Literatura científica selecionada sobre o tema "Mitigation des biais"
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Artigos de revistas sobre o assunto "Mitigation des biais"
Philipps, Nathalia, Pierre P. Kastendeuch e 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.
Texto completo da fonteRahmawati, Fitriana, e Fitri Santi. "A Literature Review on the Influence of Availability Bias and Overconfidence Bias on Investor Decisions". East Asian Journal of Multidisciplinary Research 2, n.º 12 (30 de dezembro de 2023): 4961–76. http://dx.doi.org/10.55927/eajmr.v2i12.6896.
Texto completo da fonteDjebrouni, Yasmine, Nawel Benarba, Ousmane Touat, Pasquale De Rosa, Sara Bouchenak, Angela Bonifati, Pascal Felber, Vania Marangozova e Valerio Schiavoni. "Bias Mitigation in Federated Learning for Edge Computing". Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 7, n.º 4 (19 de dezembro de 2023): 1–35. http://dx.doi.org/10.1145/3631455.
Texto completo da fonteGallaher, Joshua P., Alexander J. Kamrud e Brett J. Borghetti. "Detection and Mitigation of Inefficient Visual Searching". Proceedings of the Human Factors and Ergonomics Society Annual Meeting 64, n.º 1 (dezembro de 2020): 47–51. http://dx.doi.org/10.1177/1071181320641015.
Texto completo da fonteLee, 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, n.º 6 (20 de agosto de 2016): 751–79. http://dx.doi.org/10.1177/1046878116662955.
Texto completo da fonteK. 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, n.º 9 (30 de outubro de 2023): 776–81. http://dx.doi.org/10.17762/ijritcc.v11i9.8965.
Texto completo da fonteChu, Charlene, Simon Donato-Woodger, Shehroz Khan, Kathleen Leslie, Tianyu Shi, Rune Nyrup e Amanda Grenier. "STRATEGIES TO MITIGATE MACHINE LEARNING BIAS AFFECTING OLDER ADULTS: RESULTS FROM A SCOPING REVIEW". Innovation in Aging 7, Supplement_1 (1 de dezembro de 2023): 717–18. http://dx.doi.org/10.1093/geroni/igad104.2325.
Texto completo da fonteFeatherston, Rebecca Jean, Aron Shlonsky, Courtney Lewis, My-Linh Luong, Laura E. Downie, Adam P. Vogel, Catherine Granger, Bridget Hamilton e Karyn Galvin. "Interventions to Mitigate Bias in Social Work Decision-Making: A Systematic Review". Research on Social Work Practice 29, n.º 7 (23 de dezembro de 2018): 741–52. http://dx.doi.org/10.1177/1049731518819160.
Texto completo da fonteErkmen, Cherie Parungo, Lauren Kane e David T. Cooke. "Bias Mitigation in Cardiothoracic Recruitment". Annals of Thoracic Surgery 111, n.º 1 (janeiro de 2021): 12–15. http://dx.doi.org/10.1016/j.athoracsur.2020.07.005.
Texto completo da fonteVejsbjerg, Inge, Elizabeth M. Daly, Rahul Nair e Svetoslav Nizhnichenkov. "Interactive Human-Centric Bias Mitigation". Proceedings of the AAAI Conference on Artificial Intelligence 38, n.º 21 (24 de março de 2024): 23838–40. http://dx.doi.org/10.1609/aaai.v38i21.30582.
Texto completo da fonteTeses / dissertações sobre o assunto "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.
Texto completo da fonteIt 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/.
Texto completo da fonteFyrvald, 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.
Texto completo da fonteSalomon, 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.
Texto completo da fonteFrick, 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.
Texto completo da fonteTaylor, Stephen Luke. "Analyzing methods of mitigating initialization bias in transportation simulation models". Thesis, Georgia Institute of Technology, 2010. http://hdl.handle.net/1853/37208.
Texto completo da fonteSweeney, 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.
Texto completo da fonteThesis: 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.
Texto completo da fonteAshton, 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/.
Texto completo da fonteLowery, Meghan Rachelle. "MITIGATING SEX BIAS IN COMPENSATION DECISIONS: THE ROLE OF COMPARATIVE DATA". OpenSIUC, 2010. https://opensiuc.lib.siu.edu/dissertations/231.
