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Journal articles on the topic 'Unfairness mitigation'

Author: Grafiati

Published: 8 June 2024

Last updated: 31 July 2025

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1

Jiang, Zifan, Salman Seyedi, Emily Griner, et al. "Evaluating and mitigating unfairness in multimodal remote mental health assessments." PLOS Digital Health 3, no. 7 (2024): e0000413. http://dx.doi.org/10.1371/journal.pdig.0000413.

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Research on automated mental health assessment tools has been growing in recent years, often aiming to address the subjectivity and bias that existed in the current clinical practice of the psychiatric evaluation process. Despite the substantial health and economic ramifications, the potential unfairness of those automated tools was understudied and required more attention. In this work, we systematically evaluated the fairness level in a multimodal remote mental health dataset and an assessment system, where we compared the fairness level in race, gender, education level, and age. Demographic

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Arnaiz-Rodriguez, Adrian, Georgina Curto Rex, and Nuria Oliver. "Structural Group Unfairness: Measurement and Mitigation by Means of the Effective Resistance." Proceedings of the International AAAI Conference on Web and Social Media 19 (June 7, 2025): 83–106. https://doi.org/10.1609/icwsm.v19i1.35805.

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Social networks contribute to the distribution of social capital, defined as the relationships, norms of trust and reciprocity within a community or society that facilitate cooperation and collective action. Therefore, better positioned members in a social network benefit from faster access to diverse information and higher influence on information dissemination. A variety of methods have been proposed in the literature to measure social capital at an individual level. However, there is a lack of methods to quantify social capital at a group level, which is particularly important when the grou

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Balayn, Agathe, Christoph Lofi, and Geert-Jan Houben. "Managing bias and unfairness in data for decision support: a survey of machine learning and data engineering approaches to identify and mitigate bias and unfairness within data management and analytics systems." VLDB Journal 30, no. 5 (2021): 739–68. http://dx.doi.org/10.1007/s00778-021-00671-8.

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AbstractThe increasing use of data-driven decision support systems in industry and governments is accompanied by the discovery of a plethora of bias and unfairness issues in the outputs of these systems. Multiple computer science communities, and especially machine learning, have started to tackle this problem, often developing algorithmic solutions to mitigate biases to obtain fairer outputs. However, one of the core underlying causes for unfairness is bias in training data which is not fully covered by such approaches. Especially, bias in data is not yet a central topic in data engineering a

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Singh, Nimisha, Amita Kapoor, and Neha Soni. "A sociotechnical perspective for explicit unfairness mitigation techniques for algorithm fairness." International Journal of Information Management Data Insights 4, no. 2 (2024): 100259. http://dx.doi.org/10.1016/j.jjimei.2024.100259.

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Pagano, Tiago P., Rafael B. Loureiro, Fernanda V. N. Lisboa, et al. "Bias and Unfairness in Machine Learning Models: A Systematic Review on Datasets, Tools, Fairness Metrics, and Identification and Mitigation Methods." Big Data and Cognitive Computing 7, no. 1 (2023): 15. http://dx.doi.org/10.3390/bdcc7010015.

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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

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Ding, Xueying, Rui Xi, and Leman Akoglu. "Outlier Detection Bias Busted: Understanding Sources of Algorithmic Bias through Data-centric Factors." Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society 7 (October 16, 2024): 384–95. http://dx.doi.org/10.1609/aies.v7i1.31644.

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The astonishing successes of ML have raised growing concern for the fairness of modern methods when deployed in real world settings. However, studies on fairness have mostly focused on supervised ML, while unsupervised outlier detection (OD), with numerous applications in finance, security, etc., have attracted little attention. While a few studies proposed fairness-enhanced OD algorithms, they remain agnostic to the underlying driving mechanisms or sources of unfairness. Even within the supervised ML literature, there exists debate on whether unfairness stems solely from algorithmic biases (i

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Abdullah, Nurhidayah, and Zuhairah Ariff Abd Ghadas. "THE APPLICATION OF GOOD FAITH IN CONTRACTS DURING A FORCE MAJEURE EVENT AND BEYOND WITH SPECIAL REFERENCE TO THE COVID-19 ACT 2020." UUM Journal of Legal Studies 14, no. 1 (2023): 141–60. http://dx.doi.org/10.32890/uumjls2023.14.1.6.

