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.
Learn to employ non-discriminatory, ethical, and legal machine learning procedures 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. You will explore the many different kinds of bias that occur in the field today and get mitigation strategies that are ready to deploy across a wide range of technologies, applications, and industries. Edited by diversity experts, Eliminating Bias in Machine Learning with Practical Applications consists of chapters contributed by recognized scholars and professionals working across different artificial intelligence sectors. Each chapter addresses a different topic—including robotics, machine learning, deep learning, and natural language processing—and lays out the potentials for bias and ways of eliminating it. You will get real-world case studies throughout that highlight discriminatory machine learning practices and clearly show how they were eliminated. Offers cross-sector coverage that is applicable across multiple industries Includes online laboratory assignments, simulations, slides, video tutorials, and a code library Written by a team of experienced academics and industry leaders
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.
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.
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.
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.
Big Data And Social Science
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Author : Ian Foster
language : en
Publisher: CRC Press
Release Date : 2016-08-10
Big Data And Social Science written by Ian Foster and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-08-10 with Mathematics categories.
Both Traditional Students and Working Professionals Acquire the Skills to Analyze Social Problems. Big Data and Social Science: A Practical Guide to Methods and Tools shows how to apply data science to real-world problems in both research and the practice. The book provides practical guidance on combining methods and tools from computer science, statistics, and social science. This concrete approach is illustrated throughout using an important national problem, the quantitative study of innovation. The text draws on the expertise of prominent leaders in statistics, the social sciences, data science, and computer science to teach students how to use modern social science research principles as well as the best analytical and computational tools. It uses a real-world challenge to introduce how these tools are used to identify and capture appropriate data, apply data science models and tools to that data, and recognize and respond to data errors and limitations. For more information, including sample chapters and news, please visit the author's website.
Developing And Evaluating Methods For Mitigating Sample Selection Bias In Machine Learning
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Author : Lourdes Pelayo Ramirez
language : en
Publisher:
Release Date : 2013
Developing And Evaluating Methods For Mitigating Sample Selection Bias In Machine Learning written by Lourdes Pelayo Ramirez and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013 with categories.
Machine Learning For High Risk Applications
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Author : Patrick Hall
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2023-04-17
Machine Learning For High Risk Applications written by Patrick Hall and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-04-17 with Computers categories.
The past decade has witnessed the broad adoption of artificial intelligence and machine learning (AI/ML) technologies. However, a lack of oversight in their widespread implementation has resulted in some incidents and harmful outcomes that could have been avoided with proper risk management. Before we can realize AI/ML's true benefit, practitioners must understand how to mitigate its risks. This book describes approaches to responsible AI—a holistic framework for improving AI/ML technology, business processes, and cultural competencies that builds on best practices in risk management, cybersecurity, data privacy, and applied social science. Authors Patrick Hall, James Curtis, and Parul Pandey created this guide for data scientists who want to improve real-world AI/ML system outcomes for organizations, consumers, and the public. Learn technical approaches for responsible AI across explainability, model validation and debugging, bias management, data privacy, and ML security Learn how to create a successful and impactful AI risk management practice Get a basic guide to existing standards, laws, and assessments for adopting AI technologies, including the new NIST AI Risk Management Framework Engage with interactive resources on GitHub and Colab
Interpretable Ai
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Author : Ajay Thampi
language : en
Publisher: Simon and Schuster
Release Date : 2022-07-26
Interpretable Ai written by Ajay Thampi and has been published by Simon and Schuster this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-07-26 with Computers categories.
AI doesn’t have to be a black box. These practical techniques help shine a light on your model’s mysterious inner workings. Make your AI more transparent, and you’ll improve trust in your results, combat data leakage and bias, and ensure compliance with legal requirements. In Interpretable AI, you will learn: Why AI models are hard to interpret Interpreting white box models such as linear regression, decision trees, and generalized additive models Partial dependence plots, LIME, SHAP and Anchors, and other techniques such as saliency mapping, network dissection, and representational learning What fairness is and how to mitigate bias in AI systems Implement robust AI systems that are GDPR-compliant Interpretable AI opens up the black box of your AI models. It teaches cutting-edge techniques and best practices that can make even complex AI systems interpretable. Each method is easy to implement with just Python and open source libraries. You’ll learn to identify when you can utilize models that are inherently transparent, and how to mitigate opacity when your problem demands the power of a hard-to-interpret deep learning model. About the technology It’s often difficult to explain how deep learning models work, even for the data scientists who create them. Improving transparency and interpretability in machine learning models minimizes errors, reduces unintended bias, and increases trust in the outcomes. This unique book contains techniques for looking inside “black box” models, designing accountable algorithms, and understanding the factors that cause skewed results. About the book Interpretable AI teaches you to identify the patterns your model has learned and why it produces its results. As you read, you’ll pick up algorithm-specific approaches, like interpreting regression and generalized additive models, along with tips to improve performance during training. You’ll also explore methods for interpreting complex deep learning models where some processes are not easily observable. AI transparency is a fast-moving field, and this book simplifies cutting-edge research into practical methods you can implement with Python. What's inside Techniques for interpreting AI models Counteract errors from bias, data leakage, and concept drift Measuring fairness and mitigating bias Building GDPR-compliant AI systems About the reader For data scientists and engineers familiar with Python and machine learning. About the author Ajay Thampi is a machine learning engineer focused on responsible AI and fairness. Table of Contents PART 1 INTERPRETABILITY BASICS 1 Introduction 2 White-box models PART 2 INTERPRETING MODEL PROCESSING 3 Model-agnostic methods: Global interpretability 4 Model-agnostic methods: Local interpretability 5 Saliency mapping PART 3 INTERPRETING MODEL REPRESENTATIONS 6 Understanding layers and units 7 Understanding semantic similarity PART 4 FAIRNESS AND BIAS 8 Fairness and mitigating bias 9 Path to explainable AI