Texto completo da fonteLivros sobre o assunto "Mitigation des biais"
Whitesmith, Martha. Cognitive Bias in Intelligence Analysis. Edinburgh University Press, 2020. http://dx.doi.org/10.3366/edinburgh/9781474466349.001.0001.
Texto completo da fonteKenski, Kate. Overcoming Confirmation and Blind Spot Biases When Communicating Science. Editado por Kathleen Hall Jamieson, Dan M. Kahan e Dietram A. Scheufele. Oxford University Press, 2017. http://dx.doi.org/10.1093/oxfordhb/9780190497620.013.40.
Texto completo da fonteMink, John. Forecasting with Out-Liars: Mitigating Blame, Bias, and Apathy in Your Planning Process to Drive Meaningful and Sustainable Financial Improvements. Mindstir Media, 2021.
Encontre o texto completo da fonteSandis, Elizabeth. Early Modern Drama at the Universities. Oxford University Press, 2022. http://dx.doi.org/10.1093/oso/9780192857132.001.0001.
Texto completo da fonteCapítulos de livros sobre o assunto "Mitigation des biais"
Formanek, Kay. "Surfacing and Mitigating Bias". In Beyond D&I, 137–62. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-75336-8_6.
Texto completo da fontePat, Croskerry. "Cognitive Bias Mitigation: Becoming Better Diagnosticians". In Diagnosis, 257–87. Boca Raton : Taylor & Francis, 2017.: CRC Press, 2017. http://dx.doi.org/10.1201/9781315116334-15.
Texto completo da fonteWang, Guanchu, Mengnan Du, Ninghao Liu, Na Zou e Xia Hu. "Mitigating Algorithmic Bias with Limited Annotations". In 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.
Texto completo da fonteTarallo, Mark. "Dancing with Myself: Self-Management and Bias Mitigation". In Modern Management and Leadership, 27–34. Boca Raton: CRC Press, 2021. http://dx.doi.org/10.1201/9781003095620-6.
Texto completo da fonteWu, Ye, Yuanjing Feng, Dinggang Shen e Pew-Thian Yap. "Penalized Geodesic Tractography for Mitigating Gyral Bias". In 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.
Texto completo da fonteLi, Yinxiao. "Mitigating Position Bias in Hotels Recommender Systems". In 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.
Texto completo da fonteSharma, Ashish, Rajeshwar Mehrotra e Fiona Johnson. "A New Framework for Modeling Future Hydrologic Extremes: Nested Bias Correction as a Precursor to Stochastic Rainfall Downscaling". In Climate Change Modeling, Mitigation, and Adaptation, 357–86. Reston, VA: American Society of Civil Engineers, 2013. http://dx.doi.org/10.1061/9780784412718.ch13.
Texto completo da fonteCorliss, David J. "Designing Against Bias: Identifying and Mitigating Bias in Machine Learning and AI". In 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.
Texto completo da fonteShi, Sheng, Shanshan Wei, Zhongchao Shi, Yangzhou Du, Wei Fan, Jianping Fan, Yolanda Conyers e Feiyu Xu. "Algorithm Bias Detection and Mitigation in Lenovo Face Recognition Engine". In 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.
Texto completo da fonteWang, Xing, Guoqiang Zhao, Feng Zhang e Yongan Yang. "Characterization and Mitigation of BeiDou Triple-Frequency Code Multipath Bias". In Lecture Notes in Electrical Engineering, 467–80. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-0014-1_39.
Texto completo da fonteTrabalhos de conferências sobre o assunto "Mitigation des biais"
Calegari, Roberta, Gabriel G. Castañé, Michela Milano e Barry O'Sullivan. "Assessing and Enforcing Fairness in the AI Lifecycle". In 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.
Texto completo da fonteCheong, Jiaee, Selim Kuzucu, Sinan Kalkan e Hatice Gunes. "Towards Gender Fairness for Mental Health Prediction". In 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.
Texto completo da fonteGrari, Vincent, Sylvain Lamprier e Marcin Detyniecki. "Fairness without the Sensitive Attribute via Causal Variational Autoencoder". In 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.
