Relevant bibliographies by topics / Mitigation des biais

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Mitigation des biais'

Author: Grafiati
Published: 8 June 2024
Last updated: 31 July 2025

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Journal articles on the topic "Mitigation des biais"

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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>&nbsp;</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>&nbsp;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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Dissertations / Theses on the topic "Mitigation des biais"

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Le, Berre Guillaume. "Vers la mitigation des biais en traitement neuronal des langues." Electronic Thesis or Diss., Université de Lorraine, 2023. http://www.theses.fr/2023LORR0074.

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Il est de notoriété que les modèles d'apprentissage profond sont sensibles aux biais qui peuvent être présents dans les données utilisées pour l'apprentissage. Ces biais qui peuvent être définis comme de l'information inutile ou préjudiciable pour la tâche considérée, peuvent être de différentes natures: on peut par exemple trouver des biais dans les styles d'écriture utilisés, mais aussi des biais bien plus problématiques portant sur le sexe ou l'origine ethnique des individus. Ces biais peuvent provenir de différentes sources, comme des annotateurs ayant créé les bases de données, ou bien du
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Gadala, M. "Automation bias : exploring causal mechanisms and potential mitigation strategies." Thesis, City, University of London, 2017. http://openaccess.city.ac.uk/17889/.

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Automated decision support tools are designed to aid users and improve their performance in certain tasks by providing advice in the form of prompts, alarms, assessments, or recommendations. However, recent evidence suggests that sometimes use of such tools introduces decision errors that are not made without the tool. We refer to this phenomenon as “automation bias” (AB), resulting in a broader definition of this term than used by many authors. Sometimes, such automation-induced errors can even result in overall performance (in terms of correct decisions) which is actually worse with the tool
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Fyrvald, Johanna. "Mitigating algorithmic bias in Artificial Intelligence systems." Thesis, Uppsala universitet, Matematiska institutionen, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-388627.

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Artificial Intelligence (AI) systems are increasingly used in society to make decisions that can have direct implications on human lives; credit risk assessments, employment decisions and criminal suspects predictions. As public attention has been drawn towards examples of discriminating and biased AI systems, concerns have been raised about the fairness of these systems. Face recognition systems, in particular, are often trained on non-diverse data sets where certain groups often are underrepresented in the data. The focus of this thesis is to provide insights regarding different aspects that
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Salomon, Sophie. "Bias Mitigation Techniques and a Cost-Aware Framework for Boosted Ranking Algorithms." Case Western Reserve University School of Graduate Studies / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=case1586450345426827.

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Frick, Eric Christopher. "Mitigation of magnetic interference and compensation of bias drift in inertial sensors." Thesis, University of Iowa, 2015. https://ir.uiowa.edu/etd/5472.

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Magnetic interference in the motion capture environment is caused primarily by ferromagnetic objects and current-carrying devices disturbing the ambient, geomagnetic field. Inertial sensors gather magnetic data to determine and stabilize their global heading estimates, and such magnetic field disturbances alter heading estimates. This decreases orientation accuracy and therefore decreases motion capture accuracy. The often used Kalman Filter approach deals with magnetic interference by ignoring the magnetic data during periods interference is encountered, but this method is only effective when
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Taylor, Stephen Luke. "Analyzing methods of mitigating initialization bias in transportation simulation models." Thesis, Georgia Institute of Technology, 2010. http://hdl.handle.net/1853/37208.

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All computer simulation models require some form of initialization before their outputs can be considered meaningful. Simulation models are typically initialized in a particular, often "empty" state and therefore must be "warmed-up" for an unknown amount of simulation time before reaching a "quasi-steady-state" representative of the systems' performance. The portion of the output series that is influenced by the arbitrary initialization is referred to as the initial transient and is a widely recognized problem in simulation analysis. Although several methods exist for removing the initial tr
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Sweeney, Christopher(Christopher J. ). M. Eng Massachusetts Institute of Technology. "Understanding and mitigating unintended demographic bias in machine learning systems." Thesis, Massachusetts Institute of Technology, 2019. https://hdl.handle.net/1721.1/123131.

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This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.<br>Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2019<br>Cataloged from student-submitted PDF version of thesis.<br>Includes bibliographical references (pages 81-84).<br>Machine Learning is becoming more and more influential in our society. Algorithms that learn from data are streamlining tasks in domains like employment, banking, education, heath care, social media, etc. Unfortunat
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Isumbingabo, Emma Francoise. "Evaluation and mitigation of the undesired effect of DC bias on inverter power transformer." Master's thesis, University of Cape Town, 2009. http://hdl.handle.net/11427/5202.

