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.
It 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/.
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.
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.
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.
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.
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.
Thesis: 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.
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/.
Lowery, Meghan Rachelle. "MITIGATING SEX BIAS IN COMPENSATION DECISIONS: THE ROLE OF COMPARATIVE DATA." OpenSIUC, 2010. https://opensiuc.lib.siu.edu/dissertations/231.
Hube, Christoph [Verfasser]. "Methods for detecting and mitigating linguistic bias in text corpora / Christoph Hube." Hannover : Gottfried Wilhelm Leibniz Universität Hannover, 2020. http://d-nb.info/1212582438/34.
Wu, 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.
Terhörst, Philipp [Verfasser], Arjan [Akademischer Betreuer] Kuijper, Dieter [Akademischer Betreuer] Fellner, and Vitomir [Akademischer Betreuer] Struc. "Mitigating Soft-Biometric Driven Bias and Privacy Concerns in Face Recognition Systems / Philipp Terhörst ; Arjan Kuijper, Dieter Fellner, Vitomir Struc." Darmstadt : Universitäts- und Landesbibliothek, 2021. http://d-nb.info/1233785060/34.
Bohrer, Shawn A. "Military-media relationships : identifying and mitigating military-media biases to improve future military operations." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 2003. http://library.nps.navy.mil/uhtbin/hyperion-image/03Mar%5FBohrer.pdf.
Thesis advisor(s): Steven J. Iatrou, Karen Guttieri. Includes bibliographical references (p. 67-68). Also available online.
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.
"Discovering and Mitigating Social Data Bias." Doctoral diss., 2017. http://hdl.handle.net/2286/R.I.45009.
Dissertation/Thesis
Doctoral Dissertation Computer Science 2017
LIAO, CHENG-YI, and 廖崢圯. "Cheap Talk on Mitigating Hypothetical Bias in Contingent Valuation." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/ssqrfb.
國立臺北大學
自然資源與環境管理研究所
107
When applying the contingent value method (CVM) to value non-marketed goods (such as wetlands), researchers often face the problem of hypothetical bias (HB) in that respondents’ answered willingness to pay (WTP) is often different from their real WTP. The methods to eliminate HB are divided into ex post calibration and ex ante reminder. The former compare the real market data and questionnaire data to adjust the WTP after the survey undertaken; the latter is to provide some information in questionnaire before inquiring respondents, so that respondents can correct their WTP before answering. CT is a common practice in the ex-ante reminder. In the context of CT, the existence of HB is explicitly explained, respondents are reminded that they may fill in a dollar amount that does not match their real WTP, and then respondents are asked to promise to fill in a WTP that is closer to their real one. In order to investigate people’s WTP for conserving the Qijiawanxi wetland, Shuanglian wetland and wetlands nationwide, this study interviewed 600 questionnaires in person and added 300 short-length CTs to test whether CT could reduce HB. Three models (OLS model, Tobit model, and sample selection model) are used to analyze the empirical WTP bid functions, the results show that the effect of CT on reducing HB is not statistically significant. The reasons for this result may include: (1) The CT taken by this study is a short narrative, which may not be enough for the respondents to understand what is called HB; (2) Establishing an environment mental accounting framework (MAF) for the respondents in questionnaire can reduce the HB. First, several instruction cards the clearly state the overall scope of the wetlands surveyed are presented and explained to respondents. Secondly, we inquiry respondents’ WTP for preserving all wetlands in Taiwan, then asks their WTP for individual wetlands. (3) HB and other biases such as hypothetical and embedding biased are overlapped, the MAF for reducing the embedding bias also help to reduce HB.
Chia, I.-Hsiang, and 賈逸翔. "The Mitigation of Confirmation Bias in Health Information processing: a Comparison between Popular and Expert Opinions." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/q7gc45.
國立臺灣大學
圖書資訊學研究所
105
Confirmation bias, the preferential seeking of confirmatory information, can become an obstacle for the disseminate of valid health information online and lead to biased decision. Following previous findings, preference-inconsistent recommendation can be used to overcome this bias. We conducted an experiment to study the impact of expert system and popularity system on mitigating confirmation bias, the confirmation bias was measured in the selection phase, the evaluation phase and the final decision phases. 78 participants aged 40-70 were recruited. Participants were informed that they would participate in a health information experiment involving two cancer screening debates. Participants were assigned in such a way that each participant would see two fairly different interfaces for the two tasks. We found that the evaluation bias and the final decision were more persistent than the selection bias. The comparison between the two systems revealed that expert system has a better mitigation effect than the popularity system in the selection bias. Furthermore, it was observed that expert system would have better mitigation effect on high-involvement issue. We also found strong gender difference in our experiment. Future study which aims to investigate the mitigation effect of different techniques should take gender as a confounding variable and choose health issues which are more comparable.
Terhörst, Philipp. "Mitigating Soft-Biometric Driven Bias and Privacy Concerns in Face Recognition Systems." Phd thesis, 2021. https://tuprints.ulb.tu-darmstadt.de/18515/7/Dissertation_Terhoerst_final.pdf.
McCarthy, SL. "Attention bias in social anxiety : are there mitigating effects of self-affirmation?" Thesis, 2014. https://eprints.utas.edu.au/28097/1/McCarthy_whole_thesis.pdf.
Hallihan, Gregory M. "Mitigating Cognitive and Neural Biases in Conceptual Design." Thesis, 2012. http://hdl.handle.net/1807/33234.