Download or read book Mitigating Bias in Machine Learning written by Carlotta A. Berry and published by McGraw Hill Professional. This book was released on 2024-10-18 with total page 249 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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
Download or read book Big Data and Social Science written by Ian Foster and published by CRC Press. This book was released on 2016-08-10 with total page 493 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Human Compatible written by Stuart Jonathan Russell and published by Penguin Books. This book was released on 2019 with total page 354 pages. Available in PDF, EPUB and Kindle. Book excerpt: A leading artificial intelligence researcher lays out a new approach to AI that will enable people to coexist successfully with increasingly intelligent machines.
Download or read book Model Behavior written by Sara Kassir and published by . This book was released on 2019 with total page 50 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Understanding and Mitigating Unintended Demographic Bias in Machine Learning Systems written by Christopher J. Sweeney (M. Eng.) and published by . This book was released on 2019 with total page 84 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book An Intelligence in Our Image written by Osonde A. Osoba and published by Rand Corporation. This book was released on 2017-04-05 with total page 45 pages. Available in PDF, EPUB and Kindle. Book excerpt: Machine learning algorithms and artificial intelligence influence many aspects of life today. This report identifies some of their shortcomings and associated policy risks and examines some approaches for combating these problems.
Download or read book Data Quality and Artificial Intelligence written by and published by . This book was released on 2019 with total page 18 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book The Ethical Algorithm written by Michael Kearns and published by Oxford University Press. This book was released on 2019-10-04 with total page 288 pages. Available in PDF, EPUB and Kindle. Book excerpt: Over the course of a generation, algorithms have gone from mathematical abstractions to powerful mediators of daily life. Algorithms have made our lives more efficient, more entertaining, and, sometimes, better informed. At the same time, complex algorithms are increasingly violating the basic rights of individual citizens. Allegedly anonymized datasets routinely leak our most sensitive personal information; statistical models for everything from mortgages to college admissions reflect racial and gender bias. Meanwhile, users manipulate algorithms to "game" search engines, spam filters, online reviewing services, and navigation apps. Understanding and improving the science behind the algorithms that run our lives is rapidly becoming one of the most pressing issues of this century. Traditional fixes, such as laws, regulations and watchdog groups, have proven woefully inadequate. Reporting from the cutting edge of scientific research, The Ethical Algorithm offers a new approach: a set of principled solutions based on the emerging and exciting science of socially aware algorithm design. Michael Kearns and Aaron Roth explain how we can better embed human principles into machine code - without halting the advance of data-driven scientific exploration. Weaving together innovative research with stories of citizens, scientists, and activists on the front lines, The Ethical Algorithm offers a compelling vision for a future, one in which we can better protect humans from the unintended impacts of algorithms while continuing to inspire wondrous advances in technology.
Download or read book Information Science and Applications ICISA 2016 written by Kuinam J. Kim and published by Springer. This book was released on 2016-02-15 with total page 1439 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book contains selected papers from the 7th International Conference on Information Science and Applications (ICISA 2016) and provides a snapshot of the latest issues encountered in technical convergence and convergences of security technology. It explores how information science is core to most current research, industrial and commercial activities and consists of contributions covering topics including Ubiquitous Computing, Networks and Information Systems, Multimedia and Visualization, Middleware and Operating Systems, Security and Privacy, Data Mining and Artificial Intelligence, Software Engineering, and Web Technology. The contributions describe the most recent developments in information technology and ideas, applications and problems related to technology convergence, illustrated through case studies, and reviews converging existing security techniques. Through this volume, readers will gain an understanding of the current state-of-the-art information strategies and technologies of convergence security. The intended readers are researchers in academia, industry and other research institutes focusing on information science and technology.
Download or read book Interpretable Machine Learning written by Christoph Molnar and published by Lulu.com. This book was released on 2020 with total page 320 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is about making machine learning models and their decisions interpretable. After exploring the concepts of interpretability, you will learn about simple, interpretable models such as decision trees, decision rules and linear regression. Later chapters focus on general model-agnostic methods for interpreting black box models like feature importance and accumulated local effects and explaining individual predictions with Shapley values and LIME. All interpretation methods are explained in depth and discussed critically. How do they work under the hood? What are their strengths and weaknesses? How can their outputs be interpreted? This book will enable you to select and correctly apply the interpretation method that is most suitable for your machine learning project.