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Inverters have traditionally been used mostly in standalone systems (non-grid connected), Uninterruptible Power Supplies (UPS) and, more recently, in distributed generated systems (DGs). DG systems are based on grid connected inverters and are increasingly being connected to utility grids to convert renewable energy sources to the utility grids. Such sources are likely to have a significant impact in the future in meeting the electricity demands of industry and domestic consumption. Common DGs utilize DC power sources such as fuel cells, batteries, photovoltaic (solar) power, and wind power. M
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Ashton, Christie. "A critical review of approaches to mitigating bias in fingerprint identification." Thesis, Ashton, Christie (2018) A critical review of approaches to mitigating bias in fingerprint identification. Masters by Coursework thesis, Murdoch University, 2018. https://researchrepository.murdoch.edu.au/id/eprint/41502/.

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Fingerprint identification is a discipline used within forensic science which assists in criminal investigations1, 2. The process of fingerprint identification involves the comparison of crime scene evidence with known exemplars. This form of examination is heavily reliant on human examiners and their conclusions as to whether there is an identification, exclusion or insufficient information to identify3. This form of forensic identification has become a focus due to concern of the effects of cognitive bias on examiners conclusions. Concerns have prompted research into the area of approaches t
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Lowery, Meghan Rachelle. "MITIGATING SEX BIAS IN COMPENSATION DECISIONS: THE ROLE OF COMPARATIVE DATA." OpenSIUC, 2010. https://opensiuc.lib.siu.edu/dissertations/231.

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Gender differences in salaries are prominent in most fields. Several laws exist to decrease the amount of pay discrimination and provide remedies for discriminatory organizational behaviors, yet these laws have proven insufficient to eradicate pay inequities. One source for such discrimination in pay stems from the evaluation of employee performance. Performance appraisal systems can be biased in very small ways that yield larger negative effects on later employment-related decisions, such as compensation. The goal of this study was to examine decision-making processes and conclusions raters m
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Books on the topic "Mitigation des biais"

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Whitesmith, Martha. Cognitive Bias in Intelligence Analysis. Edinburgh University Press, 2020. http://dx.doi.org/10.3366/edinburgh/9781474466349.001.0001.

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Belief, Bias and Intelligence outlines an approach for reducing the risk of cognitive biases impacting intelligence analysis that draws from experimental research in the social sciences. It critiques the reliance of Western intelligence agencies on the use of a method for intelligence analysis developed by the CIA in the 1990’s, the Analysis of Competing Hypotheses (ACH). The book shows that the theoretical basis of the ACH method is significantly flawed, and that there is no empirical basis for the use of ACH in mitigating cognitive biases. It puts ACH to the test in an experimental setting a
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Marshall, Brandeis Hill, and Carlotta A. Berry. Mitigating Bias in Machine Learning. McGraw-Hill Education, 2024.

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Unconscious Bias : : Increasing Awareness, Providing Training and Mitigating the Impact of Bias in Workplace Investigations. Independent Publisher, 2020.

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Knell, Milo. Keous: A Tool for Mitigating Media Bias Using Artificial Intelligence. Blurb, 2020.

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Kenski, Kate. Overcoming Confirmation and Blind Spot Biases When Communicating Science. Edited by Kathleen Hall Jamieson, Dan M. Kahan, and Dietram A. Scheufele. Oxford University Press, 2017. http://dx.doi.org/10.1093/oxfordhb/9780190497620.013.40.

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This chapter focuses on two biases that lead people away from evaluating evidence and scientific studies impartially—confirmation bias and bias blind spot. The chapter first discusses different ways in which people process information and reviews the costs and benefits of utilizing cognitive shortcuts in decision making. Next, two common cognitive biases, confirmation bias and bias blind spot, are explained. Then the literature on “debiasing” is explored. Finally, the implications of confirmation bias and bias blind spot in the context of communicating about science are examined, and an agenda
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Forecasting with Out-Liars: Mitigating Blame, Bias, and Apathy in Your Planning Process to Drive Meaningful and Sustainable Financial Improvements. Mindstir Media, 2021.

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Dunbar, Edward W., Amalio Blanco, and Desirée A. Crèvecoeur-MacPhail, eds. The Psychology of Hate Crimes as Domestic Terrorism. Praeger, 2016. http://dx.doi.org/10.5040/9798216983064.

