Auswahl der wissenschaftlichen Literatur zum Thema „Mitigation des biais“
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Zeitschriftenartikel zum Thema "Mitigation des biais"
Philipps, Nathalia, Pierre P. Kastendeuch und 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.
Der volle Inhalt der QuelleRahmawati, Fitriana, und Fitri Santi. „A Literature Review on the Influence of Availability Bias and Overconfidence Bias on Investor Decisions“. East Asian Journal of Multidisciplinary Research 2, Nr. 12 (30.12.2023): 4961–76. http://dx.doi.org/10.55927/eajmr.v2i12.6896.
Der volle Inhalt der QuelleDjebrouni, Yasmine, Nawel Benarba, Ousmane Touat, Pasquale De Rosa, Sara Bouchenak, Angela Bonifati, Pascal Felber, Vania Marangozova und Valerio Schiavoni. „Bias Mitigation in Federated Learning for Edge Computing“. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 7, Nr. 4 (19.12.2023): 1–35. http://dx.doi.org/10.1145/3631455.
Der volle Inhalt der QuelleGallaher, Joshua P., Alexander J. Kamrud und Brett J. Borghetti. „Detection and Mitigation of Inefficient Visual Searching“. Proceedings of the Human Factors and Ergonomics Society Annual Meeting 64, Nr. 1 (Dezember 2020): 47–51. http://dx.doi.org/10.1177/1071181320641015.
Der volle Inhalt der QuelleLee, 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, Nr. 6 (20.08.2016): 751–79. http://dx.doi.org/10.1177/1046878116662955.
Der volle Inhalt der QuelleK. 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, Nr. 9 (30.10.2023): 776–81. http://dx.doi.org/10.17762/ijritcc.v11i9.8965.
Der volle Inhalt der QuelleChu, Charlene, Simon Donato-Woodger, Shehroz Khan, Kathleen Leslie, Tianyu Shi, Rune Nyrup und Amanda Grenier. „STRATEGIES TO MITIGATE MACHINE LEARNING BIAS AFFECTING OLDER ADULTS: RESULTS FROM A SCOPING REVIEW“. Innovation in Aging 7, Supplement_1 (01.12.2023): 717–18. http://dx.doi.org/10.1093/geroni/igad104.2325.
Der volle Inhalt der QuelleFeatherston, Rebecca Jean, Aron Shlonsky, Courtney Lewis, My-Linh Luong, Laura E. Downie, Adam P. Vogel, Catherine Granger, Bridget Hamilton und Karyn Galvin. „Interventions to Mitigate Bias in Social Work Decision-Making: A Systematic Review“. Research on Social Work Practice 29, Nr. 7 (23.12.2018): 741–52. http://dx.doi.org/10.1177/1049731518819160.
Der volle Inhalt der QuelleErkmen, Cherie Parungo, Lauren Kane und David T. Cooke. „Bias Mitigation in Cardiothoracic Recruitment“. Annals of Thoracic Surgery 111, Nr. 1 (Januar 2021): 12–15. http://dx.doi.org/10.1016/j.athoracsur.2020.07.005.
Der volle Inhalt der QuelleVejsbjerg, Inge, Elizabeth M. Daly, Rahul Nair und Svetoslav Nizhnichenkov. „Interactive Human-Centric Bias Mitigation“. Proceedings of the AAAI Conference on Artificial Intelligence 38, Nr. 21 (24.03.2024): 23838–40. http://dx.doi.org/10.1609/aaai.v38i21.30582.
Der volle Inhalt der QuelleDissertationen zum Thema "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.
Der volle Inhalt der QuelleIt 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/.
Der volle Inhalt der QuelleFyrvald, 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.
Der volle Inhalt der QuelleSalomon, 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.
Der volle Inhalt der QuelleFrick, 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.
Der volle Inhalt der QuelleTaylor, Stephen Luke. „Analyzing methods of mitigating initialization bias in transportation simulation models“. Thesis, Georgia Institute of Technology, 2010. http://hdl.handle.net/1853/37208.
Der volle Inhalt der QuelleSweeney, 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.
Der volle Inhalt der QuelleThesis: 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.
Der volle Inhalt der QuelleAshton, 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/.
Der volle Inhalt der QuelleLowery, Meghan Rachelle. „MITIGATING SEX BIAS IN COMPENSATION DECISIONS: THE ROLE OF COMPARATIVE DATA“. OpenSIUC, 2010. https://opensiuc.lib.siu.edu/dissertations/231.
Der volle Inhalt der QuelleBücher zum Thema "Mitigation des biais"
Whitesmith, Martha. Cognitive Bias in Intelligence Analysis. Edinburgh University Press, 2020. http://dx.doi.org/10.3366/edinburgh/9781474466349.001.0001.
