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Journal articles on the topic 'Mitigation des biais'

Author: Grafiati

Published: 8 June 2024

Last updated: 31 July 2025

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1

Philipps, Nathalia, Pierre P. Kastendeuch, and Georges Najjar. "Analyse de la variabilité spatio-temporelle de l’îlot de chaleur urbain à Strasbourg (France)." Climatologie 17 (2020): 10. http://dx.doi.org/10.1051/climat/202017010.

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Une analyse de la dynamique temporelle et de la distribution spatiale de l’îlot de chaleur urbain (ICU) strasbourgeois a été menée à l’aide d’un réseau de stations météorologiques réparties sur l’ensemble du territoire de l’agglomération strasbourgeoise. L’importante variabilité temporelle de l’ICU est illustrée non seulement à travers son comportement thermique journalier, mais également par le biais des fortes différences d’intensité selon les saisons et les types de temps. Favorisé lors de vents faibles et d’ensoleillement important, l’ICU se montre particulièrement intense durant les belle

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Elvira Espinosa, Andres. "“Justly and Without Bias”." Journal of Bahá’í Studies 33, no. 4 (2024): 9–37. http://dx.doi.org/10.31581/jbs-33.4.534(2023).

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This paper investigates the possibility that one purpose of consultation is the mitigation of cognitive biases in individual participantsand in the group as a whole. After exploring the nature of cognitive biases through the lens of evolutionary psychology, the paper surveys existing research on effective methods of “debiasing” individuals. This research suggests that the most effective environment for mitigating bias is a deliberative group, in which individual participants may be asked to justify their reasoning in a social environment of diverse perspectives. Bias mitigation diminishes over

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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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Rahmawati, Fitriana, and Fitri Santi. "A Literature Review on the Influence of Availability Bias and Overconfidence Bias on Investor Decisions." East Asian Journal of Multidisciplinary Research 2, no. 12 (2023): 4961–76. http://dx.doi.org/10.55927/eajmr.v2i12.6896.

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This research examines the impact of Availability Bias and Overconfidence Bias on investment decisions. Utilizing a literature review approach and VOSviewer analysis, this study explores how these biases affect investor decision-making processes and potential mitigation strategies. The objective is to highlight the significance of understanding and mitigating these biases in achieving more rational investment decisions. The findings underscore the potential negative effects of both biases, leading to overconfident and less rational investment decisions. Awareness of their interplay is crucial,

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McDaniel, Gail. "Understanding and Mitigating Bias and Noise in Data Collection, Imputation and Analysis." International Multidisciplinary Journal of Science, Technology and Business Volume 04, no. 01 (2025): 1–29. https://doi.org/10.5281/zenodo.15004631.

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<strong>Abstract</strong><strong>:</strong><strong><em> </em></strong><em>Bias and noise in data significantly impact the accuracy and reliability of research findings and data-driven decision-making. This paper provides a comprehensive overview of various types of bias and noise affecting data quality, their impact on research and decision-making, and strategies for mitigation. We examine sampling bias, </em><em>non response</em><em> bias, measurement bias, imputation bias, and analysis bias, as well as the role of noise as a source of bias. The paper also explores bias in survey de

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Lugli, Luciano C., Daniel Abujabra Merege, and Rafael Pillon Almeida. "Mitigação de viés de datasets multimodais em um classificador de categorias urbano-sociais." Estudos Avançados 38, no. 111 (2024): 365–80. http://dx.doi.org/10.1590/s0103-4014.202438111.019.

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RESUMO O referido projeto se caracteriza nas implicações relacionais do desenvolvimento sociomoral da teoria psicogenética em Piaget sobre a construção cognoscente da ética nos vieses pessoais e em referenciais da dialética discursiva na linguística. Foram parametrizados a dados funcionais de treinamento e teste em um classificador de categorias urbano-sociais em uma abordagem analítica textual por Processamento de Linguagem Natural (PLN), e baseado no mecanismo de atenção adaptada Transformers. Nessa perspectiva, desenvolveu-se uma metodologia de mitigação de viés para a reestruturação do cri

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Djebrouni, Yasmine, Nawel Benarba, Ousmane Touat, et al. "Bias Mitigation in Federated Learning for Edge Computing." Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 7, no. 4 (2023): 1–35. http://dx.doi.org/10.1145/3631455.