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In this three-volume set, an international team of experts involved in the research, management, and mitigation of hate-motivated violence examines and explains hate crimes in the United States and around the globe, drawing comparisons between countries as well as between hate crimes overall and domestic terrorism. The Psychology of Hate Crimes as Domestic Terrorism: U.S. and Global Issuestakes a hard look at hate crimes both domestically and internationally, enabling readers to see similarities and disparities as well as to make the connections between hate crimes and domestic terrorism. The
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Dunbar, Edward W., Amalio Blanco, and Desirée A. Crèvecoeur-MacPhail, eds. The Psychology of Hate Crimes as Domestic Terrorism. Praeger, 2016. http://dx.doi.org/10.5040/9798216983040.

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In this three-volume set, an international team of experts involved in the research, management, and mitigation of hate-motivated violence examines and explains hate crimes in the United States and around the globe, drawing comparisons between countries as well as between hate crimes overall and domestic terrorism. The Psychology of Hate Crimes as Domestic Terrorism: U.S. and Global Issuestakes a hard look at hate crimes both domestically and internationally, enabling readers to see similarities and disparities as well as to make the connections between hate crimes and domestic terrorism. The
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Dunbar, Edward W., Amalio Blanco, and Desirée A. Crévecoeur-MacPhail, eds. Psychology of Hate Crimes as Domestic Terrorism. Praeger, 2016. http://dx.doi.org/10.5040/9798216983057.

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In this three-volume set, an international team of experts involved in the research, management, and mitigation of hate-motivated violence examines and explains hate crimes in the United States and around the globe, drawing comparisons between countries as well as between hate crimes overall and domestic terrorism. The Psychology of Hate Crimes as Domestic Terrorism: U.S. and Global Issuestakes a hard look at hate crimes both domestically and internationally, enabling readers to see similarities and disparities as well as to make the connections between hate crimes and domestic terrorism. The
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Sandis, Elizabeth. Early Modern Drama at the Universities. Oxford University Press, 2022. http://dx.doi.org/10.1093/oso/9780192857132.001.0001.

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This is the first history of Oxford and Cambridge drama in the Tudor and Stuart period. It guides the reader through the theatrical experiences of students at university in early modern England, following them on the journey from schoolboys to scholars to graduates in the workplace. Early Modern Drama at the Universities is structured to make the subject as accessible as possible, mitigating the difficulties of this sizeable and complex body of evidence. The hundreds of plays we have inherited from Oxford and Cambridge are steeped in Classical culture, and the academic establishment’s bias aga
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Book chapters on the topic "Mitigation des biais"

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Balan, Anil. "Bias Mitigation and Fairness." In AI and Legal Education. Routledge, 2025. https://doi.org/10.4324/9781003607397-3.

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Nandan Prasad, Aditya. "Ethical Implications and Bias Mitigation." In Introduction to Data Governance for Machine Learning Systems. Apress, 2024. https://doi.org/10.1007/979-8-8688-1023-7_5.

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Marani, Badr-Eddine, Mohamed Hanini, Nihitha Malayarukil, Stergios Christodoulidis, Maria Vakalopoulou, and Enzo Ferrante. "ViG-Bias: Visually Grounded Bias Discovery and Mitigation." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-73202-7_24.

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Formanek, Kay. "Surfacing and Mitigating Bias." In Beyond D&I. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-75336-8_6.

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Espinosa Zarlenga, Mateo, Swami Sankaranarayanan, Jerone T. A. Andrews, Zohreh Shams, Mateja Jamnik, and Alice Xiang. "Efficient Bias Mitigation Without Privileged Information." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-73220-1_9.

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Pat, Croskerry. "Cognitive Bias Mitigation: Becoming Better Diagnosticians." In Diagnosis. CRC Press, 2017. http://dx.doi.org/10.1201/9781315116334-15.

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Nagar, Atulya K., Anand Jha, Ayush Katiyar, Devesh Prajapati, and Kapil Sharma. "Bias in AI: Causes and Mitigation." In A Compendium of Responsible Artificial Intelligence. CRC Press, 2025. https://doi.org/10.1201/9781003514497-4.

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Ahmed, Uzair, and Kostas Stefanidis. "Multi-attribute Bias Mitigation in Recommender Systems." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-83472-1_22.

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Wang, Guanchu, Mengnan Du, Ninghao Liu, Na Zou, and Xia Hu. "Mitigating Algorithmic Bias with Limited Annotations." In Machine Learning and Knowledge Discovery in Databases: Research Track. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-43415-0_15.

