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Journal articles on the topic 'Fairness-Accuracy trade-Off'

Author: Grafiati

Published: 8 June 2024

Last updated: 31 July 2025

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1

Jang, Taeuk, Pengyi Shi, and Xiaoqian Wang. "Group-Aware Threshold Adaptation for Fair Classification." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 6 (2022): 6988–95. http://dx.doi.org/10.1609/aaai.v36i6.20657.

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The fairness in machine learning is getting increasing attention, as its applications in different fields continue to expand and diversify. To mitigate the discriminated model behaviors between different demographic groups, we introduce a novel post-processing method to optimize over multiple fairness constraints through group-aware threshold adaptation. We propose to learn adaptive classification thresholds for each demographic group by optimizing the confusion matrix estimated from the probability distribution of a classification model output. As we only need an estimated probability distrib

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Plecko, Drago, and Elias Bareinboim. "Fairness-Accuracy Trade-Offs: A Causal Perspective." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 25 (2025): 26344–53. https://doi.org/10.1609/aaai.v39i25.34833.

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With the widespread adoption of AI systems, many of the decisions once made by humans are now delegated to automated systems. Recent works in the literature demonstrate that these automated systems, when used in socially sensitive domains, may exhibit discriminatory behavior based on sensitive characteristics such as gender, sex, religion, or race. In light of this, various notions of fairness and methods to quantify discrimination have been proposed, also leading to the development of numerous approaches for constructing fair predictors. At the same time, imposing fairness constraints may dec

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Langenberg, Anna, Shih-Chi Ma, Tatiana Ermakova, and Benjamin Fabian. "Formal Group Fairness and Accuracy in Automated Decision Making." Mathematics 11, no. 8 (2023): 1771. http://dx.doi.org/10.3390/math11081771.

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Most research on fairness in Machine Learning assumes the relationship between fairness and accuracy to be a trade-off, with an increase in fairness leading to an unavoidable loss of accuracy. In this study, several approaches for fair Machine Learning are studied to experimentally analyze the relationship between accuracy and group fairness. The results indicated that group fairness and accuracy may even benefit each other, which emphasizes the importance of selecting appropriate measures for performance evaluation. This work provides a foundation for further studies on the adequate objective

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Gupta, Soumyajit, Venelin Kovatchev, Anubrata Das, Maria De-Arteaga, and Matthew Lease. "Finding Pareto trade-offs in fair and accurate detection of toxic speech." Information Research an international electronic journal 30, iConf (2025): 123–41. https://doi.org/10.47989/ir30iconf47572.

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Introduction. Optimizing NLP models for fairness poses many challenges. Lack of differentiable fairness measures prevents gradient-based loss training or requires surrogate losses that diverge from the true metric of interest. In addition, competing objectives (e.g., accuracy vs. fairness) often require making trade-offs based on stakeholder preferences, but stakeholders may not know their preferences before seeing system performance under different trade-off settings. Method. We formulate the GAP loss, a differentiable version of a fairness measure, Accuracy Parity, to provide balanced accura

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Tae, Ki Hyun, Hantian Zhang, Jaeyoung Park, Kexin Rong, and Steven Euijong Whang. "Falcon: Fair Active Learning Using Multi-Armed Bandits." Proceedings of the VLDB Endowment 17, no. 5 (2024): 952–65. http://dx.doi.org/10.14778/3641204.3641207.

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Biased data can lead to unfair machine learning models, highlighting the importance of embedding fairness at the beginning of data analysis, particularly during dataset curation and labeling. In response, we propose Falcon, a scalable fair active learning framework. Falcon adopts a data-centric approach that improves machine learning model fairness via strategic sample selection. Given a user-specified group fairness measure, Falcon identifies samples from "target groups" (e.g., (attribute=female, label=positive)) that are the most informative for improving fairness. However, a challenge arise

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Badar, Maryam, Sandipan Sikdar, Wolfgang Nejdl, and Marco Fisichella. "FairTrade: Achieving Pareto-Optimal Trade-Offs between Balanced Accuracy and Fairness in Federated Learning." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 10 (2024): 10962–70. http://dx.doi.org/10.1609/aaai.v38i10.28971.

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As Federated Learning (FL) gains prominence in distributed machine learning applications, achieving fairness without compromising predictive performance becomes paramount. The data being gathered from distributed clients in an FL environment often leads to class imbalance. In such scenarios, balanced accuracy rather than accuracy is the true representation of model performance. However, most state-of-the-art fair FL methods report accuracy as the measure of performance, which can lead to misguided interpretations of the model's effectiveness to mitigate discrimination. To the best of our knowl

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Li, Xuran, Peng Wu, and Jing Su. "Accurate Fairness: Improving Individual Fairness without Trading Accuracy." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 12 (2023): 14312–20. http://dx.doi.org/10.1609/aaai.v37i12.26674.