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Federated learning (FL) is a distributed machine learning paradigm that enables data owners to collaborate on training models while preserving data privacy. As FL effectively leverages decentralized and sensitive data sources, it is increasingly used in ubiquitous computing including remote healthcare, activity recognition, and mobile applications. However, FL raises ethical and social concerns as it may introduce bias with regard to sensitive attributes such as race, gender, and location. Mitigating FL bias is thus a major research challenge. In this paper, we propose Astral, a novel bias mit

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Gallaher, Joshua P., Alexander J. Kamrud, and Brett J. Borghetti. "Detection and Mitigation of Inefficient Visual Searching." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 64, no. 1 (2020): 47–51. http://dx.doi.org/10.1177/1071181320641015.

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A commonly known cognitive bias is a confirmation bias: the overweighting of evidence supporting a hy- pothesis and underweighting evidence countering that hypothesis. Due to high-stress and fast-paced opera- tions, military decisions can be affected by confirmation bias. One military decision task prone to confirma- tion bias is a visual search. During a visual search, the operator scans an environment to locate a specific target. If confirmation bias causes the operator to scan the wrong portion of the environment first, the search is inefficient. This study has two primary goals: 1) detect

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Lee, Yu-Hao, Norah E. Dunbar, Claude H. Miller, et al. "Training Anchoring and Representativeness Bias Mitigation Through a Digital Game." Simulation & Gaming 47, no. 6 (2016): 751–79. http://dx.doi.org/10.1177/1046878116662955.

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Objective. Humans systematically make poor decisions because of cognitive biases. Can digital games train people to avoid cognitive biases? The goal of this study is to investigate the affordance of different educational media in training people about cognitive biases and to mitigate cognitive biases within their decision-making processes. Method. A between-subject experiment was conducted to compare a digital game, a traditional slideshow, and a combined condition in mitigating two types of cognitive biases: anchoring bias and representativeness bias. We measured both immediate effects and de

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Tripathi, Manish, and Raghav Agarwal. "Bias Mitigation in NLP: Automated Detection and Correction." International Journal of Research in Modern Engineering & Emerging Technology 13, no. 5 (2025): 45–60. https://doi.org/10.63345/ijrmeet.org.v13.i5.130503.

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Natural Language Processing (NLP) systems have shown remarkable capabilities, but they often inherit biases from the datasets they are trained on, resulting in outcomes that can be unfair or even harmful. These biases can appear in different forms, such as those related to gender, race, or socioeconomic status. Addressing and mitigating bias in NLP has become a critical area of research, aiming to ensure that machine learning models generate fair and impartial results. This paper delves into the automation of bias detection and correction within NLP systems. It reviews current methods for iden

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Yin, Maxwell J., Boyu Wang, and Charles Ling. "MABR: Multilayer Adversarial Bias Removal Without Prior Bias Knowledge." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 24 (2025): 25724–32. https://doi.org/10.1609/aaai.v39i24.34764.

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Models trained on real-world data often mirror and exacerbate existing social biases. Traditional methods for mitigating these biases typically require prior knowledge of the specific biases to be addressed, and the social groups associated with each instance. In this paper, we introduce a novel adversarial training strategy that operates withour relying on prior bias-type knowledge (e.g., gender or racial bias) and protected attribute labels. Our approach dynamically identifies biases during model training by utilizing auxiliary bias detector. These detected biases are simultaneously mitigate

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Rashed, Ahmed, Abdelkrim Kallich, and Mohamed Eltayeb. "Analyzing Fairness of Computer Vision and Natural Language Processing Models." Information 16, no. 3 (2025): 182. https://doi.org/10.3390/info16030182.