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Tarallo, Mark. "Dancing with Myself: Self-Management and Bias Mitigation." In Modern Management and Leadership. CRC Press, 2021. http://dx.doi.org/10.1201/9781003095620-6.

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Conference papers on the topic "Mitigation des biais"

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Akhonda, Mohammad Abu Baker S., Alexis Burgon, Kenny Cha, Nicholas Petrick, and Ravi K. Samala. "Label-free AI bias mitigation." In Computer-Aided Diagnosis, edited by Susan M. Astley and Axel Wismüller. SPIE, 2025. https://doi.org/10.1117/12.3047437.

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Bayasi, Nourhan, Jamil Fayyad, Ghassan Hamarneh, Rafeef Garbi, and Homayoun Najjaran. "Debiasify: Self-Distillation for Unsupervised Bias Mitigation." In 2025 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV). IEEE, 2025. https://doi.org/10.1109/wacv61041.2025.00319.

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Kose, O. Deniz, Gonzalo Mateos, and Yanning Shen. "Filtering as Rewiring for Bias Mitigation on Graphs." In 2024 IEEE 13th Sensor Array and Multichannel Signal Processing Workshop (SAM). IEEE, 2024. http://dx.doi.org/10.1109/sam60225.2024.10636593.

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Kumari, Gitanjali, Anubhav Sinha, and Asif Ekbal. "Unintended Bias Detection and Mitigation in Misogynous Memes." In Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers). Association for Computational Linguistics, 2024. https://doi.org/10.18653/v1/2024.eacl-long.166.

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Bergstrand, Selma, and Björn Gambäck. "Detecting and Mitigating LGBTQIA+ Bias in Large Norwegian Language Models." In Proceedings of the 5th Workshop on Gender Bias in Natural Language Processing (GeBNLP). Association for Computational Linguistics, 2024. http://dx.doi.org/10.18653/v1/2024.gebnlp-1.22.

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Kotwal, Ketan, and Sébastien Marcel. "Demographic Fairness Transformer for Bias Mitigation in Face Recognition." In 2024 IEEE International Joint Conference on Biometrics (IJCB). IEEE, 2024. http://dx.doi.org/10.1109/ijcb62174.2024.10744457.

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Hou, Xuege, Yali Li, and Shengjin Wang. "Attention Augmented Structure-centric Bias Mitigation with Feature Disentanglement." In ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2025. https://doi.org/10.1109/icassp49660.2025.10889443.

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Tiba, Mahdi, and Hadi Saboohi. "Gender Bias Mitigation of Persian Stereotypical Words Using FastText." In 2025 11th International Conference on Web Research (ICWR). IEEE, 2025. https://doi.org/10.1109/icwr65219.2025.11006166.

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De Coninck, Sander, Sam Leroux, and Pieter Simoens. "Mitigating Bias Using Model-Agnostic Data Attribution." In 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). IEEE, 2024. http://dx.doi.org/10.1109/cvprw63382.2024.00028.

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Ohi, Masanari, Masahiro Kaneko, Ryuto Koike, Mengsay Loem, and Naoaki Okazaki. "Likelihood-based Mitigation of Evaluation Bias in Large Language Models." In Findings of the Association for Computational Linguistics ACL 2024. Association for Computational Linguistics, 2024. http://dx.doi.org/10.18653/v1/2024.findings-acl.193.

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Reports on the topic "Mitigation des biais"

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Serakos, Demetrios, John E. Gray, and Hazim Youssef. Topics in Mitigating Radar Bias. Defense Technical Information Center, 2012. http://dx.doi.org/10.21236/ada604137.

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Williams, Samantha. An Experiment in Demonstrating and Mitigating Bias in Image Classification. Iowa State University, 2021. http://dx.doi.org/10.31274/cc-20240624-178.

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Dolabella, Marcelo, and Mauricio Mesquita Moreira. Fighting Global Warming: Is Trade Policy in Latin America and the Caribbean a Help or a Hindrance? Inter-American Development Bank, 2022. http://dx.doi.org/10.18235/0004426.

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The dire prospects of global warming have been increasing the pressure on policymakers to use trade policy as a mitigation tool, challenging trade economists canonical “targeting principle.” Even though the justifications for this stance remain as valid as ever, it no longer seems feasible in a world that is already engaging actively in using trade policy for climate purposes. However, the search for second-best solutions remains warranted. In this paper, we focus on the climate benefits of tariff reform for a broad sample of Latin American and Caribbean countries, drawing on Shapiros (2021) i
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Tipton, Kelley, Brian F. Leas, Emilia Flores, et al. Impact of Healthcare Algorithms on Racial and Ethnic Disparities in Health and Healthcare. Agency for Healthcare Research and Quality (AHRQ), 2023. http://dx.doi.org/10.23970/ahrqepccer268.