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Accuracy and individual fairness are both crucial for trustworthy machine learning, but these two aspects are often incompatible with each other so that enhancing one aspect may sacrifice the other inevitably with side effects of true bias or false fairness. We propose in this paper a new fairness criterion, accurate fairness, to align individual fairness with accuracy. Informally, it requires the treatments of an individual and the individual's similar counterparts to conform to a uniform target, i.e., the ground truth of the individual. We prove that accurate fairness also implies typical gr

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Silvia, Chiappa, Jiang Ray, Stepleton Tom, Pacchiano Aldo, Jiang Heinrich, and Aslanides John. "A General Approach to Fairness with Optimal Transport." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (2020): 3633–40. http://dx.doi.org/10.1609/aaai.v34i04.5771.

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We propose a general approach to fairness based on transporting distributions corresponding to different sensitive attributes to a common distribution. We use optimal transport theory to derive target distributions and methods that allow us to achieve fairness with minimal changes to the unfair model. Our approach is applicable to both classification and regression problems, can enforce different notions of fairness, and enable us to achieve a Pareto-optimal trade-off between accuracy and fairness. We demonstrate that it outperforms previous approaches in several benchmark fairness datasets.

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Pinzón, Carlos, Catuscia Palamidessi, Pablo Piantanida, and Frank Valencia. "On the Impossibility of Non-trivial Accuracy in Presence of Fairness Constraints." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 7 (2022): 7993–8000. http://dx.doi.org/10.1609/aaai.v36i7.20770.

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One of the main concerns about fairness in machine learning (ML) is that, in order to achieve it, one may have to trade off some accuracy. To overcome this issue, Hardt et al. proposed the notion of equality of opportunity (EO), which is compatible with maximal accuracy when the target label is deterministic with respect to the input features. In the probabilistic case, however, the issue is more complicated: It has been shown that under differential privacy constraints, there are data sources for which EO can only be achieved at the total detriment of accuracy, in the sense that a classifier

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Pasupuleti, Murali Krishna. "AI-Based Credit Scoring Models: Balancing Accuracy and Fairness." International Journal of Academic and Industrial Research Innovations(IJAIRI) 05, no. 05 (2025): 631–40. https://doi.org/10.62311/nesx/rphcr23.

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Abstract: The integration of artificial intelligence (AI) into credit scoring has transformed traditional risk assessment methodologies by enabling the analysis of complex, multidimensional data. While these models demonstrate superior predictive performance, concerns persist regarding fairness and potential bias against underrepresented demographic groups. This study investigates the trade-off between predictive accuracy and algorithmic fairness in AI-based credit scoring systems. A comparative evaluation of logistic regression, random forest, and XGBoost models was conducted using a publicly

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Singh, Arashdeep, Jashandeep Singh, Ariba Khan, and Amar Gupta. "Developing a Novel Fair-Loan Classifier through a Multi-Sensitive Debiasing Pipeline: DualFair." Machine Learning and Knowledge Extraction 4, no. 1 (2022): 240–53. http://dx.doi.org/10.3390/make4010011.

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Machine learning (ML) models are increasingly being used for high-stake applications that can greatly impact people’s lives. Sometimes, these models can be biased toward certain social groups on the basis of race, gender, or ethnicity. Many prior works have attempted to mitigate this “model discrimination” by updating the training data (pre-processing), altering the model learning process (in-processing), or manipulating the model output (post-processing). However, more work can be done in extending this situation to intersectional fairness, where we consider multiple sensitive parameters (e.g

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Gitiaux, Xavier, and Huzefa Rangwala. "Fair Representations by Compression." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 13 (2021): 11506–15. http://dx.doi.org/10.1609/aaai.v35i13.17370.

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Organizations that collect and sell data face increasing scrutiny for the discriminatory use of data. We propose a novel unsupervised approach to map data into a compressed binary representation independent of sensitive attributes. We show that in an information bottleneck framework, a parsimonious representation should filter out information related to sensitive attributes if they are provided directly to the decoder. Empirical results show that the method achieves state-of-the-art accuracy-fairness trade-off and that explicit control of the entropy of the representation bit stream allows the

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Gao, Shiqi, Xianxian Li, Zhenkui Shi, Peng Liu, and Chunpei Li. "Towards Fair and Decentralized Federated Learning System for Gradient Boosting Decision Trees." Security and Communication Networks 2022 (August 2, 2022): 1–18. http://dx.doi.org/10.1155/2022/4202084.

