Enhancing Fairness In Supervised Machine Learning

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Enhancing Fairness In Supervised Machine Learning

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Author : Bita Omidi
language : en
Publisher:
Release Date : 2021

Enhancing Fairness In Supervised Machine Learning written by Bita Omidi and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021 with categories.

The increasing influence of machine learning algorithms and artificial intelligence on the high-impact domains of decision-making has led to an increasing concern for the ethical and legal challenges posed by sensitive data-driven systems. Machine learning can identify the statistical patterns in the historically collected big data generated by a huge number of instances that might be affected by human and structural biases. ML algorithms have the potential to amplify these inequities. Lately, there have been several attempts to reduce bias in artificial intelligence in order to maintain fairness in machine learning projects. These methods fall under three categories of pre-processing, in-processing, and post-processing techniques. There are at least 21 notations of fairness in the recent literature, which not only provide different measurement methods of fairness but also lead to completely different concepts. It is worth mentioning that, it is impossible to satisfy all of the definitions of fairness at the same time and some of them are incompatible with each other. As a result, it is important to choose a fairness definition that need to be satisfied according to the context that we are working on. The current study investigates some of the most common definitions and metrics for fairness introduced by researchers to compare three of the proposed de-biasing techniques regarding their effects on the performance and fairness measures through empirical experiments on four different datasets. The de-biasing methods include the "Reweighing Algorithm", "Adversarial De-biasing Method", the "Reject Option Classification Method" performed on the classification tasks of "Survival of patients with heart failure"(Heart Failure Dataset), "Prediction of hospital readmission among diabetes patients" (Diabetes Dataset), "Credit classification of bank account holders" (German Credit Dataset), and "The COVID19 related anxiety level classification of Canadians" (CAMH Dataset). Findings show that the adversarial de-biasing in-processing method can be the best technique for mitigating bias working with the deep learning classifiers when we are capable of changing the classification process. This method has not led to a considerable reduction of accuracy except for the CAMH dataset. The "Reject Option Classification" which is a post-processing method, causes the most deterioration of prediction accuracy in all datasets. On the other hand, this method has the best performance in alleviating the bias generated through the classification process. The "Reweighing Algorithm" as a pre-processing technique does not cause a considerable reduction in the accuracy and is capable of reducing bias in classification tasks, although its performance is not as strong as the Reject Option classifier.

Enhancing Fairness In Machine Learning Through Reweighting

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Author : Xuan Zhao
language : de
Publisher:
Release Date : 2024

Enhancing Fairness In Machine Learning Through Reweighting written by Xuan Zhao and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024 with categories.

Mitigating Bias In Machine Learning

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Author : Carlotta A. Berry
language : en
Publisher: McGraw Hill Professional
Release Date : 2024-10-18

Mitigating Bias In Machine Learning written by Carlotta A. Berry and has been published by McGraw Hill Professional this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-10-18 with Technology & Engineering categories.

This practical guide shows, step by step, how to use machine learning to carry out actionable decisions that do not discriminate based on numerous human factors, including ethnicity and gender. The authors examine the many kinds of bias that occur in the field today and provide mitigation strategies that are ready to deploy across a wide range of technologies, applications, and industries. Edited by engineering and computing experts, Mitigating Bias in Machine Learning includes contributions from recognized scholars and professionals working across different artificial intelligence sectors. Each chapter addresses a different topic and real-world case studies are featured throughout that highlight discriminatory machine learning practices and clearly show how they were reduced. Mitigating Bias in Machine Learning addresses: Ethical and Societal Implications of Machine Learning Social Media and Health Information Dissemination Comparative Case Study of Fairness Toolkits Bias Mitigation in Hate Speech Detection Unintended Systematic Biases in Natural Language Processing Combating Bias in Large Language Models Recognizing Bias in Medical Machine Learning and AI Models Machine Learning Bias in Healthcare Achieving Systemic Equity in Socioecological Systems Community Engagement for Machine Learning

Dissertation Abstracts International

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Author :
language : en
Publisher:
Release Date : 2008

Dissertation Abstracts International written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008 with Dissertations, Academic categories.