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Many parties face difficulties in performing contracts due to the economic dislocation since the outbreak of COVID-19. The extraordinary nature of this pandemic situation calls for good faith in contractual settings. The discussion of this paper focuses on the imposition in a force majeure event which will cause many contracts to be unenforceable. The research method used doctrinal analysis to discuss the force majeure clause in the context of the COVID-19 pandemic and the obligation of good faith in contracts. This paper will discuss the COVID-19 pandemic as a force majeure event, arguing tha

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Verger, Mélina, Chunyang Fan, Sébastien Lallé, François Bouchet, and Vanda Luengo. "A Comprehensive Study on Evaluating and Mitigating Algorithmic Unfairness with the MADD Metric." Journal of Educational Data Mining (JEDM) 16, no. 1 (2024): 365–409. https://doi.org/10.5281/zenodo.12180668.

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Predictive student models are increasingly used in learning environments due to their ability to enhanceeducational outcomes and support stakeholders in making informed decisions. However, predictive modelscan be biased and produce unfair outcomes, leading to potential discrimination against certain individualsand harmful long-term implications. This has prompted research on fairness metrics meant tocapture and quantify such biases. Nonetheless, current metrics primarily focus on predictive performancecomparisons between groups, without considering the behavior of the models or the severity of

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Popoola, Gideon, and John Sheppard. "Investigating and Mitigating the Performance–Fairness Tradeoff via Protected-Category Sampling." Electronics 13, no. 15 (2024): 3024. http://dx.doi.org/10.3390/electronics13153024.

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Machine learning algorithms have become common in everyday decision making, and decision-assistance systems are ubiquitous in our everyday lives. Hence, research on the prevention and mitigation of potential bias and unfairness of the predictions made by these algorithms has been increasing in recent years. Most research on fairness and bias mitigation in machine learning often treats each protected variable separately, but in reality, it is possible for one person to belong to multiple protected categories. Hence, in this work, combining a set of protected variables and generating new columns

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Menziwa, Yolanda, Eunice Lebogang Sesale, and Solly Matshonisa Seeletse. "Challenges in research data collection and mitigation interventions." International Journal of Research in Business and Social Science (2147- 4478) 13, no. 2 (2024): 336–44. http://dx.doi.org/10.20525/ijrbs.v13i2.3187.

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This paper investigated the challenges that researchers in a health sciences university can experience, and ways to counterbalance the negative effects of these challenges. Focus was on the extent to which gatekeepers on higher education institutions (HEIs) can restrict research, and the way natural sciences researchers often experience gatekeeper biasness on denying them access as compared to the way health sciences researchers are treated. The method compared experiences of researchers for Master of Science (MSc) degrees in selected science subjects, and the projects undertaken by health sci

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Rana, Saadia Afzal, Zati Hakim Azizul, and Ali Afzal Awan. "A step toward building a unified framework for managing AI bias." PeerJ Computer Science 9 (October 26, 2023): e1630. http://dx.doi.org/10.7717/peerj-cs.1630.

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Integrating artificial intelligence (AI) has transformed living standards. However, AI’s efforts are being thwarted by concerns about the rise of biases and unfairness. The problem advocates strongly for a strategy for tackling potential biases. This article thoroughly evaluates existing knowledge to enhance fairness management, which will serve as a foundation for creating a unified framework to address any bias and its subsequent mitigation method throughout the AI development pipeline. We map the software development life cycle (SDLC), machine learning life cycle (MLLC) and cross industry s

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Latif, Aadil, Wolfgang Gawlik, and Peter Palensky. "Quantification and Mitigation of Unfairness in Active Power Curtailment of Rooftop Photovoltaic Systems Using Sensitivity Based Coordinated Control." Energies 9, no. 6 (2016): 436. http://dx.doi.org/10.3390/en9060436.