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Machine learning (ML) algorithms play a critical role in decision-making across various domains, such as healthcare, finance, education, and law enforcement. However, concerns about fairness and bias in these systems have raised significant ethical and social challenges. To address these challenges, this research utilizes two prominent fairness libraries, Fairlearn by Microsoft and AIF360 by IBM. These libraries offer comprehensive frameworks for fairness analysis, providing tools to evaluate fairness metrics, visualize results, and implement bias mitigation algorithms. The study focuses on as

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K. Devasenapathy, Arun Padmanabhan,. "Uncovering Bias: Exploring Machine Learning Techniques for Detecting and Mitigating Bias in Data – A Literature Review." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 9 (2023): 776–81. http://dx.doi.org/10.17762/ijritcc.v11i9.8965.

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The presence of Bias in models developed using machine learning algorithms has emerged as a critical issue. This literature review explores the topic of uncovering the existence of bias in data and the application of techniques for detecting and mitigating Bias. The review provides a comprehensive analysis of the existing literature, focusing on pre-processing techniques, post-pre-processing techniques, and fairness constraints employed to uncover and address the existence of Bias in machine learning models. The effectiveness, limitations, and trade-offs of these techniques are examined, highl

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Chu, Charlene, Simon Donato-Woodger, Shehroz Khan, et al. "STRATEGIES TO MITIGATE MACHINE LEARNING BIAS AFFECTING OLDER ADULTS: RESULTS FROM A SCOPING REVIEW." Innovation in Aging 7, Supplement_1 (2023): 717–18. http://dx.doi.org/10.1093/geroni/igad104.2325.

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Abstract Digital ageism, defined as age-related bias in artificial intelligence (AI) and technological systems, has emerged as a significant concern for its potential impact on society, health, equity, and older people’s well-being. This scoping review aims to identify mitigation strategies used in research studies to address age-related bias in machine learning literature. We conducted a scoping review following Arksey & O’Malley’s methodology, and completed a comprehensive search strategy of five databases (Web of Science, CINAHL, EMBASE, IEEE Xplore, and ACM digital library). Articles w

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Kumar, Praveen, and Shailendra Bade. "Accuracy and Bias Mitigation in GenAI / LLM-based Financial Underwriting and Clinical Summarization Systems." International Journal of Science and Research (IJSR) 13, no. 10 (2024): 55–59. http://dx.doi.org/10.21275/sr24930023705.

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Hejazi, Alireza, Michael Lee, David Smith, Rose White, and Rachel Wong. "Scenario Narrative Validator: A Framework for Bias-Free Foresight." Nuts About Leadership 2, no. 1 (2025): 196–207. https://doi.org/10.5281/zenodo.15194886.

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This paper introduces the Scenario Narrative Validator (SNV), a novel methodological tool designed to enhance the robustness of future scenarios by systematically identifying and mitigating cognitive biases (e.g., anchoring, confirmation bias) and research fallacies (e.g., linear projection, planning fallacy). Developed through an interdisciplinary approach integrating cognitive psychology, systems thinking, and foresight methodologies, the SNV provides a structured validation framework organized into five criteria: methodological transparency, cognitive bias mitigation, stakeholder perspectiv

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Erkmen, Cherie Parungo, Lauren Kane, and David T. Cooke. "Bias Mitigation in Cardiothoracic Recruitment." Annals of Thoracic Surgery 111, no. 1 (2021): 12–15. http://dx.doi.org/10.1016/j.athoracsur.2020.07.005.

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Vejsbjerg, Inge, Elizabeth M. Daly, Rahul Nair, and Svetoslav Nizhnichenkov. "Interactive Human-Centric Bias Mitigation." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 21 (2024): 23838–40. http://dx.doi.org/10.1609/aaai.v38i21.30582.

