Littérature scientifique sur le sujet « Mitigation des biais »
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Articles de revues sur le sujet "Mitigation des biais"
Philipps, Nathalia, Pierre P. Kastendeuch et 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.
Texte intégralRahmawati, Fitriana, et 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 (30 décembre 2023) : 4961–76. http://dx.doi.org/10.55927/eajmr.v2i12.6896.
Texte intégralDjebrouni, Yasmine, Nawel Benarba, Ousmane Touat, Pasquale De Rosa, Sara Bouchenak, Angela Bonifati, Pascal Felber, Vania Marangozova et Valerio Schiavoni. « Bias Mitigation in Federated Learning for Edge Computing ». Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 7, no 4 (19 décembre 2023) : 1–35. http://dx.doi.org/10.1145/3631455.
Texte intégralGallaher, Joshua P., Alexander J. Kamrud et Brett J. Borghetti. « Detection and Mitigation of Inefficient Visual Searching ». Proceedings of the Human Factors and Ergonomics Society Annual Meeting 64, no 1 (décembre 2020) : 47–51. http://dx.doi.org/10.1177/1071181320641015.
Texte intégralLee, Yu-Hao, Norah E. Dunbar, Claude H. Miller, Brianna L. Lane, Matthew L. Jensen, Elena Bessarabova, Judee K. Burgoon et al. « Training Anchoring and Representativeness Bias Mitigation Through a Digital Game ». Simulation & ; Gaming 47, no 6 (20 août 2016) : 751–79. http://dx.doi.org/10.1177/1046878116662955.
Texte intégralK. 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 (30 octobre 2023) : 776–81. http://dx.doi.org/10.17762/ijritcc.v11i9.8965.
Texte intégralChu, Charlene, Simon Donato-Woodger, Shehroz Khan, Kathleen Leslie, Tianyu Shi, Rune Nyrup et Amanda Grenier. « STRATEGIES TO MITIGATE MACHINE LEARNING BIAS AFFECTING OLDER ADULTS : RESULTS FROM A SCOPING REVIEW ». Innovation in Aging 7, Supplement_1 (1 décembre 2023) : 717–18. http://dx.doi.org/10.1093/geroni/igad104.2325.
Texte intégralFeatherston, Rebecca Jean, Aron Shlonsky, Courtney Lewis, My-Linh Luong, Laura E. Downie, Adam P. Vogel, Catherine Granger, Bridget Hamilton et Karyn Galvin. « Interventions to Mitigate Bias in Social Work Decision-Making : A Systematic Review ». Research on Social Work Practice 29, no 7 (23 décembre 2018) : 741–52. http://dx.doi.org/10.1177/1049731518819160.
Texte intégralErkmen, Cherie Parungo, Lauren Kane et David T. Cooke. « Bias Mitigation in Cardiothoracic Recruitment ». Annals of Thoracic Surgery 111, no 1 (janvier 2021) : 12–15. http://dx.doi.org/10.1016/j.athoracsur.2020.07.005.
Texte intégralVejsbjerg, Inge, Elizabeth M. Daly, Rahul Nair et Svetoslav Nizhnichenkov. « Interactive Human-Centric Bias Mitigation ». Proceedings of the AAAI Conference on Artificial Intelligence 38, no 21 (24 mars 2024) : 23838–40. http://dx.doi.org/10.1609/aaai.v38i21.30582.
Texte intégralThèses sur le sujet "Mitigation des biais"
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.