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At present, gradient boosting decision trees (GBDTs) has become a popular machine learning algorithm and has shined in many data mining competitions and real-world applications for its salient results on classification, ranking, prediction, etc. Federated learning which aims to mitigate privacy risks and costs, enables many entities to keep data locally and train a model collaboratively under an orchestration service. However, most of the existing systems often fail to make an excellent trade-off between accuracy and communication. In addition, they overlook an important aspect: fairness such

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Pan, Chenglu, Jiarong Xu, Yue Yu, et al. "Towards Fair Graph Federated Learning via Incentive Mechanisms." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 13 (2024): 14499–507. http://dx.doi.org/10.1609/aaai.v38i13.29365.

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Graph federated learning (FL) has emerged as a pivotal paradigm enabling multiple agents to collaboratively train a graph model while preserving local data privacy. Yet, current efforts overlook a key issue: agents are self-interested and would hesitant to share data without fair and satisfactory incentives. This paper is the first endeavor to address this issue by studying the incentive mechanism for graph federated learning. We identify a unique phenomenon in graph federated learning: the presence of agents posing potential harm to the federation and agents contributing with delays. This sta

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Sun, Ying, Fariborz Haghighat, and Benjamin C. M. Fung. "Trade-off between accuracy and fairness of data-driven building and indoor environment models: A comparative study of pre-processing methods." Energy 239 (January 2022): 122273. http://dx.doi.org/10.1016/j.energy.2021.122273.

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Sun, Ying, Fariborz Haghighat, and Benjamin C. M. Fung. "Trade-off between accuracy and fairness of data-driven building and indoor environment models: A comparative study of pre-processing methods." Energy 239 (January 2022): 122273. http://dx.doi.org/10.1016/j.energy.2021.122273.

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Li, Yanying, Xiuling Wang, Yue Ning, and Hui Wang. "FairLP: Towards Fair Link Prediction on Social Network Graphs." Proceedings of the International AAAI Conference on Web and Social Media 16 (May 31, 2022): 628–39. http://dx.doi.org/10.1609/icwsm.v16i1.19321.

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Link prediction has been widely applied in social network analysis. Despite its importance, link prediction algorithms can be biased by disfavoring the links between individuals in particular demographic groups. In this paper, we study one particular type of bias, namely, the bias in predicting inter-group links (i.e., links across different demographic groups). First, we formalize the definition of bias in link prediction by providing quantitative measurements of accuracy disparity, which measures the difference in prediction accuracy of inter-group and intra-group links. Second, we unveil th

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Zeng, Ziqian, Rashidul Islam, Kamrun Naher Keya, James Foulds, Yangqiu Song, and Shimei Pan. "Fair Representation Learning for Heterogeneous Information Networks." Proceedings of the International AAAI Conference on Web and Social Media 15 (May 22, 2021): 877–87. http://dx.doi.org/10.1609/icwsm.v15i1.18111.

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Recently, much attention has been paid to the societal impact of AI, especially concerns regarding its fairness. A growing body of research has identified unfair AI systems and proposed methods to debias them, yet many challenges remain. Representation learning methods for Heterogeneous Information Networks (HINs), fundamental building blocks used in complex network mining, have socially consequential applications such as automated career counseling, but there have been few attempts to ensure that it will not encode or amplify harmful biases, e.g. sexism in the job market. To address this gap,

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Miah, Haroon, Dimitrios Kollias, Giacinto Luca Pedone, Drew Provan, and Frederick Chen. "Can Machine Learning Assist in Diagnosis of Primary Immune Thrombocytopenia? A Feasibility Study." Diagnostics 14, no. 13 (2024): 1352. http://dx.doi.org/10.3390/diagnostics14131352.

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Primary Immune Thrombocytopenia (ITP) is a rare autoimmune disease characterised by the immune-mediated destruction of peripheral blood platelets in patients leading to low platelet counts and bleeding. The diagnosis and effective management of ITP are challenging because there is no established test to confirm the disease and no biomarker with which one can predict the response to treatment and outcome. In this work, we conduct a feasibility study to check if machine learning can be applied effectively for the diagnosis of ITP using routine blood tests and demographic data in a non-acute outp

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Amalia, Wildi, Asrianda, and Fajriana. "Implementation of a Hybrid Fuzzy SAW and Particle Swarm Optimization Algorithm for a Dynamic Laptop Recommendation System Based on User Preferences." INOVTEK Polbeng - Seri Informatika 10, no. 2 (2025): 1045–54. https://doi.org/10.35314/nqkpbg48.