Der volle Inhalt der QuelleKenski, Kate. Overcoming Confirmation and Blind Spot Biases When Communicating Science. Herausgegeben von Kathleen Hall Jamieson, Dan M. Kahan und Dietram A. Scheufele. Oxford University Press, 2017. http://dx.doi.org/10.1093/oxfordhb/9780190497620.013.40.
Der volle Inhalt der QuelleMink, John. Forecasting with Out-Liars: Mitigating Blame, Bias, and Apathy in Your Planning Process to Drive Meaningful and Sustainable Financial Improvements. Mindstir Media, 2021.
Den vollen Inhalt der Quelle findenSandis, Elizabeth. Early Modern Drama at the Universities. Oxford University Press, 2022. http://dx.doi.org/10.1093/oso/9780192857132.001.0001.
Der volle Inhalt der QuelleBuchteile zum Thema "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.
Der volle Inhalt der QuellePat, 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.
Der volle Inhalt der QuelleWang, Guanchu, Mengnan Du, Ninghao Liu, Na Zou und 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.
Der volle Inhalt der QuelleTarallo, 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.
Der volle Inhalt der QuelleWu, Ye, Yuanjing Feng, Dinggang Shen und 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.
Der volle Inhalt der QuelleLi, 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.
Der volle Inhalt der QuelleSharma, Ashish, Rajeshwar Mehrotra und 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.
Der volle Inhalt der QuelleCorliss, 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.
Der volle Inhalt der QuelleShi, Sheng, Shanshan Wei, Zhongchao Shi, Yangzhou Du, Wei Fan, Jianping Fan, Yolanda Conyers und 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.
Der volle Inhalt der QuelleWang, Xing, Guoqiang Zhao, Feng Zhang und 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.
Der volle Inhalt der QuelleKonferenzberichte zum Thema "Mitigation des biais"
Calegari, Roberta, Gabriel G. Castañé, Michela Milano und 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.
Der volle Inhalt der QuelleCheong, Jiaee, Selim Kuzucu, Sinan Kalkan und 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.
Der volle Inhalt der QuelleGrari, Vincent, Sylvain Lamprier und 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.
Der volle Inhalt der QuellePark, Souneil, Seungwoo Kang, Sangjeong Lee, Sangyoung Chung und 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.
Der volle Inhalt der QuelleQraitem, Maan, Kate Saenko und 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.
Der volle Inhalt der QuelleJiang, Jian, Viswonathan Manoranjan, Hanan Salam und 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.
Der volle Inhalt der QuelleJeon, Eojin, Mingyu Lee, Juhyeong Park, Yeachan Kim, Wing-Lam Mok und 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.
Der volle Inhalt der QuelleAkl, Naeem, und 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.
Der volle Inhalt der QuelleHeuss, Maria, Daniel Cohen, Masoud Mansoury, Maarten de Rijke und 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.
Der volle Inhalt der QuelleDervişoğlu, Havvanur, und 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.
Der volle Inhalt der QuelleBerichte der Organisationen zum Thema "Mitigation des biais"
Serakos, Demetrios, John E. Gray und Hazim Youssef. Topics in Mitigating Radar Bias. Fort Belvoir, VA: Defense Technical Information Center, Januar 2012. http://dx.doi.org/10.21236/ada604137.
Der volle Inhalt der QuelleDolabella, Marcelo, und Mauricio Mesquita Moreira. Fighting Global Warming: Is Trade Policy in Latin America and the Caribbean a Help or a Hindrance? Inter-American Development Bank, August 2022. http://dx.doi.org/10.18235/0004426.
Der volle Inhalt der QuelleTipton, 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), Dezember 2023. http://dx.doi.org/10.23970/ahrqepccer268.
Der volle Inhalt der QuellePanek, Krol und 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), Februar 2016. http://dx.doi.org/10.55274/r0010858.
Der volle Inhalt der QuelleCarter, Sara, Jane Griffin, Samantha Lako, Cheryl Harewood, Lisa Kessler und Elizabeth Parish. The impacts of COVID-19 on schools’ willingness to participate in research. RTI Press, Januar 2024. http://dx.doi.org/10.3768/rtipress.2024.rb.0036.2401.
Der volle Inhalt der QuelleBray, Jonathan, Ross Boulanger, Misko Cubrinovski, Kohji Tokimatsu, Steven Kramer, Thomas O'Rourke, Ellen Rathje, Russell Green, Peter Robertson und 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, März 2017. http://dx.doi.org/10.55461/gzzx9906.
Der volle Inhalt der QuelleEslava, Marcela, Alessandro Maffioli und Marcela Meléndez Arjona. Second-tier Government Banks and Access to Credit: Micro-Evidence from Colombia. Inter-American Development Bank, März 2012. http://dx.doi.org/10.18235/0011364.
Der volle Inhalt der QuelleAvis, William. Refugee and Mixed Migration Displacement from Afghanistan. Institute of Development Studies (IDS), August 2021. http://dx.doi.org/10.19088/k4d.2022.002.
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