Texte intégralIt is well known that deep learning models are sensitive to biases that may be present in the data used for training. These biases, which can be defined as useless or detrimental information for the task in question, can be of different kinds: one can, for example, find biases in the writing styles used, but also much more problematic biases relating to the sex or ethnic origin of individuals. These biases can come from different sources, such as annotators who created the databases, or from the annotation process itself. My thesis deals with the study of these biases and, in particular, is organized around the mitigation of the effects of biases on the training of Natural Language Processing (NLP) models. In particular, I have worked a lot with pre-trained models such as BERT, RoBERTa or UnifiedQA which have become essential in recent years in all areas of NLP and which, despite their extensive pre-training, are very sensitive to these bias problems.My thesis is organized in three parts, each presenting a different way of managing the biases present in the data. The first part presents a method allowing to use the biases present in an automatic summary database in order to increase the variability and the controllability of the generated summaries. Then, in the second part, I am interested in the automatic generation of a training dataset for the multiple-choice question-answering task. The advantage of such a generation method is that it makes it possible not to call on annotators and therefore to eliminate the biases coming from them in the data. Finally, I am interested in training a multitasking model for optical text recognition. I show in this last part that it is possible to increase the performance of our models by using different types of data (handwritten and typed) during their training
Gadala, M. « Automation bias : exploring causal mechanisms and potential mitigation strategies ». Thesis, City, University of London, 2017. http://openaccess.city.ac.uk/17889/.
Texte intégralFyrvald, 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.
Texte intégralSalomon, 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.
Texte intégralFrick, 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.
Texte intégralTaylor, Stephen Luke. « Analyzing methods of mitigating initialization bias in transportation simulation models ». Thesis, Georgia Institute of Technology, 2010. http://hdl.handle.net/1853/37208.
Texte intégralSweeney, 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.
Texte intégralThesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2019
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 81-84).
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. Unfortunately, machine learning models are very susceptible to unintended bias, resulting in unfair and discriminatory algorithms with the power to adversely impact society. This unintended bias is usually subtle, emanating from many different sources and taking on many forms. This thesis will focus on understanding how unfair biases with respect to various demographic groups show up in machine learning systems. Furthermore, we develop multiple techniques to mitigate unintended demographic bias at various stages of typical machine learning pipelines. Using Natural Language Processing as a framework, we show substantial improvements in fairness for standard machine learning systems, when using our bias mitigation techniques.
by Christopher Sweeney.
M. Eng.
M.Eng. Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science
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.
Texte intégralAshton, 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/.
Texte intégralLowery, Meghan Rachelle. « MITIGATING SEX BIAS IN COMPENSATION DECISIONS : THE ROLE OF COMPARATIVE DATA ». OpenSIUC, 2010. https://opensiuc.lib.siu.edu/dissertations/231.
Texte intégralLivres sur le sujet "Mitigation des biais"
Whitesmith, Martha. Cognitive Bias in Intelligence Analysis. Edinburgh University Press, 2020. http://dx.doi.org/10.3366/edinburgh/9781474466349.001.0001.
Texte intégralKenski, Kate. Overcoming Confirmation and Blind Spot Biases When Communicating Science. Sous la direction de Kathleen Hall Jamieson, Dan M. Kahan et Dietram A. Scheufele. Oxford University Press, 2017. http://dx.doi.org/10.1093/oxfordhb/9780190497620.013.40.
Texte intégralMink, John. Forecasting with Out-Liars : Mitigating Blame, Bias, and Apathy in Your Planning Process to Drive Meaningful and Sustainable Financial Improvements. Mindstir Media, 2021.
Trouver le texte intégralSandis, Elizabeth. Early Modern Drama at the Universities. Oxford University Press, 2022. http://dx.doi.org/10.1093/oso/9780192857132.001.0001.
Texte intégralChapitres de livres sur le sujet "Mitigation des biais"
Formanek, Kay. « Surfacing and Mitigating Bias ». Dans Beyond D&I, 137–62. Cham : Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-75336-8_6.
Texte intégralPat, Croskerry. « Cognitive Bias Mitigation : Becoming Better Diagnosticians ». Dans Diagnosis, 257–87. Boca Raton : Taylor & Francis, 2017. : CRC Press, 2017. http://dx.doi.org/10.1201/9781315116334-15.
Texte intégralWang, Guanchu, Mengnan Du, Ninghao Liu, Na Zou et Xia Hu. « Mitigating Algorithmic Bias with Limited Annotations ». Dans Machine Learning and Knowledge Discovery in Databases : Research Track, 241–58. Cham : Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-43415-0_15.