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Mavrogiorgos, Konstantinos, Athanasios Kiourtis, Argyro Mavrogiorgou, Andreas Menychtas, and Dimosthenis Kyriazis. "Bias in Machine Learning: A Literature Review." Applied Sciences 14, no. 19 (2024): 8860. http://dx.doi.org/10.3390/app14198860.

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Bias could be defined as the tendency to be in favor or against a person or a group, thus promoting unfairness. In computer science, bias is called algorithmic or artificial intelligence (i.e., AI) and can be described as the tendency to showcase recurrent errors in a computer system, which result in “unfair” outcomes. Bias in the “outside world” and algorithmic bias are interconnected since many types of algorithmic bias originate from external factors. The enormous variety of different types of AI biases that have been identified in diverse domains highlights the need for classifying the sai

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Maripova, Tamanno. "Mitigating Algorithmic Bias in Predictive Models." American Journal of Engineering and Technology 07, no. 05 (2025): 192–201. https://doi.org/10.37547/tajet/volume07issue05-19.

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This article considers the issue of systematic errors in predictive machine-learning models generating disparate outcomes for different social groups and proposes a holistic approach to its mitigation. The risks and increasing legal requirements, along with corporate commitments to ethical AIs, drive the relevance of this study. The work herewith attempts to develop a bias-source taxonomy at data collection and annotation, proxy-feature selection, model training, and deployment stages; also, it tries to compare pre-, in-, and post-processing methods' effectiveness on representative datasets me

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Yang, Zhenhuan, Yan Lok Ko, Kush R. Varshney, and Yiming Ying. "Minimax AUC Fairness: Efficient Algorithm with Provable Convergence." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 10 (2023): 11909–17. http://dx.doi.org/10.1609/aaai.v37i10.26405.

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The use of machine learning models in consequential decision making often exacerbates societal inequity, in particular yielding disparate impact on members of marginalized groups defined by race and gender. The area under the ROC curve (AUC) is widely used to evaluate the performance of a scoring function in machine learning, but is studied in algorithmic fairness less than other performance metrics. Due to the pairwise nature of the AUC, defining an AUC-based group fairness metric is pairwise-dependent and may involve both intra-group and inter-group AUCs. Importantly, considering only one ca

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Khanam, Taslima. "Rule of law approach to alleviation of poverty: An analysis on human rights dimension of governance." IIUC Studies 15 (September 21, 2020): 23–32. http://dx.doi.org/10.3329/iiucs.v15i0.49342.

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A society without rule of law is similar to a bowl having holes in it, so it leaks. Without plugging the leaks, putting more money in it makes no sense. Almost this claptrap is going in the form of poverty mitigation programs. Retorting the fact, this paper reflects that substantial poverty must be implied as formed by society itself and argues that lots of inhabitants of the world are deprived of the opportunity to get improved livings and live in dearth, as they are not within the shield of the rule of law. They may possibly be the citizens of nation state in which they live; nevertheless, t

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Fahad S. Alenazi. "Dynamic Detection and Debias of Bayesian Network Classifier (3D-BN)." Journal of Information Systems Engineering and Management 10, no. 35s (2025): 419–31. https://doi.org/10.52783/jisem.v10i35s.6020.

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Fairness in machine learning is a complex and multifaceted concept, increasingly critical in automated decision-making systems. Numerous metrics and techniques have been developed to measure and mitigate bias effectively. However, tensions often arise between different fairness notions, such as individual versus group fairness, and even among various group fairness approaches. These conflicts are typically rooted in inadequate implementation of fairness measures rather than fundamental contradictions. Additionally, failing to account for interdependencies among attributes can lead to unintende

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Soundarya, Lakshmi M. "Mitigating Bias/unfairness in Hiring, Promotion." Journal of Research and Review in Human Resource Management & Labour Studies 1, no. 2 (2024): 23–31. https://doi.org/10.5281/zenodo.13903339.

