Auswahl der wissenschaftlichen Literatur zum Thema „Bias mitigation“
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Zeitschriftenartikel zum Thema "Bias mitigation"
Erkmen, 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 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 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 QuelleSingh, Richa, Puspita Majumdar, Surbhi Mittal und Mayank Vatsa. „Anatomizing Bias in Facial Analysis“. Proceedings of the AAAI Conference on Artificial Intelligence 36, Nr. 11 (28.06.2022): 12351–58. http://dx.doi.org/10.1609/aaai.v36i11.21500.
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 QuellePatil, Pranita, und Kevin Purcell. „Decorrelation-Based Deep Learning for Bias Mitigation“. Future Internet 14, Nr. 4 (29.03.2022): 110. http://dx.doi.org/10.3390/fi14040110.
Der volle Inhalt der QuelleKim, Hyo-eun. „Fairness Criteria and Mitigation of AI Bias“. Korean Journal of Psychology: General 40, Nr. 4 (25.12.2021): 459–85. http://dx.doi.org/10.22257/kjp.2021.12.40.4.459.
Der volle Inhalt der QuellePark, Souneil, Seungwoo Kang, Sangyoung Chung und Junehwa Song. „A Computational Framework for Media Bias Mitigation“. ACM Transactions on Interactive Intelligent Systems 2, Nr. 2 (Juni 2012): 1–32. http://dx.doi.org/10.1145/2209310.2209311.
Der volle Inhalt der QuelleDissertationen zum Thema "Bias mitigation"
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 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 QuelleIsumbingabo, 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 QuelleWu, Lin. „Contribution to spatial bias mitigation in interferometric radiometers devoted to Earth observation : application to the SMOS mission“. Doctoral thesis, Universitat Politècnica de Catalunya, 2014. http://hdl.handle.net/10803/144655.
Der volle Inhalt der QuelleLe, 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
Dougherty, John Paul. „Three Essays on the Economic Sustainability of Drought Insurance and Soil Investment for Smallholder Farmers in the Developing World“. The Ohio State University, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=osu1531672015876609.
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 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
Bücher zum Thema "Bias mitigation"
Li, Junfeng. Jian huan qi hou bian hua: Yuan ze, mu biao, xing dong ji dui ce. 8. Aufl. Beijing Shi: Zhongguo ji hua chu ban she, 2011.
Den vollen Inhalt der Quelle findenChen, Junwu. Zhongguo zhong chang qi tan jian pai zhan lüe mu biao yan jiu. 8. Aufl. Beijing Shi: Zhongguo shi hua chu ban she, 2012.
Den vollen Inhalt der Quelle findenHu, Angang. Zhongguo ying dui quan qiu qi hou bian hua. 8. Aufl. Beijing: Qing hua da xue chu ban she, 2009.
Den vollen Inhalt der Quelle findenQi hou bian hua yu di tan jing ji. Beijing Shi: Zhongguo shui li shui dian chu ban she, 2010.
Den vollen Inhalt der Quelle findenWu, Shaohong. Zhongguo zong he qi hou bian hua feng xian. 8. Aufl. Beijing: Ke xue chu ban she, 2011.
Den vollen Inhalt der Quelle findenYang, Deping. Zhongguo di tan zheng ce xi tong gou jian yan jiu: Zhu ti, gong ju yu bian qian. 8. Aufl. Beijing Shi: Jing ji ke xue chu ban she, 2016.
Den vollen Inhalt der Quelle findenFeng zhi mu biao xia Zhongguo di tan fa zhan lu jing xuan ze yan jiu: Yi Tianjin wei li. Beijing Shi: Ren min chu ban she, 2016.
Den vollen Inhalt der Quelle findenXu, He. Qi hou bian hua xin shi jiao xia de Zhongguo zhan lüe huan jing ping jia: Integrating the Climate Change Issues into Strategic Environmental Assessment in China. 8. Aufl. Beijing: Ke xue chu ban she, 2013.
