post-processing for improvement of anticipated inequalities while keeping t

18. Fairness in focus: quantitative insights into bias within machine learning risk evaluations and established credit models

    Jacob Ford · 2025 · Management System Engineering · 4 citations

    As the adoption of machine learning algorithms expands across industries, the focus on how these tools can perpetuate existing biases have gained attention. Given the expanding literature in a nascent field, an example of how leading bias indicators could be aggregated and deployed to evaluate the fairness of a machine learning tool would prove useful for future data scientists. This research addresses how algorithmic bias may be quantified by conducting a case study of a machine learning alternative credit risk metric, comparing threshold effects for various protected classes contrasted with a conventional credit score (FICO). Our research pursues two objectives: (1) to scrutinize the exten

19. Public Perceptions of Algorithmic Bias and Fairness in Cloud-Based Decision Systems

    Amal Alhosban, Ritik Gaire, H. Al-Ababneh · 2025 · Standards · 4 citations

    Cloud-based machine learning systems are increasingly used in sectors such as healthcare, finance, and public services, where they influence decisions with significant social consequences. While these technologies offer scalability and efficiency, they raise significant concerns regarding security, privacy, and compliance. One of the central issues is algorithmic bias, which can emerge from data, design choices, or system interactions, and is often amplified when deployed at scale through cloud infrastructures. This study examines the relationship between algorithmic bias, social equity, and cloud-based innovation. Drawing on a survey of public perceptions, we find strong recognition of the

20. Algorithmic Fairness and Bias in Machine Learning Systems

    Rohan Chandra, Karun Sanjaya, A. 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

21. Machine Learning and Public Health: Identifying and Mitigating Algorithmic Bias through a Systematic Review

    S. Altamirano, Arjan Vreeken, S. Ghebreab · 2025 · 2 citations

    Machine learning (ML) promises to revolutionize public health through improved surveillance, risk stratification, and resource allocation. However, without systematic attention to algorithmic bias, ML may inadvertently reinforce existing health disparities. We present a systematic literature review of algorithmic bias identification, discussion, and reporting in Dutch public health ML research from 2021 to 2025. To this end, we developed the Risk of Algorithmic Bias Assessment Tool (RABAT) by integrating elements from established frameworks (Cochrane Risk of Bias, PROBAST, Microsoft Responsible AI checklist) and applied it to 35 peer-reviewed studies. Our analysis reveals pervasive gaps: alt

22. 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

23. 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

24. 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