Download or read book Value Sensitive Design written by Batya Friedman and published by MIT Press. This book was released on 2019-05-21 with total page 258 pages. Available in PDF, EPUB and Kindle. Book excerpt: Using our moral and technical imaginations to create responsible innovations: theory, method, and applications for value sensitive design. Implantable medical devices and human dignity. Private and secure access to information. Engineering projects that transform the Earth. Multigenerational information systems for international justice. How should designers, engineers, architects, policy makers, and others design such technology? Who should be involved and what values are implicated? In Value Sensitive Design, Batya Friedman and David Hendry describe how both moral and technical imagination can be brought to bear on the design of technology. With value sensitive design, under development for more than two decades, Friedman and Hendry bring together theory, methods, and applications for a design process that engages human values at every stage. After presenting the theoretical foundations of value sensitive design, which lead to a deep rethinking of technical design, Friedman and Hendry explain seventeen methods, including stakeholder analysis, value scenarios, and multilifespan timelines. Following this, experts from ten application domains report on value sensitive design practice. Finally, Friedman and Hendry explore such open questions as the need for deeper investigation of indirect stakeholders and further method development. This definitive account of the state of the art in value sensitive design is an essential resource for designers and researchers working in academia and industry, students in design and computer science, and anyone working at the intersection of technology and society.
Download or read book Beyond D I written by Kay Formanek and published by Springer Nature. This book was released on 2021-11-10 with total page 321 pages. Available in PDF, EPUB and Kindle. Book excerpt: D&I is no longer a passing fad. It’s not about legal compliance or HR box-ticking, in fact diversity and inclusion is a critical factor for success. #MeToo, #BlackLivesMatter and the ballooning disparate consequences of Covid-19 on minorities brings renewed emphasis on D&I agendas, and the economic reality that diverse talent is good for business and good for sustainability. In Beyond D&I, Kay Formanek brings her more than twenty years’ experience working with the world’s leading organizations to take diversity and inclusion into the strategic roadmap of the organization. Whether you’re a leader, HR practitioner, sponsor of a D&I initiative or an employee who wants to see your organization benefit from more inclusivity, the book equips you with the tools you need to develop the strategic case for diversity, craft a compelling narrative and chart a tailored roadmap to lock in diversity gains and close key performance gaps. As well as two core anchor models—the Virtuous Circle and Integrated Diversity Model— the book features case studies, profiles of inclusive leaders, engaging and intuitive visuals and a wealth of evidence-based initiatives that you can start implementing today. With five essential elements and six core capabilities, the result is a definitive, holistic and practical guide that will help you convert your D&I initiatives into sustainable diversity performance.
Download or read book The Oxford Handbook of Ethics of AI written by Markus Dirk Dubber and published by Oxford Handbooks. This book was released on 2020 with total page 896 pages. Available in PDF, EPUB and Kindle. Book excerpt: This interdisciplinary and international handbook captures and shapes much needed reflection on normative frameworks for the production, application, and use of artificial intelligence in all spheres of individual, commercial, social, and public life.
Download or read book Powering the Digital Economy Opportunities and Risks of Artificial Intelligence in Finance written by El Bachir Boukherouaa and published by International Monetary Fund. This book was released on 2021-10-22 with total page 35 pages. Available in PDF, EPUB and Kindle. Book excerpt: This paper discusses the impact of the rapid adoption of artificial intelligence (AI) and machine learning (ML) in the financial sector. It highlights the benefits these technologies bring in terms of financial deepening and efficiency, while raising concerns about its potential in widening the digital divide between advanced and developing economies. The paper advances the discussion on the impact of this technology by distilling and categorizing the unique risks that it could pose to the integrity and stability of the financial system, policy challenges, and potential regulatory approaches. The evolving nature of this technology and its application in finance means that the full extent of its strengths and weaknesses is yet to be fully understood. Given the risk of unexpected pitfalls, countries will need to strengthen prudential oversight.