Den vollen Inhalt der Quelle findenShi, Wenzhen. WTO, qi hou bian qian yu neng yuan. 8. Aufl. Taibei Shi: Yuan zhao chu ban you xian gong si, 2013.
Den vollen Inhalt der Quelle findenLi, Jianping. "Tan jin" shi dai: Quan qiu bian nuan, wo men ru he yu huo chong sheng. 8. Aufl. Beijing: Zhongguo huan jing ke xue chu ban she, 2010.
Den vollen Inhalt der Quelle findenBuchteile zum Thema "Bias mitigation"
Pat, 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 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 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 QuelleParsons, Paul. „Promoting Representational Fluency for Cognitive Bias Mitigation in Information Visualization“. In Cognitive Biases in Visualizations, 137–47. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-95831-6_10.
Der volle Inhalt der QuelleGhadage, Adinath, Dewei Yi, George Coghill und Wei Pang. „Multi-stage Bias Mitigation for Individual Fairness in Algorithmic Decisions“. In Artificial Neural Networks in Pattern Recognition, 40–52. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-20650-4_4.
Der volle Inhalt der QuelleVorontsov, Eugene, und Samuel Kadoury. „Label Noise in Segmentation Networks: Mitigation Must Deal with Bias“. In Deep Generative Models, and Data Augmentation, Labelling, and Imperfections, 251–58. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-88210-5_25.
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 QuelleDost, Katharina, Hamish Duncanson, Ioannis Ziogas, Patricia Riddle und Jörg Wicker. „Divide and Imitate: Multi-cluster Identification and Mitigation of Selection Bias“. In Advances in Knowledge Discovery and Data Mining, 149–60. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-05936-0_12.
Der volle Inhalt der QuelleSaxena, Akrati, Harsh Saxena und Ralucca Gera. „k-TruthScore: Fake News Mitigation in the Presence of Strong User Bias“. In Computational Data and Social Networks, 113–26. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-66046-8_10.
Der volle Inhalt der QuelleKonferenzberichte zum Thema "Bias mitigation"
Qraitem, 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 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 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 QuelleCalegari, 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 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 QuelleKumar, Deepak, Oleg Lesota, George Zerveas, Daniel Cohen, Carsten Eickhoff, Markus Schedl und Navid Rekabsaz. „Parameter-efficient Modularised Bias Mitigation via AdapterFusion“. In Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics. Stroudsburg, PA, USA: Association for Computational Linguistics, 2023. http://dx.doi.org/10.18653/v1/2023.eacl-main.201.
Der volle Inhalt der QuelleHuang, Hui, Shuangzhi Wu, Kehai Chen, Hui Di, Muyun Yang und Tiejun Zhao. „Improving Translation Quality Estimation with Bias Mitigation“. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Stroudsburg, PA, USA: Association for Computational Linguistics, 2023. http://dx.doi.org/10.18653/v1/2023.acl-long.121.
Der volle Inhalt der QuellePatrikar, Ajay M., Arjuna Mahenthiran und Ahmad Said. „Leveraging synthetic data for AI bias mitigation“. In Synthetic Data for Artificial Intelligence and Machine Learning: Tools, Techniques, and Applications, herausgegeben von Kimberly E. Manser, Raghuveer M. Rao und Christopher L. Howell. SPIE, 2023. http://dx.doi.org/10.1117/12.2662276.
Der volle Inhalt der QuelleBerichte der Organisationen zum Thema "Bias mitigation"
Dolabella, 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 QuelleSerakos, 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 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 QuelleGolub, Alla, Benjamin Henderson, Thomas Hertel, Steven Rose, Misak Avetisyan und Brent Sohngen. Effects of GHG Mitigation Policies on Livestock Sectors. GTAP Working Paper, Juli 2010. http://dx.doi.org/10.21642/gtap.wp62.
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.
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.
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