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Papers on “algorithmic bias fairness machine learning”

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

   T. Pagano, R. Loureiro, F. V. Lisboa, et al. · 2023 · Big Data Cogn. Comput. · 300 cites

   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 · 116 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

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

   Emilio Ferrara · 2024 · Machine Learning with Applications · 107 cites

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 · 77 cites

   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, Nikolaos Dimitriou, K. Quezada-Tavárez, et al. · 2022 · Proceedings of the 2022 ACM Conference on Fairness, Accountability, and Transparency · 49 cites

   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 · 42 cites

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

   Damien Dablain, B. Krawczyk, N. Chawla · 2022 · Discover Data · 39 cites

   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

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

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

   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.