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<em>The work/analysis investigates the common problem of implicit attitude on promotion as well as hiring in the organization. The study explores how it is distinct in making decisions and addressing the influence. The research is based on multi method procedure i.e survey among experienced professionals or employees working in a company, analyzing the present literature in order to comprehend the influence of implicit prejudice on demographics i.e sex, origin/race, economic strength. The research digs into insights concerning to DEI inside the company, throwing light on the pivotal role of th

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Qi, Jin. "Mitigating Delays and Unfairness in Appointment Systems." Management Science 63, no. 2 (2017): 566–83. http://dx.doi.org/10.1287/mnsc.2015.2353.

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Lehrieder, Frank, Simon Oechsner, Tobias Hoßfeld, et al. "Mitigating unfairness in locality-aware peer-to-peer networks." International Journal of Network Management 21, no. 1 (2011): 3–20. http://dx.doi.org/10.1002/nem.772.

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Mao, Runze, Wenqi Fan, and Qing Li. "GCARe: Mitigating Subgroup Unfairness in Graph Condensation through Adversarial Regularization." Applied Sciences 13, no. 16 (2023): 9166. http://dx.doi.org/10.3390/app13169166.

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Training Graph Neural Networks (GNNs) on large-scale graphs in the deep learning era can be expensive. While graph condensation has recently emerged as a promising approach through which to reduce training cost by compressing large graphs into smaller ones and for preserving most knowledge, its capability in treating different node subgroups fairly during compression remains unexplored. In this paper, we investigate current graph condensation techniques from a perspective of fairness, and show that they bear severe disparate impact toward node subgroups. Specifically, GNNs trained on condensed

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Mahmud, Md Sultan, and Md Forkan Uddin. "Mitigating unfairness problem in WLANs caused by asymmetric co-channel interference." International Journal of Mobile Communications 16, no. 3 (2018): 307. http://dx.doi.org/10.1504/ijmc.2018.091385.

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Mahmud, Md Sultan, and Md Forkean Uddin. "Mitigating unfairness problem in WLANs caused by asymmetric co-channel interference." International Journal of Mobile Communications 16, no. 1 (2018): 1. http://dx.doi.org/10.1504/ijmc.2018.10004556.

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Bejerano, Yigal, Seung-Jae Han, and Mark Smith. "A novel frequency planning algorithm for mitigating unfairness in wireless LANs." Computer Networks 54, no. 15 (2010): 2575–90. http://dx.doi.org/10.1016/j.comnet.2010.04.009.

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Wang, Wei, Ben Leong, and Wei Tsang Ooi. "Mitigating Unfairness Due to Physical Layer Capture in Practical 802.11 Mesh Networks." IEEE Transactions on Mobile Computing 14, no. 1 (2015): 99–112. http://dx.doi.org/10.1109/tmc.2014.2315796.

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Shahbazi, Nima, Mahdi Erfanian, Abolfazl Asudeh, Fatemeh Nargesian, and Divesh Srivastava. "FairEM360: A Suite for Responsible Entity Matching." Proceedings of the VLDB Endowment 17, no. 12 (2024): 4417–20. http://dx.doi.org/10.14778/3685800.3685889.

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Entity matching is one of the earliest tasks that occur in the big data pipeline and is alarmingly exposed to unintentional biases that affect the quality of data. Identifying and mitigating the biases that exist in the data or are introduced by the matcher at this stage can contribute to promoting fairness in downstream tasks. This demonstration showcases FairEM360, a framework for 1) auditing the output of entity matchers across a wide range of fairness measures and paradigms, 2) providing potential explanations for the underlying reasons for unfairness, and 3) providing resolutions for the

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Wu, Yinghui, Hai Yang, Shuo Zhao, and Pan Shang. "Mitigating unfairness in urban rail transit operation: A mixed-integer linear programming approach." Transportation Research Part B: Methodological 149 (July 2021): 418–42. http://dx.doi.org/10.1016/j.trb.2021.04.014.

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Zhang, Wenbin, Tina Hernandez-Boussard, and Jeremy Weiss. "Censored Fairness through Awareness." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 12 (2023): 14611–19. http://dx.doi.org/10.1609/aaai.v37i12.26708.