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Bias mitigation algorithms differ in their definition of bias and how they go about achieving that objective. Bias mitigation algorithms impact different cohorts differently and allowing end users and data scientists to understand the impact of these differences in order to make informed choices is a relatively unexplored domain. This demonstration presents an interactive bias mitigation pipeline that allows users to understand the cohorts impacted by their algorithm choice and provide feedback in order to provide a bias mitigated pipeline that most aligns with their goals.

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Wongvorachan, Tarid, Okan Bulut, Joyce Xinle Liu, and Elisabetta Mazzullo. "A Comparison of Bias Mitigation Techniques for Educational Classification Tasks Using Supervised Machine Learning." Information 15, no. 6 (2024): 326. http://dx.doi.org/10.3390/info15060326.

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Machine learning (ML) has become integral in educational decision-making through technologies such as learning analytics and educational data mining. However, the adoption of machine learning-driven tools without scrutiny risks perpetuating biases. Despite ongoing efforts to tackle fairness issues, their application to educational datasets remains limited. To address the mentioned gap in the literature, this research evaluates the effectiveness of four bias mitigation techniques in an educational dataset aiming at predicting students’ dropout rate. The overarching research question is: “How ef

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Aditya Kambhampati. "Mitigating bias in financial decision systems through responsible machine learning." World Journal of Advanced Engineering Technology and Sciences 15, no. 2 (2025): 1415–21. https://doi.org/10.30574/wjaets.2025.15.2.0687.

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Algorithmic bias in financial decision systems perpetuates and sometimes amplifies societal inequities, affecting millions of consumers through discriminatory lending practices, inequitable pricing, and exclusionary fraud detection. Minority borrowers face interest rate premiums that collectively cost communities hundreds of millions of dollars annually, while technological barriers to financial inclusion affect tens of millions of "credit invisible" Americans. This article provides a comprehensive framework for detecting, measuring, and mitigating algorithmic bias across the machine learning

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Featherston, Rebecca Jean, Aron Shlonsky, Courtney Lewis, et al. "Interventions to Mitigate Bias in Social Work Decision-Making: A Systematic Review." Research on Social Work Practice 29, no. 7 (2018): 741–52. http://dx.doi.org/10.1177/1049731518819160.

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Purpose: This systematic review synthesized evidence supporting interventions aimed at mitigating cognitive bias associated with the decision-making of social work professionals. Methods: A systematic search was conducted within 10 social services and health-care databases. Review authors independently screened studies in duplicate against prespecified inclusion criteria, and two review authors undertook data extraction and quality assessment. Results: Four relevant studies were identified. Because these studies were too heterogeneous to conduct meta-analyses, results are reported narratively.

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LEGRAND, CIRIMWAMI, KINJA SYLVIE, and AMANI CHRISTIAN. "BARRIERS TO COMMUNITY PARTICIPATION IN CLIMATE CHANGE MITIGATION THROUGH TREE PLANTING INITIATIVES IN MITI, EASTERN DEMOCRATIC REPUBLIC OF THE CONGO. Title in French OBSTACLES À LA PARTICIPATION DE LA COMMUNAUTÉ À L’ATTÉNUATION DU CHANGEMENT CLIMATIQUE PAR LE BIAIS D’INITIATIVES DE PLANTATION D’ARBRES À MITI, DANS L’EST DE LA RÉPUBLIQUE DÉMOCRATIQUE DU CONGO." Greener Journal of Agricultural Sciences 14, no. 1 (2024): 40–49. https://doi.org/10.15580/gjas.2024.1.112323144.

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Abstract in English Consequences of climate change are felt everywhere leaving anyone indifferent. One of the often-recommended nature-based solutions is tree planting initiatives to mitigate climate change. This study aims to understand the motivations that make farmers, although aware of the harmful effects of climate change and equipped with some major assets, prefer to grow crops than planting trees in Miti, Sud-Kivu province in the east of the Democratic Republic of the Congo. In this way, the study will determine key barriers preventing local community to participate in climate change mi

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Bulut, Solmaz, Mehdi Rostami, Shahla Shokatpour Lotfi, et al. "The Impact of Counselor Bias in Assessment: A Comprehensive Review and Best Practices." Journal of Assessment and Research in Applied Counseling 5, no. 4 (2023): 89–103. http://dx.doi.org/10.61838/kman.jarac.5.4.11.