Texto completo da fontePark, Souneil, Seungwoo Kang, Sangjeong Lee, Sangyoung Chung e Junehwa Song. "Mitigating media bias". In the hypertext 2008 workshop. New York, New York, USA: ACM Press, 2008. http://dx.doi.org/10.1145/1379157.1379169.
Texto completo da fonteQraitem, Maan, Kate Saenko e Bryan A. Plummer. "Bias Mimicking: A Simple Sampling Approach for Bias Mitigation". In 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2023. http://dx.doi.org/10.1109/cvpr52729.2023.01945.
Texto completo da fonteJiang, Jian, Viswonathan Manoranjan, Hanan Salam e Oya Celiktutan. "Generalised Bias Mitigation for Personality Computing". In MM '23: The 31st ACM International Conference on Multimedia. New York, NY, USA: ACM, 2023. http://dx.doi.org/10.1145/3607865.3616175.
Texto completo da fonteJeon, Eojin, Mingyu Lee, Juhyeong Park, Yeachan Kim, Wing-Lam Mok e SangKeun Lee. "Improving Bias Mitigation through Bias Experts in Natural Language Understanding". In 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.
Texto completo da fonteAkl, Naeem, e Ahmed Tewfik. "Optimal information sequencing for cognitive bias mitigation". In 2014 6th International Symposium on Communications, Control and Signal Processing (ISCCSP). IEEE, 2014. http://dx.doi.org/10.1109/isccsp.2014.6877806.
Texto completo da fonteHeuss, Maria, Daniel Cohen, Masoud Mansoury, Maarten de Rijke e Carsten Eickhoff. "Predictive Uncertainty-based Bias Mitigation in Ranking". In 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.
Texto completo da fonteDervişoğlu, Havvanur, e Mehmet Fatih Amasyali. "Racial Bias Mitigation with Federated Learning Approach". In 2023 8th International Conference on Computer Science and Engineering (UBMK). IEEE, 2023. http://dx.doi.org/10.1109/ubmk59864.2023.10286618.
Texto completo da fonteRelatórios de organizações sobre o assunto "Mitigation des biais"
Serakos, Demetrios, John E. Gray e Hazim Youssef. Topics in Mitigating Radar Bias. Fort Belvoir, VA: Defense Technical Information Center, janeiro de 2012. http://dx.doi.org/10.21236/ada604137.
Texto completo da fonteDolabella, Marcelo, e Mauricio Mesquita Moreira. Fighting Global Warming: Is Trade Policy in Latin America and the Caribbean a Help or a Hindrance? Inter-American Development Bank, agosto de 2022. http://dx.doi.org/10.18235/0004426.
Texto completo da fonteTipton, 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), dezembro de 2023. http://dx.doi.org/10.23970/ahrqepccer268.
Texto completo da fontePanek, Krol e 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), fevereiro de 2016. http://dx.doi.org/10.55274/r0010858.
Texto completo da fonteCarter, Sara, Jane Griffin, Samantha Lako, Cheryl Harewood, Lisa Kessler e Elizabeth Parish. The impacts of COVID-19 on schools’ willingness to participate in research. RTI Press, janeiro de 2024. http://dx.doi.org/10.3768/rtipress.2024.rb.0036.2401.
Texto completo da fonteBray, Jonathan, Ross Boulanger, Misko Cubrinovski, Kohji Tokimatsu, Steven Kramer, Thomas O'Rourke, Ellen Rathje, Russell Green, Peter Robertson e 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, março de 2017. http://dx.doi.org/10.55461/gzzx9906.
Texto completo da fonteEslava, Marcela, Alessandro Maffioli e Marcela Meléndez Arjona. Second-tier Government Banks and Access to Credit: Micro-Evidence from Colombia. Inter-American Development Bank, março de 2012. http://dx.doi.org/10.18235/0011364.
Texto completo da fonteAvis, William. Refugee and Mixed Migration Displacement from Afghanistan. Institute of Development Studies (IDS), agosto de 2021. http://dx.doi.org/10.19088/k4d.2022.002.
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