Texte intégralTarallo, Mark. « Dancing with Myself : Self-Management and Bias Mitigation ». Dans Modern Management and Leadership, 27–34. Boca Raton : CRC Press, 2021. http://dx.doi.org/10.1201/9781003095620-6.
Texte intégralWu, Ye, Yuanjing Feng, Dinggang Shen et Pew-Thian Yap. « Penalized Geodesic Tractography for Mitigating Gyral Bias ». Dans Medical Image Computing and Computer Assisted Intervention – MICCAI 2018, 12–19. Cham : Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-00931-1_2.
Texte intégralLi, Yinxiao. « Mitigating Position Bias in Hotels Recommender Systems ». Dans Communications in Computer and Information Science, 74–84. Cham : Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-37249-0_6.
Texte intégralSharma, Ashish, Rajeshwar Mehrotra et Fiona Johnson. « A New Framework for Modeling Future Hydrologic Extremes : Nested Bias Correction as a Precursor to Stochastic Rainfall Downscaling ». Dans Climate Change Modeling, Mitigation, and Adaptation, 357–86. Reston, VA : American Society of Civil Engineers, 2013. http://dx.doi.org/10.1061/9780784412718.ch13.
Texte intégralCorliss, David J. « Designing Against Bias : Identifying and Mitigating Bias in Machine Learning and AI ». Dans Lecture Notes in Networks and Systems, 411–18. Cham : Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-47715-7_28.
Texte intégralShi, Sheng, Shanshan Wei, Zhongchao Shi, Yangzhou Du, Wei Fan, Jianping Fan, Yolanda Conyers et Feiyu Xu. « Algorithm Bias Detection and Mitigation in Lenovo Face Recognition Engine ». Dans Natural Language Processing and Chinese Computing, 442–53. Cham : Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-60457-8_36.
Texte intégralWang, Xing, Guoqiang Zhao, Feng Zhang et Yongan Yang. « Characterization and Mitigation of BeiDou Triple-Frequency Code Multipath Bias ». Dans Lecture Notes in Electrical Engineering, 467–80. Singapore : Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-0014-1_39.
Texte intégralActes de conférences sur le sujet "Mitigation des biais"
Calegari, Roberta, Gabriel G. Castañé, Michela Milano et Barry O'Sullivan. « Assessing and Enforcing Fairness in the AI Lifecycle ». Dans Thirty-Second International Joint Conference on Artificial Intelligence {IJCAI-23}. California : International Joint Conferences on Artificial Intelligence Organization, 2023. http://dx.doi.org/10.24963/ijcai.2023/735.
Texte intégralCheong, Jiaee, Selim Kuzucu, Sinan Kalkan et Hatice Gunes. « Towards Gender Fairness for Mental Health Prediction ». Dans Thirty-Second International Joint Conference on Artificial Intelligence {IJCAI-23}. California : International Joint Conferences on Artificial Intelligence Organization, 2023. http://dx.doi.org/10.24963/ijcai.2023/658.
Texte intégralGrari, Vincent, Sylvain Lamprier et Marcin Detyniecki. « Fairness without the Sensitive Attribute via Causal Variational Autoencoder ». Dans Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. California : International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/98.
Texte intégralPark, Souneil, Seungwoo Kang, Sangjeong Lee, Sangyoung Chung et Junehwa Song. « Mitigating media bias ». Dans the hypertext 2008 workshop. New York, New York, USA : ACM Press, 2008. http://dx.doi.org/10.1145/1379157.1379169.
Texte intégralQraitem, Maan, Kate Saenko et Bryan A. Plummer. « Bias Mimicking : A Simple Sampling Approach for Bias Mitigation ». Dans 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2023. http://dx.doi.org/10.1109/cvpr52729.2023.01945.
Texte intégralJiang, Jian, Viswonathan Manoranjan, Hanan Salam et Oya Celiktutan. « Generalised Bias Mitigation for Personality Computing ». Dans MM '23 : The 31st ACM International Conference on Multimedia. New York, NY, USA : ACM, 2023. http://dx.doi.org/10.1145/3607865.3616175.