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Recommendation systems often struggle to balance personalization with fairness, particularly in addressing the marginalization of minority brands caused by data and algorithmic biases. This study tackles that challenge by developing a dynamic laptop recommendation system tailored to user preferences, leveraging a hybrid algorithm that combines Fuzzy Simple Additive Weighting (Fuzzy SAW) and Particle Swarm Optimization (PSO). Fuzzy SAW is employed to manage uncertainties in subjective preferences such as budget and intended use, while PSO dynamically optimizes the weight of each criterion to en

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Trejo-Moncada, Denise M. "The eXplainable Artificial Intelligence Paradox in Law: Technological Limits and Legal Transparency." Journal of Artificial Intelligence and Computing Applications 2, no. 1 (2024): 19–27. https://doi.org/10.5281/zenodo.14692066.

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The integration of Artificial Intelligence (AI) into legal systems offers transformative potential, promising enhanced efficiency and predictive accuracy. However, this progress also brings to the spotlight the explainability paradox: the unavoidable trade-off between the accuracy of complex Machine Learning (ML) and Deep Learning (DL) models and their lack of transparency. This paradox challenges foundational legal principles such as fairness, due process, and the right to explanation. While eXplainable AI (XAI) techniques have emerged to address this issue, their post-hoc nature, limited fid

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Ali, Ali Mohammed Omar. "Explainability in AI: Interpretable Models for Data Science." International Journal for Research in Applied Science and Engineering Technology 13, no. 2 (2025): 766–71. https://doi.org/10.22214/ijraset.2025.66968.

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As artificial intelligence (AI) continues to drive advancements across various domains, the need for explainability in AI models has become increasingly critical. Many state-of-the-art machine learning models, particularly deep learning architectures, operate as "black boxes," making their decision-making processes difficult to interpret. Explainable AI (XAI) aims to enhance model transparency, ensuring that AI-driven decisions are understandable, trustworthy, and aligned with ethical and regulatory standards. This paper explores different approaches to AI interpretability, including intrinsic

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Li, Qin, Zhou, Cheng, Zhang, and Ai. "Intelligent Rapid Adaptive Offloading Algorithm for Computational Services in Dynamic Internet of Things System." Sensors 19, no. 15 (2019): 3423. http://dx.doi.org/10.3390/s19153423.

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As restricted resources have seriously limited the computational performance of massive Internet of things (IoT) devices, better processing capability is urgently required. As an innovative technology, multi-access edge computing can provide cloudlet capabilities by offloading computation-intensive services from devices to a nearby edge server. This paper proposes an intelligent rapid adaptive offloading (IRAO) algorithm for a dynamic IoT system to increase overall computational performance and simultaneously keep the fairness of multiple participants, which can achieve agile centralized contr

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HERBORDT, MARTIN C., and CHARLES C. WEEMS. "ENPASSANT: AN ENVIRONMENT FOR EVALUATING MASSIVELY PARALLEL ARRAY ARCHITECTURES FOR SPATIALLY MAPPED APPLICATIONS." International Journal of Pattern Recognition and Artificial Intelligence 09, no. 02 (1995): 175–200. http://dx.doi.org/10.1142/s0218001495000109.

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Although massively parallel arrays for spatially mapped applications have been proposed since the 1950s42 and built since the 1960s,12 there have been very few systematic empirical studies that cover more than a small fraction of the design space. The problems have included the lack of a test suite of non-trivial application codes; inadequate language support; the difficulties of balancing evaluation performance with flexibility; and balancing test suite portability with accuracy of evaluation. We describe an environment that addresses these problems. A realistic workload including a series of

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Kieslich, Kimon, Birte Keller, and Christopher Starke. "Artificial intelligence ethics by design. Evaluating public perception on the importance of ethical design principles of artificial intelligence." Big Data & Society 9, no. 1 (2022): 205395172210929. http://dx.doi.org/10.1177/20539517221092956.

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Despite the immense societal importance of ethically designing artificial intelligence, little research on the public perceptions of ethical artificial intelligence principles exists. This becomes even more striking when considering that ethical artificial intelligence development has the aim to be human-centric and of benefit for the whole society. In this study, we investigate how ethical principles (explainability, fairness, security, accountability, accuracy, privacy, and machine autonomy) are weighted in comparison to each other. This is especially important, since simultaneously consider

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Costa, Diogo, Miguel Costa, and Sandro Pinto. "Train Me If You Can: Decentralized Learning on the Deep Edge." Applied Sciences 12, no. 9 (2022): 4653. http://dx.doi.org/10.3390/app12094653.