Download or read book Interpretable Machine Learning with Python written by Serg Masís and published by Packt Publishing Ltd. This book was released on 2021-03-26 with total page 737 pages. Available in PDF, EPUB and Kindle. Book excerpt: A deep and detailed dive into the key aspects and challenges of machine learning interpretability, complete with the know-how on how to overcome and leverage them to build fairer, safer, and more reliable models Key Features Learn how to extract easy-to-understand insights from any machine learning model Become well-versed with interpretability techniques to build fairer, safer, and more reliable models Mitigate risks in AI systems before they have broader implications by learning how to debug black-box models Book DescriptionDo you want to gain a deeper understanding of your models and better mitigate poor prediction risks associated with machine learning interpretation? If so, then Interpretable Machine Learning with Python deserves a place on your bookshelf. We’ll be starting off with the fundamentals of interpretability, its relevance in business, and exploring its key aspects and challenges. As you progress through the chapters, you'll then focus on how white-box models work, compare them to black-box and glass-box models, and examine their trade-off. You’ll also get you up to speed with a vast array of interpretation methods, also known as Explainable AI (XAI) methods, and how to apply them to different use cases, be it for classification or regression, for tabular, time-series, image or text. In addition to the step-by-step code, this book will also help you interpret model outcomes using examples. You’ll get hands-on with tuning models and training data for interpretability by reducing complexity, mitigating bias, placing guardrails, and enhancing reliability. The methods you’ll explore here range from state-of-the-art feature selection and dataset debiasing methods to monotonic constraints and adversarial retraining. By the end of this book, you'll be able to understand ML models better and enhance them through interpretability tuning. What you will learn Recognize the importance of interpretability in business Study models that are intrinsically interpretable such as linear models, decision trees, and Naïve Bayes Become well-versed in interpreting models with model-agnostic methods Visualize how an image classifier works and what it learns Understand how to mitigate the influence of bias in datasets Discover how to make models more reliable with adversarial robustness Use monotonic constraints to make fairer and safer models Who this book is for This book is primarily written for data scientists, machine learning developers, and data stewards who find themselves under increasing pressures to explain the workings of AI systems, their impacts on decision making, and how they identify and manage bias. It’s also a useful resource for self-taught ML enthusiasts and beginners who want to go deeper into the subject matter, though a solid grasp on the Python programming language and ML fundamentals is needed to follow along.
Download or read book Computer Vision ECCV 2018 Workshops written by Laura Leal-Taixé and published by Springer. This book was released on 2019-01-22 with total page 750 pages. Available in PDF, EPUB and Kindle. Book excerpt: The six-volume set comprising the LNCS volumes 11129-11134 constitutes the refereed proceedings of the workshops that took place in conjunction with the 15th European Conference on Computer Vision, ECCV 2018, held in Munich, Germany, in September 2018.43 workshops from 74 workshops proposals were selected for inclusion in the proceedings. The workshop topics present a good orchestration of new trends and traditional issues, built bridges into neighboring fields, and discuss fundamental technologies and novel applications.
Download or read book Government Policy toward Open Source Software written by Robert W. Hahn and published by Rowman & Littlefield. This book was released on 2010-12-01 with total page 128 pages. Available in PDF, EPUB and Kindle. Book excerpt: Can open source software—software that is usually available without charge and that individuals are free to modify—survive against the fierce competition of proprietary software, such as Microsoft Windows? Should the government intervene on its behalf? This book addresses a host of issues raised by the rapid growth of open source software, including government subsidies for research and development, government procurement policy, and patent and copyright policy. Contributors offer diverse perspectives on a phenomenon that has become a lightning rod for controversy in the field of information technology. Contributors include James Bessen (Research on Innovation), David S. Evans (National Economic Research Associates), Lawrence Lessig (Stanford University), Bradford L. Smith (Microsoft Corporation), and Robert W. Hahn (director, AEI-Brookings Joint Center).