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There has been increasing concern within the machine learning community and beyond that Artificial Intelligence (AI) faces a bias and discrimination crisis which needs AI fairness with urgency. As many have begun to work on this problem, most existing work depends on the availability of class label for the given fairness definition and algorithm which may not align with real-world usage. In this work, we study an AI fairness problem that stems from the gap between the design of a "fair" model in the lab and its deployment in the real-world. Specifically, we consider defining and mitigating ind

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de Andrés-Sánchez, Jorge, and Laura González-Vila Puchades. "Longevity Risk and Annuitisation Decisions in the Absence of Special-Rate Life Annuities." Risks 13, no. 2 (2025): 37. https://doi.org/10.3390/risks13020037.

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Longevity risk affecting older adults can be transferred to the insurance market by purchasing a lifetime annuity. Special-rate life annuities, which are priced, among other factors, on the basis of health and lifestyle factors, go beyond traditional considerations of age and sex by using modified mortality tables. However, they are not available in many countries. In regions where life annuities are priced solely via standard mortality tables, retirees with below-average life expectancy may face unfair pricing. This study aims to quantify this actuarial unfairness and proposes an alternative

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Ko, Joonhyuk, Juba Ziani, Saswat Das, Matt Williams, and Ferdinando Fioretto. "Fairness Issues and Mitigations in (Differentially Private) Socio-Demographic Data Processes." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 27 (2025): 28160–67. https://doi.org/10.1609/aaai.v39i27.35035.

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Statistical agencies rely on sampling techniques to collect socio-demographic data crucial for policy-making and resource allocation. This paper shows that surveys of important societal relevance introduce sampling errors that unevenly impact group-level estimates, thereby compromising fairness in downstream decisions. To address these issues, this paper introduces an optimization approach modeled on real-world survey design processes, ensuring sampling costs are optimized while maintaining error margins within prescribed tolerances. Additionally, privacy-preserving methods used to determine s

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Pade, Robin, and Sven Feurer. "The mitigating role of nostalgia for consumer price unfairness perceptions in response to disadvantageous personalized pricing." Journal of Business Research 145 (June 2022): 277–87. http://dx.doi.org/10.1016/j.jbusres.2022.02.057.

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Safdari, Fash, and Anatoliy Gorbenko. "Theoretical and experimental study of performance anomaly in multi-rate IEEE802.11ac wireless networks." Radioelectronic and Computer Systems, no. 4 (November 29, 2022): 85–97. http://dx.doi.org/10.32620/reks.2022.4.06.

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IEEE 802.11 wireless local area networks (WLANs) are shared networks, which use contention-based distributed coordination function (DCF) to share access to wireless medium among numerous wireless stations. The performance of the distributed coordination function mechanism mostly depends on the network load, number of wireless nodes and their data rates. The throughput unfairness, also known as performance anomaly is inherent in the very nature of mixed data rate Wi-Fi networks using the distributed coordination function. This unfairness exhibits itself through the fact that slow clients consum

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Delobelle, Pieter, Paul Temple, Gilles Perrouin, Benoit Frénay, Patrick Heymans, and Bettina Berendt. "Ethical Adversaries." ACM SIGKDD Explorations Newsletter 23, no. 1 (2021): 32–41. http://dx.doi.org/10.1145/3468507.3468513.

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Machine learning is being integrated into a growing number of critical systems with far-reaching impacts on society. Unexpected behaviour and unfair decision processes are coming under increasing scrutiny due to this widespread use and its theoretical considerations. Individuals, as well as organisations, notice, test, and criticize unfair results to hold model designers and deployers accountable. We offer a framework that assists these groups in mitigating unfair representations stemming from the training datasets. Our framework relies on two inter-operating adversaries to improve fairness. F

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Mansoury, Masoud. ""Understanding and mitigating multi-sided exposure bias in recommender systems" by Masoud Mansoury with Aparna S. Varde as coordinator." ACM SIGWEB Newsletter, Autumn (September 2022): 1–4. http://dx.doi.org/10.1145/3566100.3566103.