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Objective: This review article aims to comprehensively explore the impact of counselor bias on assessment processes within the counseling profession. It seeks to identify the types and manifestations of biases, assess their implications on counseling outcomes, and recommend best practices for mitigating these biases to promote more equitable counseling practices. Methods and Materials: A systematic literature review was conducted, examining peer-reviewed articles, books, and conference proceedings published between 1997 and 2023. Databases such as PsycINFO, PubMed, ERIC, and Google Scholar wer

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Sripathi, Madhavi. "Mitigating Data Bias in Healthcare AI: Strategies and Impact on Patient Outcomes." Journal of Advanced Research in Quality Control & Management 08, no. 02 (2023): 01–05. http://dx.doi.org/10.24321/2582.3280.202302.

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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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Van Busum, Kelly, and Shiaofen Fang. "Interactive Mitigation of Biases in Machine Learning Models for Undergraduate Student Admissions." AI 6, no. 7 (2025): 152. https://doi.org/10.3390/ai6070152.

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Bias and fairness issues in artificial intelligence (AI) algorithms are major concerns, as people do not want to use software they cannot trust. Because these issues are intrinsically subjective and context-dependent, creating trustworthy software requires human input and feedback. (1) Introduction: This work introduces an interactive method for mitigating the bias introduced by machine learning models by allowing the user to adjust bias and fairness metrics iteratively to make the model more fair in the context of undergraduate student admissions. (2) Related Work: The social implications of

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Usman Jayadi, Nur Sayidah, and Sri Utami Ady. "PSYCHOLOGY OF STRATEGIC LEADERSHIP: UNVEILING COGNITIVE BIASES AND EMOTIONAL INFLUENCE." MORFAI JOURNAL 4, no. 4 (2025): 1216–24. https://doi.org/10.54443/morfai.v4i4.2297.

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Strategic leadership plays a crucial role in steering organizations through the complexities of a rapidly evolving global landscape. In today's competitive environment, effective leadership transcends informed decision-making and technical expertise, encompassing a deep understanding of the psychological dynamics that influence decisions. This study examines the psychological foundations of strategic leadership, emphasizing the impact of cognitive biases and emotional intelligence on leadership behaviors and decision-making processes. Key cognitive biases such as overconfidence, confirmation b

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Yi, Peiling, and Arkaitz Zubiaga. "ID-XCB: Data-Independent Debiasing for Fair and Accurate Transformer-Based Cyberbullying Detection." Proceedings of the International AAAI Conference on Web and Social Media 19 (June 7, 2025): 2143–54. https://doi.org/10.1609/icwsm.v19i1.35924.

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The use of swear words is a common proxy to collect datasets with cyberbullying incidents, which increases the chances of collecting such events that are otherwise hard to find. However, datasets collected through this means also have a risk of introducing biases in cyberbullying detection models which can learn spurious associations between swear words and the presence of incidents. In this study, we undertake a pioneering study of measuring and mitigating swearing bias in cyberbullying detection tasks. Initially, we employ word-level bias measures to demonstrate the distinctive features rela

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Singh, Richa, Puspita Majumdar, Surbhi Mittal, and Mayank Vatsa. "Anatomizing Bias in Facial Analysis." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 11 (2022): 12351–58. http://dx.doi.org/10.1609/aaai.v36i11.21500.

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Existing facial analysis systems have been shown to yield biased results against certain demographic subgroups. Due to its impact on society, it has become imperative to ensure that these systems do not discriminate based on gender, identity, or skin tone of individuals. This has led to research in the identification and mitigation of bias in AI systems. In this paper, we encapsulate bias detection/estimation and mitigation algorithms for facial analysis. Our main contributions include a systematic review of algorithms proposed for understanding bias, along with a taxonomy and extensive overvi

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Harshila, Gujar. "Addressing Unconscious Bias: Tools and Techniques to Mitigate Bias in the Workplace." Journal of Scientific and Engineering Research 11, no. 4 (2024): 351–54. https://doi.org/10.5281/zenodo.13604160.