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Objectives. To examine the evidence on whether and how healthcare algorithms (including algorithm-informed decision tools) exacerbate, perpetuate, or reduce racial and ethnic disparities in access to healthcare, quality of care, and health outcomes, and examine strategies that mitigate racial and ethnic bias in the development and use of algorithms. Data sources. We searched published and grey literature for relevant studies published between January 2011 and February 2023. Based on expert guidance, we determined that earlier articles are unlikely to reflect current algorithms. We also hand-se
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Panek, Krol, and Huth. PR-312-12208-R03 USEPA AERMOD Plume Rise and Volume Formulations and Implications for Existing RICE. Pipeline Research Council International, Inc. (PRCI), 2016. http://dx.doi.org/10.55274/r0010858.

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AERMOD is the EPA recommended dispersion modeling tool for evaluating impacts from typical compressor station engine sources. This is a companion document to two previous PRCI reports that addressed AERMOD Fortran compiler issues and a subsequent report that examined AERMOD Plume Volume Molar ratio Method (PVMRM) issues that lead to conservative model over-predictions. This report further explores AERMOD plume rise and volume estimates as a possible cause or contributor of model over-prediction and resulting plume chemistry concerns. AERMOD over-prediction bias has significant negative implica
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Carter, Sara, Jane Griffin, Samantha Lako, Cheryl Harewood, Lisa Kessler, and Elizabeth Parish. The impacts of COVID-19 on schools’ willingness to participate in research. RTI Press, 2024. http://dx.doi.org/10.3768/rtipress.2024.rb.0036.2401.

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COVID-19 had significant impacts on the field of education and, in turn, on school-based research. During this unprecedented time, nearly all schools closed, disrupting learning as schools shifted to a virtual format. Addressing the lasting effects of school closures is a major challenge in the post-pandemic education climate. Educators indicate these challenges have limited their willingness or ability to participate in research. We analyzed over 700 reasons for refusal in four recent education studies to examine the effects of COVID-19 on school-based research. About 4% of education leaders
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Bray, Jonathan, Ross Boulanger, Misko Cubrinovski, et al. U.S.—New Zealand— Japan International Workshop, Liquefaction-Induced Ground Movement Effects, University of California, Berkeley, California, 2-4 November 2016. Pacific Earthquake Engineering Research Center, University of California, Berkeley, CA, 2017. http://dx.doi.org/10.55461/gzzx9906.

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There is much to learn from the recent New Zealand and Japan earthquakes. These earthquakes produced differing levels of liquefaction-induced ground movements that damaged buildings, bridges, and buried utilities. Along with the often spectacular observations of infrastructure damage, there were many cases where well-built facilities located in areas of liquefaction-induced ground failure were not damaged. Researchers are working on characterizing and learning from these observations of both poor and good performance. The “Liquefaction-Induced Ground Movements Effects” workshop provided an opp
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Eslava, Marcela, Alessandro Maffioli, and Marcela Meléndez Arjona. Second-tier Government Banks and Access to Credit: Micro-Evidence from Colombia. Inter-American Development Bank, 2012. http://dx.doi.org/10.18235/0011364.

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Government-owned development banks have often been justified by the need to respond to financial market imperfections that hinder the establishment and growth of promising businesses, and as a result, stifle economic development more generally. However, evidence on the effectiveness of these banks in mitigating financial constraints is still lacking. To fill this gap, this paper analyzes the impact of Bancoldex, Colombia's publicly owned development bank, on access to credit. It uses a unique dataset that contains key characteristics of all loans issued to businesses in Colombia, including the
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Avis, William. Refugee and Mixed Migration Displacement from Afghanistan. Institute of Development Studies (IDS), 2021. http://dx.doi.org/10.19088/k4d.2022.002.

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This rapid literature review summarises evidence and key lessons that exist regarding previous refugee and mixed migration displacement from Afghanistan to surrounding countries. The review identified a diverse literature that explored past refugee and mixed migration, with a range of quantitative and qualitative studies identified. A complex and fluid picture is presented with waves of mixed migration (both outflow and inflow) associated with key events including the: Soviet–Afghan War (1979–1989); Afghan Civil War (1992–96); Taliban Rule (1996–2001); War in Afghanistan (2001–2021). A context
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