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Masoud Mansoury is a postdoctoral researcher at Amsterdam Machine Learning Lab at University of Amsterdam, Netherlands. He is also a member of Discovery Lab collaborating with Data Science team at Elsevier Company in the area of recommender systems. Masoud received his PhD in Computer and Information Science from Eindhoven University of Technology, Netherlands, in 2021. He has published his research works in top conferences such as FAccT, RecSys, and CIKM. His research interests include recommender systems, algorithmic bias, and contextual bandits. This research conducted by Masoud Mansoury in

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Bargh, Mortaza S., and Sunil Choenni. "Towards an Integrated Approach for Preserving Data Utility, Privacy and Fairness." 2018 International Conference on Multidisciplinary Research 2022 (December 30, 2022): 290–360. http://dx.doi.org/10.26803/myres.2022.24.

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Data reusability has become a distinct characteristic of scientific, commercial, and administrative practices nowadays. However, an unlimited and careless reuse of data may lead to privacy breaches and unfair impacts on individuals and vulnerable groups. Data content adaption is a key aspect of preserving data privacy and fairness. Often, such content adaption affects data utility adversely. Further, the interaction between privacy protection and fairness protection can be subject to making trade-offs because mitigating privacy risks may adversely affect detecting unfairness and vice versa. Th

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Agrawal, Nimesh, Anuj Kumar Sirohi, Sandeep Kumar, and Jayadeva. "No Prejudice! Fair Federated Graph Neural Networks for Personalized Recommendation." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 10 (2024): 10775–83. http://dx.doi.org/10.1609/aaai.v38i10.28950.

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Ensuring fairness in Recommendation Systems (RSs) across demographic groups is critical due to the increased integration of RSs in applications such as personalized healthcare, finance, and e-commerce. Graph-based RSs play a crucial role in capturing intricate higher-order interactions among entities. However, integrating these graph models into the Federated Learning (FL) paradigm with fairness constraints poses formidable challenges as this requires access to the entire interaction graph and sensitive user information (such as gender, age, etc.) at the central server. This paper addresses th

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Tang, Ze, Ziwei Wang, and Xinbao Wang. "Sudden Infant Death Syndrome Mortality Trends and Socioeconomic Inequalities Worldwide: Evidence from the Global Burden of Disease Study." Children 12, no. 6 (2025): 747. https://doi.org/10.3390/children12060747.

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Background: Sudden Infant Death Syndrome (SIDS) remains an important global health concern despite its decline in recent decades. This research assesses the global, regional, and national tendencies in SIDS mortality and DALYs from 1991 to 2021, highlighting the differences across various sociodemographic indexes (SDIs). Methods: Utilizing data from the Global Burden of Disease (GBD) study 2021, SIDS mortality and DALYs were evaluated across different global regions, SDI categories, and age groups. The trends over the study period were determined by conducting estimated annual percentage chang

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Udoh, I. J., and O. I. Alabi. "An Assessment of Counter-Terrorism Options: A State Dependent Terror Queuing Model Perspective." International Journal of Research and Innovation in Applied Science IX, no. II (2024): 74–103. http://dx.doi.org/10.51584/ijrias.2024.90209.

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The study is an application of a state-dependent queuing theory to evaluate the performance of counterterrorism (CT) options. The CT options examined include the Stick (use of force), the Carrot (non-coercive approaches), their combined variant, and covert agents. The model incorporates state transitions to capture the dynamic nature of terrorist recruitment processes in a CT environment. Performance measures are adapted from conventional queue frameworks to assess the effectiveness of these CT options in mitigating terrorist threats. The study analyses the CT options under an arithmetic progr

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Ngcobo, Senzo, and Colin D. Reddy. "Exploring the Link between Organisational Performance Pressures and the Factors that Compromise Ethical Leadership." Athens Journal of Business & Economics 10, no. 2 (2024): 139–58. http://dx.doi.org/10.30958/ajbe.10-2-4.