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Unconscious bias in the workplace can undermine diversity and inclusion efforts, leading to inequitable outcomes and a less inclusive work environment. This book explores the tools and techniques to identify, address, and mitigate unconscious bias in organizations. By understanding the roots of unconscious bias and implementing targeted strategies, organizations can foster a more inclusive culture, enhance employee engagement, and improve overall performance.

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Gill, Michael J., and Alexandra Pizzuto. "Unwilling to Un-Blame: Whites Who Dismiss Historical Causes of Societal Disparities Also Dismiss Personal Mitigating Information for Black Offenders." Social Cognition 40, no. 1 (2022): 55–87. http://dx.doi.org/10.1521/soco.2022.40.1.55.

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When will racial bias in blame and punishment emerge? Here, we focus on White people's willingness to “un-blame” Black and White offenders upon learning of their unfortunate life histories or biological impairments. We predicted that personal mitigating narratives of Black (but not White) offenders would be ignored by Whites who are societal-level anti-historicists. Societal-level anti-historicists deny that a history of oppression by Whites has shaped current societal-level intergroup disparities. Thus, our prediction centers on how societal-level beliefs relate to bias against individuals. O

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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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Kurmi, Vinod K., Rishabh Sharma, Yash Vardhan Sharma, and Vinay P. Namboodiri. "Gradient Based Activations for Accurate Bias-Free Learning." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 7 (2022): 7255–62. http://dx.doi.org/10.1609/aaai.v36i7.20687.

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Bias mitigation in machine learning models is imperative, yet challenging. While several approaches have been proposed, one view towards mitigating bias is through adversarial learning. A discriminator is used to identify the bias attributes such as gender, age or race in question. This discriminator is used adversarially to ensure that it cannot distinguish the bias attributes. The main drawback in such a model is that it directly introduces a trade-off with accuracy as the features that the discriminator deems to be sensitive for discrimination of bias could be correlated with classification

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Patil, Pranita, and Kevin Purcell. "Decorrelation-Based Deep Learning for Bias Mitigation." Future Internet 14, no. 4 (2022): 110. http://dx.doi.org/10.3390/fi14040110.

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Although deep learning has proven to be tremendously successful, the main issue is the dependency of its performance on the quality and quantity of training datasets. Since the quality of data can be affected by biases, a novel deep learning method based on decorrelation is presented in this study. The decorrelation specifically learns bias invariant features by reducing the non-linear statistical dependency between features and bias itself. This makes the deep learning models less prone to biased decisions by addressing data bias issues. We introduce Decorrelated Deep Neural Networks (DcDNN)

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Kim, Hyo-eun. "Fairness Criteria and Mitigation of AI Bias." Korean Journal of Psychology: General 40, no. 4 (2021): 459–85. http://dx.doi.org/10.22257/kjp.2021.12.40.4.459.

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Park, Souneil, Seungwoo Kang, Sangyoung Chung, and Junehwa Song. "A Computational Framework for Media Bias Mitigation." ACM Transactions on Interactive Intelligent Systems 2, no. 2 (2012): 1–32. http://dx.doi.org/10.1145/2209310.2209311.

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Hopkins, Taylor. "Bias Mitigation: Identifying Barriers and Finding Solutions." Forensic Science International: Synergy 6 (2023): 100420. http://dx.doi.org/10.1016/j.fsisyn.2023.100420.

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Chu, Charlene, Simon Donato-Woodger, Shehroz S. Khan, et al. "Strategies to Mitigate Age-Related Bias in Machine Learning: Scoping Review." JMIR Aging 7 (March 22, 2024): e53564. http://dx.doi.org/10.2196/53564.