Texte intégralJeon, Eojin, Mingyu Lee, Juhyeong Park, Yeachan Kim, Wing-Lam Mok et SangKeun Lee. « Improving Bias Mitigation through Bias Experts in Natural Language Understanding ». Dans Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing. Stroudsburg, PA, USA : Association for Computational Linguistics, 2023. http://dx.doi.org/10.18653/v1/2023.emnlp-main.681.
Texte intégralAkl, Naeem, et Ahmed Tewfik. « Optimal information sequencing for cognitive bias mitigation ». Dans 2014 6th International Symposium on Communications, Control and Signal Processing (ISCCSP). IEEE, 2014. http://dx.doi.org/10.1109/isccsp.2014.6877806.
Texte intégralHeuss, Maria, Daniel Cohen, Masoud Mansoury, Maarten de Rijke et Carsten Eickhoff. « Predictive Uncertainty-based Bias Mitigation in Ranking ». Dans CIKM '23 : The 32nd ACM International Conference on Information and Knowledge Management. New York, NY, USA : ACM, 2023. http://dx.doi.org/10.1145/3583780.3615011.
Texte intégralDervişoğlu, Havvanur, et Mehmet Fatih Amasyali. « Racial Bias Mitigation with Federated Learning Approach ». Dans 2023 8th International Conference on Computer Science and Engineering (UBMK). IEEE, 2023. http://dx.doi.org/10.1109/ubmk59864.2023.10286618.
Texte intégralRapports d'organisations sur le sujet "Mitigation des biais"
Serakos, Demetrios, John E. Gray et Hazim Youssef. Topics in Mitigating Radar Bias. Fort Belvoir, VA : Defense Technical Information Center, janvier 2012. http://dx.doi.org/10.21236/ada604137.
Texte intégralDolabella, Marcelo, et Mauricio Mesquita Moreira. Fighting Global Warming : Is Trade Policy in Latin America and the Caribbean a Help or a Hindrance ? Inter-American Development Bank, août 2022. http://dx.doi.org/10.18235/0004426.
Texte intégralTipton, Kelley, Brian F. Leas, Emilia Flores, Christopher Jepson, Jaya Aysola, Jordana Cohen, Michael Harhay et al. Impact of Healthcare Algorithms on Racial and Ethnic Disparities in Health and Healthcare. Agency for Healthcare Research and Quality (AHRQ), décembre 2023. http://dx.doi.org/10.23970/ahrqepccer268.
Texte intégralPanek, Krol et Huth. PR-312-12208-R03 USEPA AERMOD Plume Rise and Volume Formulations and Implications for Existing RICE. Chantilly, Virginia : Pipeline Research Council International, Inc. (PRCI), février 2016. http://dx.doi.org/10.55274/r0010858.
Texte intégralCarter, Sara, Jane Griffin, Samantha Lako, Cheryl Harewood, Lisa Kessler et Elizabeth Parish. The impacts of COVID-19 on schools’ willingness to participate in research. RTI Press, janvier 2024. http://dx.doi.org/10.3768/rtipress.2024.rb.0036.2401.
Texte intégralBray, Jonathan, Ross Boulanger, Misko Cubrinovski, Kohji Tokimatsu, Steven Kramer, Thomas O'Rourke, Ellen Rathje, Russell Green, Peter Robertson et Christine Beyzaei. 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, mars 2017. http://dx.doi.org/10.55461/gzzx9906.
Texte intégralEslava, Marcela, Alessandro Maffioli et Marcela Meléndez Arjona. Second-tier Government Banks and Access to Credit : Micro-Evidence from Colombia. Inter-American Development Bank, mars 2012. http://dx.doi.org/10.18235/0011364.
Texte intégralAvis, William. Refugee and Mixed Migration Displacement from Afghanistan. Institute of Development Studies (IDS), août 2021. http://dx.doi.org/10.19088/k4d.2022.002.
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