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Purpose: This research paper explores the link between threat-appraised organizational performance pressure and factors that compromise ethical leadership. Design/Methodology/Approach: The study uses a qualitative approach, using a rank-type Delphi method and administered questionnaires to 40 academic and practitioner experts and 10 organisational leaders. The collected data was analysed through qualitative comparative analysis (QCA). Results: The findings provide empirical evidence of the detrimental impact of threat-appraised performance pressure on ethical leadership behaviour. Four themes

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Huikko-Tarvainen, Sari, and Tommi Auvinen. "Archetypes of bad physician leaders – a qualitative study from a large Finnish central hospital." Leadership in Health Services 38, no. 5 (2025): 48–64. https://doi.org/10.1108/lhs-12-2024-0154.

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Purpose This study aims to explore the archetypes of bad leaders as perceived by Finnish physicians across various hierarchical levels. Understanding these archetypes is essential for addressing leadership deficiencies and mitigating the detrimental effects of bad leadership and the positivity bias in leadership research. Design/methodology/approach Data were collected through in-person, semi-structured interviews with Finnish physicians (n = 50), including residents, specialists, heads of departments and chief physicians. Inductive content analysis, followed by thematization, was used to iden

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Xu, Zikang, Jun Li, Qingsong Yao, Han Li, Mingyue Zhao, and S. Kevin Zhou. "Addressing fairness issues in deep learning-based medical image analysis: a systematic review." npj Digital Medicine 7, no. 1 (2024). http://dx.doi.org/10.1038/s41746-024-01276-5.

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AbstractDeep learning algorithms have demonstrated remarkable efficacy in various medical image analysis (MedIA) applications. However, recent research highlights a performance disparity in these algorithms when applied to specific subgroups, such as exhibiting poorer predictive performance in elderly females. Addressing this fairness issue has become a collaborative effort involving AI scientists and clinicians seeking to understand its origins and develop solutions for mitigation within MedIA. In this survey, we thoroughly examine the current advancements in addressing fairness issues in Med

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Du, Bingqian, Liyao Xiang, and Chuan Wu. "Mitigating Unfairness in Differentially-Private Federated Learning." ACM Transactions on Modeling and Performance Evaluation of Computing Systems, March 25, 2025. https://doi.org/10.1145/3725847.

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Federated learning is a new learning paradigm which utilizes crowdsourced data stored at dispersed user devices (aka clients) to learn a global model. Studies have shown that even though data are kept on local devices, an adversary is still able to infer client information during the training process or from the learned model. Differential privacy has recently been introduced to deep learning model training, to protect data privacy of clients. Nonetheless, it exacerbates unfairness with the learned model among participating clients due to its uniform clipping and noise addition, even when the

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Villarreal, Dan. "Sociolinguistic auto-coding has fairness problems too: measuring and mitigating bias." Linguistics Vanguard, March 12, 2024. http://dx.doi.org/10.1515/lingvan-2022-0114.

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Abstract Sociolinguistics researchers can use sociolinguistic auto-coding (SLAC) to predict humans’ hand-codes of sociolinguistic data. While auto-coding promises opportunities for greater efficiency, like other computational methods there are inherent concerns about this method’s fairness – whether it generates equally valid predictions for different speaker groups. Unfairness would be problematic for sociolinguistic work given the central importance of correlating speaker groups to differences in variable usage. The current study examines SLAC fairness through the lens of gender fairness in

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Brown, Alexander, Nenad Tomasev, Jan Freyberg, Yuan Liu, Alan Karthikesalingam, and Jessica Schrouff. "Detecting shortcut learning for fair medical AI using shortcut testing." Nature Communications 14, no. 1 (2023). http://dx.doi.org/10.1038/s41467-023-39902-7.

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AbstractMachine learning (ML) holds great promise for improving healthcare, but it is critical to ensure that its use will not propagate or amplify health disparities. An important step is to characterize the (un)fairness of ML models—their tendency to perform differently across subgroups of the population—and to understand its underlying mechanisms. One potential driver of algorithmic unfairness, shortcut learning, arises when ML models base predictions on improper correlations in the training data. Diagnosing this phenomenon is difficult as sensitive attributes may be causally linked with di

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Deho, Oscar Blessed, Chen Zhan, Jiuyong Li, Jixue Liu, Lin Liu, and Thuc Duy Le. "How do the existing fairness metrics and unfairness mitigation algorithms contribute to ethical learning analytics?" British Journal of Educational Technology, April 12, 2022. http://dx.doi.org/10.1111/bjet.13217.