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The end of Moore’s Law aligned with data privacy concerns is forcing machine learning (ML) to shift from the cloud to the deep edge. In the next-generation ML systems, the inference and part of the training process will perform at the edge, while the cloud stays responsible for major updates. This new computing paradigm, called federated learning (FL), alleviates the cloud and network infrastructure while increasing data privacy. Recent advances empowered the inference pass of quantized artificial neural networks (ANNs) on Arm Cortex-M and RISC-V microcontroller units (MCUs). Nevertheless, the

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Schwartz, Jessica M., Maureen George, Sarah Collins Rossetti, et al. "Factors Influencing Clinician Trust in Predictive Clinical Decision Support Systems for In-Hospital Deterioration: Qualitative Descriptive Study." JMIR Human Factors 9, no. 2 (2022): e33960. http://dx.doi.org/10.2196/33960.

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Background Clinician trust in machine learning–based clinical decision support systems (CDSSs) for predicting in-hospital deterioration (a type of predictive CDSS) is essential for adoption. Evidence shows that clinician trust in predictive CDSSs is influenced by perceived understandability and perceived accuracy. Objective The aim of this study was to explore the phenomenon of clinician trust in predictive CDSSs for in-hospital deterioration by confirming and characterizing factors known to influence trust (understandability and accuracy), uncovering and describing other influencing factors,

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d'Hondt, Jens E., Haojun Li, Fan Yang, Odysseas Papapetrou, and John Paparrizos. "A Structured Study of Multivariate Time-Series Distance Measures." Proceedings of the ACM on Management of Data 3, no. 3 (2025): 1–29. https://doi.org/10.1145/3725258.

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Distance measures are fundamental to time series analysis and have been extensively studied for decades. Until now, research efforts mainly focused on univariate time series, leaving multivariate cases largely under-explored. Furthermore, the existing experimental studies on multivariate distances have critical limitations: (a) focusing only on lock-step and elastic measures while ignoring categories such as sliding and kernel measures; (b) considering only one normalization technique; and (c) placing limited focus on statistical analysis of findings. Motivated by these shortcomings, we presen

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Li, Jingyang, and Guoqiang Li. "The Triangular Trade-off between Robustness, Accuracy and Fairness in Deep Neural Networks: A Survey." ACM Computing Surveys, February 12, 2024. http://dx.doi.org/10.1145/3645088.

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With the rapid development of deep learning, AI systems are being used more in complex and important domains and necessitates the simultaneous fulfillment of multiple constraints: accurate, robust, and fair. Accuracy measures how well a DNN can generalize to new data. Robustness demonstrates how well the network can withstand minor perturbations without changing the results. Fairness focuses on treating different groups equally. This survey provides an overview of the triangular trade-off among robustness, accuracy, and fairness in neural networks. This trade-off makes it difficult for AI syst

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Talbert, Douglas A., Katherine L. Phillips, Katherine E. Brown, and Steve Talbert. "Assessing and Addressing Model Trustworthiness Trade-offs in Trauma Triage." International Journal on Artificial Intelligence Tools 33, no. 03 (2024). http://dx.doi.org/10.1142/s0218213024600078.

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Trauma triage occurs in suboptimal environments for making consequential decisions. Published triage studies demonstrate the extremes of the complexity/accuracy trade-off, either studying simple models with poor accuracy or very complex models with accuracies nearing published goals. Using a Level I Trauma Center’s registry cases (n = 50 644), this study describes, uses, and derives observations from a methodology to more thoroughly examine this trade-off. This or similar methods can provide the insight needed for practitioners to balance understandability with accuracy. Additionally, this stu

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Chen, Zhenpeng, Jie M. Zhang, Federica Sarro, and Mark Harman. "A Comprehensive Empirical Study of Bias Mitigation Methods for Machine Learning Classifiers." ACM Transactions on Software Engineering and Methodology, February 9, 2023. http://dx.doi.org/10.1145/3583561.

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Software bias is an increasingly important operational concern for software engineers. We present a large-scale, comprehensive empirical study of 17 representative bias mitigation methods for Machine Learning (ML) classifiers, evaluated with 11 ML performance metrics (e.g., accuracy), 4 fairness metrics, and 20 types of fairness-performance trade-off assessment, applied to 8 widely-adopted software decision tasks. The empirical coverage is much more comprehensive, covering the largest numbers of bias mitigation methods, evaluation metrics, and fairness-performance trade-off measures compared t

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Buijsman, Stefan. "Navigating fairness measures and trade-offs." AI and Ethics, July 17, 2023. http://dx.doi.org/10.1007/s43681-023-00318-0.