Program And Abstracts

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Author : American Folklore Society. Annual Meeting
language : en
Publisher:
Release Date : 2009

Program And Abstracts written by American Folklore Society. Annual Meeting and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009 with Folklore categories.

Understanding Healthcare Delivery Science

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Author : Michael Howell
language : en
Publisher: McGraw Hill Professional
Release Date : 2019-09-27

Understanding Healthcare Delivery Science written by Michael Howell and has been published by McGraw Hill Professional this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-09-27 with Medical categories.

An accessible new title focused on the science of healthcare delivery, from the acclaimed Understanding series A Doody’s Core Title for 2024! “... a landmark text that will shape the field and inform our dialog for years to come—-and it should be part of the required curriculum at medical and nursing schools around the world. Excellence in healthcare delivery science should become a core competency of the modern physician. Howell and Stevens have given medicine an important gift that may enable just that.” —Sachin H. Jain, MD, MBA, FACP; President and CEO, CareMore and Aspire Health; Co-Founder and Co-Editor-in-Chief, Healthcare: The Journal of Delivery Science and Innovation “You hold in your hands 35 years of investigation and learning, condensed into understandable principles and applications. It is a guidebook for effective care delivery leadership, practice, and success.” —Brent C. James, MD, MStat, Clinical Professor, Stanford University School of Medicine “...a must-read for anyone who, like me, is frustrated with the pace of our progress and is committed to creating a learning health system for all.” —Lisa Simpson, MB, BCh, MPH, FAAP, President and CEO, AcademyHealth “... will quickly become the go-to, must-read resource for practitioners looking to have an impact as innovators in healthcare delivery.” —David H. Roberts, MD, Steven P. Simcox, Patrick A. Clifford, and James H. Higby Associate Professor of Medicine, Harvard Medical School Today’s healthcare system is profoundly complicated, but we persist in trying to roll out breakthroughs as if the healthcare system were still just the straightforward “physician’s workshop” of the early 20th century. Only rarely do we employ research-quality analytics to assess how well our care delivery innovations really work in the practice. And shockingly, the US healthcare delivery system spends only 0.1% of revenue on R&D in how we actually deliver care. Small wonder that we find ourselves faced with the current medical paradox: Treatments that seemed miraculous at the beginning of our lifetimes are routine today, but low-quality care and medical errors harm millions of people worldwide even as spiraling healthcare costs bankrupt an unacceptable number of American families every year. Healthcare delivery science bridges this gap between scientific research and complex, real-world healthcare delivery and operations. With its engaging, clinically relevant style, Understanding Healthcare Delivery Science is the perfect introduction to this emerging field. This reader-friendly text pairs a thorough discussion of commonly available healthcare improvement tools and top-tier research methods with numerous case studies that put the content into a clinically relevant framework, making this text a valuable tool for administrators, researchers, and clinicians alike.

Bootstrapping Named Entity Annotation By Means Of Active Machine Learning

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Author : Fredrik Olsson
language : en
Publisher:
Release Date : 2008

Bootstrapping Named Entity Annotation By Means Of Active Machine Learning written by Fredrik Olsson and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008 with Computational linguistics categories.

On the development of a method called BootMark for bootstrapping the marking up of named entities in textual documents.

Fair Employment Practice Cases

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Author :
language : en
Publisher:
Release Date : 1997

Fair Employment Practice Cases written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1997 with Discrimination in employment categories.

With case table.

Business Seventh Edition Custom Publication

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Author : Pride
language : en
Publisher: Houghton Mifflin
Release Date : 2002-06

Business Seventh Edition Custom Publication written by Pride and has been published by Houghton Mifflin this book supported file pdf, txt, epub, kindle and other format this book has been release on 2002-06 with Business & Economics categories.

Scientific American

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Author :
language : en
Publisher:
Release Date : 1875

Scientific American written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1875 with Science categories.