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Background Research suggests that digital ageism, that is, age-related bias, is present in the development and deployment of machine learning (ML) models. Despite the recognition of the importance of this problem, there is a lack of research that specifically examines the strategies used to mitigate age-related bias in ML models and the effectiveness of these strategies. Objective To address this gap, we conducted a scoping review of mitigation strategies to reduce age-related bias in ML. Methods We followed a scoping review methodology framework developed by Arksey and O’Malley. The search wa

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Hudson, P., W. J. W. Botzen, H. Kreibich, P. Bubeck, and J. C. J. H. Aerts. "Evaluating the effectiveness of flood damage mitigation measures by the application of propensity score matching." Natural Hazards and Earth System Sciences 14, no. 7 (2014): 1731–47. http://dx.doi.org/10.5194/nhess-14-1731-2014.

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Abstract. The employment of damage mitigation measures (DMMs) by individuals is an important component of integrated flood risk management. In order to promote efficient damage mitigation measures, accurate estimates of their damage mitigation potential are required. That is, for correctly assessing the damage mitigation measures' effectiveness from survey data, one needs to control for sources of bias. A biased estimate can occur if risk characteristics differ between individuals who have, or have not, implemented mitigation measures. This study removed this bias by applying an econometric ev

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Hudson, P., W. J. W. Botzen, H. Kreibich, P. Bubeck, and J. C. J. H. Aerts. "Evaluating the effectiveness of flood damage mitigation measures by the application of Propensity Score Matching." Natural Hazards and Earth System Sciences Discussions 2, no. 1 (2014): 681–723. http://dx.doi.org/10.5194/nhessd-2-681-2014.

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Abstract. The employment of damage mitigation measures by individuals is an important component of integrated flood risk management. In order to promote efficient damage mitigation measures, accurate estimates of their damage mitigation potential are required. That is, for correctly assessing the damage mitigation measures' effectiveness from survey data, one needs to control for sources of bias. A biased estimate can occur if risk characteristics differ between individuals who have, or have not, implemented mitigation measures. This study removed this bias by applying an econometric evaluatio

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Chen, Zhenpeng, Xinyue Li, Jie M. Zhang, et al. "Software Fairness Dilemma: Is Bias Mitigation a Zero-Sum Game?" Proceedings of the ACM on Software Engineering 2, FSE (2025): 1780–801. https://doi.org/10.1145/3729350.

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Fairness is a critical requirement for Machine Learning (ML) software, driving the development of numerous bias mitigation methods. Previous research has identified a leveling-down effect in bias mitigation for computer vision and natural language processing tasks, where fairness is achieved by lowering performance for all groups without benefiting the unprivileged group. However, it remains unclear whether this effect applies to bias mitigation for tabular data tasks, a key area in fairness research with significant real-world applications. This study evaluates eight bias mitigation methods f

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Khan, Shehroz S., Tianyu Shi, Simon Donato-Woodger, and Charlene H. Chu. "Mitigating Digital Ageism in Skin Lesion Detection with Adversarial Learning." Algorithms 18, no. 2 (2025): 55. https://doi.org/10.3390/a18020055.

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Deep learning-based medical image classification models have been shown to exhibit race-, gender-, and age-related biases towards certain demographic attributes. Existing bias mitigation methods primarily focus on learning debiased models, which may not guarantee that all sensitive information is removed and usually targets discrete sensitive attributes. In order to address age-related bias in these models, we introduce a novel method called Mitigating Digital Ageism using Adversarially Learned Representation (MA-ADReL), which aims to achieve fairness for age as a sensitive continuous attribut

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Cai, Zhenyu. "Quantum Error Mitigation using Symmetry Expansion." Quantum 5 (September 21, 2021): 548. http://dx.doi.org/10.22331/q-2021-09-21-548.