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AbstractTo monitor and prevent bias in AI systems, we can use a wide range of (statistical) fairness measures. However, it is mathematically impossible to optimize all of these measures at the same time. In addition, optimizing a fairness measure often greatly reduces the accuracy of the system (Kozodoi et al., Eur J Oper Res 297:1083–1094, 2022). As a result, we need a substantive theory that informs us how to make these decisions and for what reasons. I show that by using Rawls’ notion of justice as fairness, we can create a basis for navigating fairness measures and the accuracy trade-off.

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Fermanian, Jean-David, Dominique Guégan, and Xuwen Liu. "Fair Learning by Model Averaging (Revised Version)." Risk and Decision Analysis, March 2, 2025. https://doi.org/10.1177/15697371251321734.

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The central question of this paper is how to simply enhance a set of given supervised learning algorithms under some fairness requirements, to ensure that any sensitive variable does not “unfairly” influence the outcome. To achieve this goal, we work with several notions of fairness (Demographic Parity, Equalized Odds, Lack of Disparate Mistreatment), possibly generalised to more general concepts of conditional fairness. We linearly combine an ensemble of binary and/or continuous basis classifiers or regressors to build an “approximately optimal” solution in terms of fairness and accuracy for

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Rajeev Nair, Rosemol Thomas, Sandra MV, and Ms. Siji K B. "Interpretable AI: Enhancing Transparency and Fairness in Decision-Making." International Journal of Advanced Research in Science, Communication and Technology, March 17, 2025, 438–43. https://doi.org/10.48175/ijarsct-23772.

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Interpretable Artificial Intelligence aims to make machine learning models more transparent, interpretable, and accountable, addressing the ”black box” nature of traditional AI systems. As AI plays a critical role in high-stakes domains like healthcare, finance, and autonomous systems, ensuring trust and fairness in decision-making has become essential and this paper also explores key techniques in AI. This study adopts a mixed-methods approach to analyse, evaluate, and compare XAI techniques across key domains. This research examines a three-phase approach in XAI, focusing on exploring differ

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Wei, Chen, Kui Xu, Zhexian Shen, Xiaochen Xia, Wei Xie, and Chunguo Li. "Location-aided uplink transmission for user-centric cell-free massive MIMO systems: a fairness priority perspective." EURASIP Journal on Wireless Communications and Networking 2022, no. 1 (2022). http://dx.doi.org/10.1186/s13638-022-02171-x.

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AbstractIn this paper, we investigate the uplink transmission for user-centric cell-free massive multiple-input multiple-output (MIMO) systems. The largest-large-scale-fading-based access point (AP) selection method is adopted to achieve a user-centric operation. Under this user-centric framework, we propose a novel inter-cluster interference-based (IC-IB) pilot assignment scheme to alleviate pilot contamination. Considering the local characteristics of channel estimates and statistics, we propose a location-aided distributed uplink combining scheme to balance the relationship among the spectr

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Rueda, Jon, Janet Delgado Rodríguez, Iris Parra Jounou, Joaquín Hortal-Carmona, Txetxu Ausín, and David Rodríguez-Arias. "“Just” accuracy? Procedural fairness demands explainability in AI-based medical resource allocations." AI & SOCIETY, December 21, 2022. http://dx.doi.org/10.1007/s00146-022-01614-9.

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AbstractThe increasing application of artificial intelligence (AI) to healthcare raises both hope and ethical concerns. Some advanced machine learning methods provide accurate clinical predictions at the expense of a significant lack of explainability. Alex John London has defended that accuracy is a more important value than explainability in AI medicine. In this article, we locate the trade-off between accurate performance and explainable algorithms in the context of distributive justice. We acknowledge that accuracy is cardinal from outcome-oriented justice because it helps to maximize pati

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Duricic, Tomislav, Dominik Kowald, Emanuel Lacic, and Elisabeth Lex. "Beyond-accuracy: a review on diversity, serendipity, and fairness in recommender systems based on graph neural networks." Frontiers in Big Data 6 (December 19, 2023). http://dx.doi.org/10.3389/fdata.2023.1251072.

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By providing personalized suggestions to users, recommender systems have become essential to numerous online platforms. Collaborative filtering, particularly graph-based approaches using Graph Neural Networks (GNNs), have demonstrated great results in terms of recommendation accuracy. However, accuracy may not always be the most important criterion for evaluating recommender systems' performance, since beyond-accuracy aspects such as recommendation diversity, serendipity, and fairness can strongly influence user engagement and satisfaction. This review paper focuses on addressing these dimensi

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NOULAPEU NGAFFO, Armielle, Julien ALBERT, Benoît FRENAY, and Gilles PERROUIN. "A Fair Enhanced Bayesian Personalized Ranking Using Adversarial Learning." ACM Transactions on Recommender Systems, July 16, 2025. https://doi.org/10.1145/3749106.