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Sun, Xiaotan, Makiya Nakashima, Christopher Nguyen, et al. "FairICP: identifying biases and increasing transparency at the point of care in post-implementation clinical decision support using inductive conformal prediction." Journal of the American Medical Informatics Association, June 15, 2025. https://doi.org/10.1093/jamia/ocaf095.

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Abstract Objectives Fairness concerns stemming from known and unknown biases in healthcare practices have raised questions about the trustworthiness of Artificial Intelligence (AI)-driven Clinical Decision Support Systems (CDSS). Studies have shown unforeseen performance disparities in subpopulations when applied to clinical settings different from training. Existing unfairness mitigation strategies often struggle with scalability and accessibility, while their pursuit of group-level prediction performance parity does not effectively translate into fairness at the point of care. This study int

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Huang, Dong, Jie M. Zhang, Qingwen Bu, Xiaofei Xie, Junjie Chen, and Heming Cui. "Bias Testing and Mitigation in LLM-based Code Generation." ACM Transactions on Software Engineering and Methodology, March 18, 2025. https://doi.org/10.1145/3724117.

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As the adoption of LLMs becomes more widespread in software coding ecosystems, a pressing issue has emerged: does the generated code contain social bias and unfairness, such as those related to age, gender, and race? This issue concerns the integrity, fairness, and ethical foundation of software applications that depend on the code generated by these models but are underexplored in the literature. This paper presents a novel bias testing framework that is specifically designed for code generation tasks. Based on this framework, we conduct an extensive empirical study on the biases in code gene

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Bozeman, Joe F., Catharina Hollauer, Arjun Thangaraj Ramshankar, et al. "Embed systemic equity throughout industrial ecology applications: How to address machine learning unfairness and bias." Journal of Industrial Ecology, June 18, 2024. http://dx.doi.org/10.1111/jiec.13509.

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AbstractRecent calls have been made for equity tools and frameworks to be integrated throughout the research and design life cycle —from conception to implementation—with an emphasis on reducing inequity in artificial intelligence (AI) and machine learning (ML) applications. Simply stating that equity should be integrated throughout, however, leaves much to be desired as industrial ecology (IE) researchers, practitioners, and decision‐makers attempt to employ equitable practices. In this forum piece, we use a critical review approach to explain how socioecological inequities emerge in ML appli

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Wan, Mingyang, Daochen Zha, Ninghao Liu, and Na Zou. "In-Processing Modeling Techniques for Machine Learning Fairness: A Survey." ACM Transactions on Knowledge Discovery from Data, July 30, 2022. http://dx.doi.org/10.1145/3551390.

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Machine learning models are becoming pervasive in high-stakes applications. Despite their clear benefits in terms of performance, the models could show discrimination against minority groups and result in fairness issues in a decision-making process, leading to severe negative impacts on the individuals and the society. In recent years, various techniques have been developed to mitigate the unfairness for machine learning models. Among them, in-processing methods have drawn increasing attention from the community, where fairness is directly taken into consideration during model design to induc

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Tsai, Thomas C., Sercan Arik, Benjamin H. Jacobson, et al. "Algorithmic fairness in pandemic forecasting: lessons from COVID-19." npj Digital Medicine 5, no. 1 (2022). http://dx.doi.org/10.1038/s41746-022-00602-z.

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AbstractRacial and ethnic minorities have borne a particularly acute burden of the COVID-19 pandemic in the United States. There is a growing awareness from both researchers and public health leaders of the critical need to ensure fairness in forecast results. Without careful and deliberate bias mitigation, inequities embedded in data can be transferred to model predictions, perpetuating disparities, and exacerbating the disproportionate harms of the COVID-19 pandemic. These biases in data and forecasts can be viewed through both statistical and sociological lenses, and the challenges of both

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