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Even with the recent rapid developments in quantum hardware, noise remains the biggest challenge for the practical applications of any near-term quantum devices. Full quantum error correction cannot be implemented in these devices due to their limited scale. Therefore instead of relying on engineered code symmetry, symmetry verification was developed which uses the inherent symmetry within the physical problem we try to solve. In this article, we develop a general framework named symmetry expansion which provides a wide spectrum of symmetry-based error mitigation schemes beyond symmetry verifi

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Katoch, Naman. "Addressing Bias in AI: Ethical Concerns, Challenges, and Mitigation Strategies." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 04 (2025): 1–9. https://doi.org/10.55041/ijsrem45370.

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Abstract - Artificial Intelligence (AI) has revolutionized most industries, but it is also susceptible to bias, which gives rise to ethical problems and unintended consequences. AI bias can arise from biased training data, flawed algorithms, or institutional biases, which give rise to discriminatory judgments in healthcare, finance, law enforcement, and the workplace. This paper explores the reasons behind AI bias, its ethical dimensions, and how biased algorithms affect society. Besides, it also covers various mitigation strategies, including data preprocessing techniques, fair- aware algorit

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Bergemann, Rito, Sugandh Sharma, Jackie Vanderpuye-Orle, and Jean-Etienne Poirrier. "PP25 Artificial Intelligence In Healthcare Decision-Making: Addressing Challenges, Ethical Considerations, And Bias." International Journal of Technology Assessment in Health Care 40, S1 (2024): S64. https://doi.org/10.1017/s0266462324001983.

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IntroductionArtificial intelligence (AI) is transforming healthcare decision-making, particularly in evidence evaluation and health technology assessment (HTA). This research explores challenges and ethical considerations associated with AI implementation, and biases. It highlights the need for diverse stakeholder perspectives and collaboration to ensure responsible AI use. Through transparency, accountability, and bias mitigation, AI has the potential to revolutionize decision-making and improve patient care while promoting equitable outcomes.MethodsLiterature research was conducted, includin

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Rotenberg, Wendy. "Mitigation of U.S. Home Bias in the Valuation of Canadian Natural Resource Firms: Choice of Reporting and Transaction Currency." Multinational Finance Journal 17, no. 3/4 (2013): 201–41. http://dx.doi.org/10.17578/17-3/4-4.

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Christensen-Branum, Lezlie, Ashley Strong, and Cindy D'On Jones. "Mitigating Myside Bias in Argumentation." Journal of Adolescent & Adult Literacy 62, no. 4 (2018): 435–45. http://dx.doi.org/10.1002/jaal.915.

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Dunbar, Norah E., Matthew L. Jensen, Claude H. Miller, et al. "Mitigation of Cognitive Bias with a Serious Game." International Journal of Game-Based Learning 7, no. 4 (2017): 86–100. http://dx.doi.org/10.4018/ijgbl.2017100105.

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One of the benefits of using digital games for education is that games can provide feedback for learners to assess their situation and correct their mistakes. We conducted two studies to examine the effectiveness of different feedback design (timing, duration, repeats, and feedback source) in a serious game designed to teach learners about cognitive biases. We also compared the digital game-based learning condition to a professional training video. Overall, the digital game was significantly more effective than the video condition. Longer durations and repeats improve the effects on bias-mitig

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Guan, Maime, and Joachim Vandekerckhove. "A Bayesian approach to mitigation of publication bias." Psychonomic Bulletin & Review 23, no. 1 (2015): 74–86. http://dx.doi.org/10.3758/s13423-015-0868-6.

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Shahul Hameed, Mohamed Ashik, Asifa Mehmood Qureshi, and Abhishek Kaushik. "Bias Mitigation via Synthetic Data Generation: A Review." Electronics 13, no. 19 (2024): 3909. http://dx.doi.org/10.3390/electronics13193909.

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Artificial intelligence (AI) is widely used in healthcare applications to perform various tasks. Although these models have great potential to improve the healthcare system, they have also raised significant ethical concerns, including biases that increase the risk of health disparities in medical applications. The under-representation of a specific group can lead to bias in the datasets that are being replicated in the AI models. These disadvantaged groups are disproportionately affected by bias because they may have less accurate algorithmic forecasts or underestimate the need for treatment.

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