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The ranking task is the critical step performed during a recommendation process to predict the top-list of most-wanted products for users. Learn-to-rank algorithms have been developed to refine the ranking process. However, the underrepresentation of some demographic user categories leads to unwanted biased ranking performances that affect the fairness aspects of the recommendation. Bayesian Pairwise Ranking (BPR) is among the most popular ranking algorithms for its important ranking accuracy performance. BPR with machine learning recommendation models can unfairly perform for minority user gr

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He, Jiaqi, Apoorva Sheera, Jack McFarland, et al. "A Framework for Measuring and Benchmarking Fairness of Generative Crowd-Flow Models." ACM Journal on Computing and Sustainable Societies, March 16, 2025. https://doi.org/10.1145/3724409.

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Urban population growth has significantly complicated the management of mobility systems, demanding innovative tools for planning. Generative Crowd-Flow (GCF) models, which leverage machine learning to simulate urban movement patterns, offer a promising solution but lack sufficient evaluation of their fairness—a critical factor for equitable urban planning. We present an approach to measure and benchmark the fairness of GCF models by developing a first of its kind set of fairness metrics specifically tailored for this purpose. Using observed flow data, we employ a stochastic biased sampling ap

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Oh, Hyeji, and Chulyun Kim. "Fairness-aware recommendation with meta learning." Scientific Reports 14, no. 1 (2024). http://dx.doi.org/10.1038/s41598-024-60808-x.

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AbstractFairness has become a critical value online, and the latest studies consider it in many problems. In recommender systems, fairness is important since the visibility of items is controlled by systems. Previous fairness-aware recommender systems assume that sufficient relationship data between users and items are available. However, it is common that new users and items are frequently introduced, and they have no relationship data yet. In this paper, we study recommendation methods to enhance fairness in a cold-start state. Fairness is more significant when the preference of a user or th

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Loi, Michele, and Markus Christen. "Choosing how to discriminate: navigating ethical trade-offs in fair algorithmic design for the insurance sector." Philosophy & Technology, March 13, 2021. http://dx.doi.org/10.1007/s13347-021-00444-9.

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AbstractHere, we provide an ethical analysis of discrimination in private insurance to guide the application of non-discriminatory algorithms for risk prediction in the insurance context. This addresses the need for ethical guidance of data-science experts, business managers, and regulators, proposing a framework of moral reasoning behind the choice of fairness goals for prediction-based decisions in the insurance domain. The reference to private insurance as a business practice is essential in our approach, because the consequences of discrimination and predictive inaccuracy in underwriting a

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Zhang, Tao, Tianqing Zhu, Mengde Han, et al. "Fairness in graph-based semi-supervised learning." Knowledge and Information Systems, October 1, 2022. http://dx.doi.org/10.1007/s10115-022-01738-w.

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AbstractMachine learning is widely deployed in society, unleashing its power in a wide range of applications owing to the advent of big data. One emerging problem faced by machine learning is the discrimination from data, and such discrimination is reflected in the eventual decisions made by the algorithms. Recent study has proved that increasing the size of training (labeled) data will promote the fairness criteria with model performance being maintained. In this work, we aim to explore a more general case where quantities of unlabeled data are provided, indeed leading to a new form of learni

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Scher, Sebastian, Simone Kopeinik, Andreas Trügler, and Dominik Kowald. "Modelling the long-term fairness dynamics of data-driven targeted help on job seekers." Scientific Reports 13, no. 1 (2023). http://dx.doi.org/10.1038/s41598-023-28874-9.

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AbstractThe use of data-driven decision support by public agencies is becoming more widespread and already influences the allocation of public resources. This raises ethical concerns, as it has adversely affected minorities and historically discriminated groups. In this paper, we use an approach that combines statistics and data-driven approaches with dynamical modeling to assess long-term fairness effects of labor market interventions. Specifically, we develop and use a model to investigate the impact of decisions caused by a public employment authority that selectively supports job-seekers t

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Müllner, Peter, Elisabeth Lex, Markus Schedl, and Dominik Kowald. "Differential privacy in collaborative filtering recommender systems: a review." Frontiers in Big Data 6 (October 12, 2023). http://dx.doi.org/10.3389/fdata.2023.1249997.

