Mitigating Bias In Machine Learning

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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

Model Behavior

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Author : Sara Kassir
language : en
Publisher:
Release Date : 2019

Model Behavior written by Sara Kassir and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019 with categories.

Data Quality And Artificial Intelligence

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Author :
language : en
Publisher:
Release Date : 2019

Data Quality And Artificial Intelligence written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019 with categories.

Algorithms used in machine learning systems and artificial intelligence (AI) can only be as good as the data used for their development. High quality data are essential for high quality algorithms. Yet, the call for high quality data in discussions around AI often remains without any further specifications and guidance as to what this actually means. Since there are several sources of error in all data collections, users of AI-related technology need to know where the data come from and the potential shortcomings of the data. AI systems based on incomplete or biased data can lead to inaccurate outcomes that infringe on people’s fundamental rights, including discrimination. Being transparent about which data are used in AI systems helps to prevent possible rights violations. This is especially important in times of big data, where the volume of data is sometimes valued over quality.

Understanding And Mitigating Unintended Demographic Bias In Machine Learning Systems

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Author : Christopher J. Sweeney (M. Eng.)
language : en
Publisher:
Release Date : 2019

Understanding And Mitigating Unintended Demographic Bias In Machine Learning Systems written by Christopher J. Sweeney (M. Eng.) and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019 with categories.

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.

Algorithm Bias Systems

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Author : Orin Brightfield
language : en
Publisher: Publifye AS
Release Date : 2025-05-05

Algorithm Bias Systems written by Orin Brightfield and has been published by Publifye AS this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-05-05 with Computers categories.

Algorithm Bias Systems explores the pervasive issue of algorithmic bias, revealing how these systems can perpetuate and amplify societal inequalities. Far from being neutral, algorithms used in areas like hiring and criminal justice often reflect existing biases in data, leading to unfair outcomes. For instance, search algorithms can reinforce stereotypes, while AI-driven hiring processes may discriminate against certain groups due to biased training data. The book argues that algorithmic bias isn't a mere technical glitch but a systemic problem rooted in flawed design and a lack of diverse perspectives. The book takes a comprehensive approach, starting with the fundamental concepts of algorithmic bias and its manifestations. It then delves into specific examples, such as biased search results and discriminatory hiring practices. The analysis extends to the use of algorithms in criminal justice, highlighting how they can perpetuate racial disparities in sentencing. Throughout its chapters, the book uses case studies, empirical research, and statistical analysis to support its arguments, drawing from real-world datasets to illustrate the impact of bias. Ultimately, Algorithm Bias Systems aims to provide practical strategies for mitigating bias, including algorithm auditing, data diversification, and ethical guidelines for AI development. This makes the book uniquely valuable, offering insights for policymakers, data scientists, and anyone concerned about the societal implications of AI and the quest for algorithmic fairness.

Uncovering Bias In Machine Learning

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Author : Ayodele Odubela
language : en
Publisher: Wiley
Release Date : 2021-10-05

Uncovering Bias In Machine Learning written by Ayodele Odubela and has been published by Wiley this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-10-05 with Computers categories.

With machine learning systems becoming more ubiquitous in automated decision making, it is crucial that we make these systems sensitive to the type of bias that results in discrimination, especially discrimination on illegal grounds. Machine learning is already being used to make or assist decisions in the following domains of Recruiting (Screening job applicants), Banking (Credit ratings/Loan approvals), Judiciary (Recidivism risk assessments), Welfare (Welfare Benefit Eligibility), Journalism (News Recommender Systems) etc. Given the scale and impact of these industries, it is crucial that we take measures to prevent unfair discrimination in them via legal as well as technical means. This book will give data scientists and Machine learning engineers insight on how building machine learning models and algorithms can negatively impact users. The book will also provide tools and code examples to help document, identify, and mitigate different types of machine bias. The audience are Data Scientists, Machine Learning Engineers, and Researchers who implement and productionalize machine learning models. This book has been needed for decades because it not only helps the reader understand how human bias slips into models but gives them code and techniques to analyze the models they’ve already built. This book will also give engineers the tools to push back on demands from management that result in harmful models. While this book will focus on machine learning that is used to predict data about users that can be impactful on their lives. Thousands of consumer products use machine learning and these algorithms can cause major damage if influenced by biased data. Google has already classified black people as “gorillas” in Google Photos. Some facial recognition doesn’t even pick up darker toned skin. In terms of trends, ML and AI are by far the hottest fields in computing. The problem with this high-paying, high-growth area is that few practitioners are actually skilled in reducing and mitigating harm caused to users. This book will allow Data Scientists, Machine Learning Engineers, Software Developers, and Researchers alike to apply these explainability steps to their system.

Machine Learning

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Author : William W. Cohen
language : en
Publisher: Morgan Kaufmann Publishers
Release Date : 1994

Machine Learning written by William W. Cohen and has been published by Morgan Kaufmann Publishers this book supported file pdf, txt, epub, kindle and other format this book has been release on 1994 with Computers categories.

Presents 42 papers from the July 1994 conference. Topics covered include improving accuracy of incorrect domain theories, greedy attribute selection, boosting and other machine learning algorithms, incremental reduced-error pruning, learning disjunctive concepts using genetic algorithms, and a Baye

Ethical Considerations And Bias Detection In Artificial Intelligence Machine Learning Applications

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Author : Jayesh Rane
language : en
Publisher: Deep Science Publishing
Release Date : 2025-07-10

Ethical Considerations And Bias Detection In Artificial Intelligence Machine Learning Applications written by Jayesh Rane and has been published by Deep Science Publishing this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-07-10 with Computers categories.

At a time when artificial intelligence (AI) and machine learning (ML) are used to make sensitive societal decisions such as the ones related to criminal justice, healthcare, finance, education, employment, algorithmic fairness and bias mitigation are among the most important but challenging issues at hand. The goal of this book is to provide a holistic view across various disciplines of the ethical base, detection methods, and technical measures for trustworthy AI systems. Starting from a solid foundation of statistical bias, transparency systems and fairness-aware ML models, this book methodically looks at state-of-the-art methodologies, where we highlight their shortcomings and introduce a unified model framework for detecting bias and transparent algorithms. Moving beyond technical diagnoses, it examines key sociotechnical and policy tools that are required to implement AI responsibly, providing guidance to researchers, engineers, policy makers, and organizational leaders. Literature review has been driven following the experimental case, the fairness trade-offs, intersectional bias, explainability and regulatory compliance are discussed in depth by the authors. This work underscores that fairness in automated decision-making systems depends not only on algorithmic accuracy, but also institutional will and stakeholder engagement. The chapters in this book function as both an academic primer and a resourceful handbook, transitioning readers through an ever-growing ethical AI terrain. Whether you are a data scientist building and deploying an algorithm that encourages ethical speech, or a regulator working to create and refine guidelines around such algorithms, this book provides you with both the tools and the understanding you need for ethical technology development and deployment.

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.

Mitigating Phishing Attacks

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Author : Ebrima N. Ceesay
language : en
Publisher:
Release Date : 2008

Mitigating Phishing Attacks written by Ebrima N. Ceesay and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008 with categories.