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State-of-the-art recommender systems produce high-quality recommendations to support users in finding relevant content. However, through the utilization of users' data for generating recommendations, recommender systems threaten users' privacy. To alleviate this threat, often, differential privacy is used to protect users' data via adding random noise. This, however, leads to a substantial drop in recommendation quality. Therefore, several approaches aim to improve this trade-off between accuracy and user privacy. In this work, we first overview threats to user privacy in recommender systems,

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Islam, Sheikh Rabiul, Ingrid Russell, William Eberle, Douglas Talbert, and Md Golam Moula Mehedi Hasan. "Advances in Explainable, Fair, and Trustworthy AI." International Journal on Artificial Intelligence Tools 33, no. 03 (2024). http://dx.doi.org/10.1142/s0218213024030015.

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This special issue encapsulates the multifaceted landscape of contemporary challenges and innovations in Artificial Intelligence (AI) and Machine Learning (ML), with a particular focus on issues related to explainability, fairness, and trustworthiness. The exploration begins with the computational intricacies of understanding and explaining the behavior of binary neurons within neural networks. Simultaneously, ethical dimensions in AI are scrutinized, emphasizing the nuanced considerations required in defining autonomous ethical agents. The pursuit of fairness is exemplified through frameworks

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Pal, Manjish, Subham Pokhriyal, Sandipan Sikdar, and Niloy Ganguly. "Ensuring generalized fairness in batch classification." Scientific Reports 13, no. 1 (2023). http://dx.doi.org/10.1038/s41598-023-45943-1.

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AbstractIn this paper, we consider the problem of batch classification and propose a novel framework for achieving fairness in such settings. The problem of batch classification involves selection of a set of individuals, often encountered in real-world scenarios such as job recruitment, college admissions etc. This is in contrast to a typical classification problem, where each candidate in the test set is considered separately and independently. In such scenarios, achieving the same acceptance rate (i.e., probability of the classifier assigning positive class) for each group (membership deter

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Shanklin, Robert, Michele Samorani, Shannon Harris, and Michael A. Santoro. "Ethical Redress of Racial Inequities in AI: Lessons from Decoupling Machine Learning from Optimization in Medical Appointment Scheduling." Philosophy & Technology 35, no. 4 (2022). http://dx.doi.org/10.1007/s13347-022-00590-8.

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AbstractAn Artificial Intelligence algorithm trained on data that reflect racial biases may yield racially biased outputs, even if the algorithm on its own is unbiased. For example, algorithms used to schedule medical appointments in the USA predict that Black patients are at a higher risk of no-show than non-Black patients, though technically accurate given existing data that prediction results in Black patients being overwhelmingly scheduled in appointment slots that cause longer wait times than non-Black patients. This perpetuates racial inequity, in this case lesser access to medical care.

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Rath, Sandeep, Kumar Rajaram, Mark E. Hudson, and Aman Mahajan. "Multilocation, Dynamic Staff Planning for a Healthcare System: Methodology and Application." Operations Research, July 22, 2025. https://doi.org/10.1287/opre.2023.0438.

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Multilocation, Dynamic Staff Planning for a Healthcare System: Methodology and Application This study develops and implements a robust optimization model for dynamically assigning anesthesiologists across multiple hospitals in a large health system. The model addresses uncertainty in surgical demand by incorporating a three-stage decision process: presurgical location assignments, midhorizon on-call deployment, and day-of realization of overtime or idleness. The authors formulate the problem as a multistage robust mixed-integer program and solve it efficiently using a novel nested column and c

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Cortés-Andrés, Jordi, Gustau Camps-Valls, Sebastian Sippel, et al. "Physics-aware nonparametric regression models for Earth data analysis." Environmental Research Letters, April 14, 2022. http://dx.doi.org/10.1088/1748-9326/ac6762.

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Abstract Process understanding and modeling is at the core of scientific reasoning. Principled parametric and mechanistic modeling dominated science and engineering until the recent emergence of machine learning. Despite great success in many areas, machine learning algorithms in the Earth and climate sciences, and more broadly in physical sciences, are not explicitly designed to be physically-consistent and may, therefore, violate the most basic laws of physics. In this work, motivated by the field of algorithmic fairness, we reconcile data-driven machine learning with physics modeling by ill

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Szepannek, Gero, and Karsten Lübke. "Facing the Challenges of Developing Fair Risk Scoring Models." Frontiers in Artificial Intelligence 4 (October 14, 2021). http://dx.doi.org/10.3389/frai.2021.681915.

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Algorithmic scoring methods are widely used in the finance industry for several decades in order to prevent risk and to automate and optimize decisions. Regulatory requirements as given by the Basel Committee on Banking Supervision (BCBS) or the EU data protection regulations have led to an increasing interest and research activity on understanding black box machine learning models by means of explainable machine learning. Even though this is a step into a right direction, such methods are not able to guarantee for a fair scoring as machine learning models are not necessarily unbiased and may

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