Automating Inequality


Book Description

WINNER: The 2019 Lillian Smith Book Award, 2018 McGannon Center Book Prize, and shortlisted for the Goddard Riverside Stephan Russo Book Prize for Social Justice Astra Taylor, author of The People's Platform: "The single most important book about technology you will read this year." Dorothy Roberts, author of Killing the Black Body: "A must-read." A powerful investigative look at data-based discrimination?and how technology affects civil and human rights and economic equity The State of Indiana denies one million applications for healthcare, foodstamps and cash benefits in three years—because a new computer system interprets any mistake as “failure to cooperate.” In Los Angeles, an algorithm calculates the comparative vulnerability of tens of thousands of homeless people in order to prioritize them for an inadequate pool of housing resources. In Pittsburgh, a child welfare agency uses a statistical model to try to predict which children might be future victims of abuse or neglect. Since the dawn of the digital age, decision-making in finance, employment, politics, health and human services has undergone revolutionary change. Today, automated systems—rather than humans—control which neighborhoods get policed, which families attain needed resources, and who is investigated for fraud. While we all live under this new regime of data, the most invasive and punitive systems are aimed at the poor. In Automating Inequality, Virginia Eubanks systematically investigates the impacts of data mining, policy algorithms, and predictive risk models on poor and working-class people in America. The book is full of heart-wrenching and eye-opening stories, from a woman in Indiana whose benefits are literally cut off as she lays dying to a family in Pennsylvania in daily fear of losing their daughter because they fit a certain statistical profile. The U.S. has always used its most cutting-edge science and technology to contain, investigate, discipline and punish the destitute. Like the county poorhouse and scientific charity before them, digital tracking and automated decision-making hide poverty from the middle-class public and give the nation the ethical distance it needs to make inhumane choices: which families get food and which starve, who has housing and who remains homeless, and which families are broken up by the state. In the process, they weaken democracy and betray our most cherished national values. This deeply researched and passionate book could not be more timely.




Automated Machine Learning


Book Description

This open access book presents the first comprehensive overview of general methods in Automated Machine Learning (AutoML), collects descriptions of existing systems based on these methods, and discusses the first series of international challenges of AutoML systems. The recent success of commercial ML applications and the rapid growth of the field has created a high demand for off-the-shelf ML methods that can be used easily and without expert knowledge. However, many of the recent machine learning successes crucially rely on human experts, who manually select appropriate ML architectures (deep learning architectures or more traditional ML workflows) and their hyperparameters. To overcome this problem, the field of AutoML targets a progressive automation of machine learning, based on principles from optimization and machine learning itself. This book serves as a point of entry into this quickly-developing field for researchers and advanced students alike, as well as providing a reference for practitioners aiming to use AutoML in their work.




Automated Media


Book Description

In this era of pervasive automation, Mark Andrejevic provides an original framework for tracing the logical trajectory of automated media and their social, political, and cultural consequences. This book explores the cascading logic of automation, which develops from the information collection process through to data processing and, finally, automated decision making. It argues that pervasive digital monitoring combines with algorithmic decision making and machine learning to create new forms of power and control that pose challenges to democratic forms of accountability and individual autonomy alike. Andrejevic provides an overview of the implications of these developments for the fate of human experience, describing the "bias of automation" through the logics of pre-emption, operationalism, and "framelessness." Automated Media is a fascinating and groundbreaking new volume: a must-read for students and researchers of critical media studies interested in the intersections of media, technology, and the digital economy.




Contemporary Empirical Methods in Software Engineering


Book Description

This book presents contemporary empirical methods in software engineering related to the plurality of research methodologies, human factors, data collection and processing, aggregation and synthesis of evidence, and impact of software engineering research. The individual chapters discuss methods that impact the current evolution of empirical software engineering and form the backbone of future research. Following an introductory chapter that outlines the background of and developments in empirical software engineering over the last 50 years and provides an overview of the subsequent contributions, the remainder of the book is divided into four parts: Study Strategies (including e.g. guidelines for surveys or design science); Data Collection, Production, and Analysis (highlighting approaches from e.g. data science, biometric measurement, and simulation-based studies); Knowledge Acquisition and Aggregation (highlighting literature research, threats to validity, and evidence aggregation); and Knowledge Transfer (discussing open science and knowledge transfer with industry). Empirical methods like experimentation have become a powerful means of advancing the field of software engineering by providing scientific evidence on software development, operation, and maintenance, but also by supporting practitioners in their decision-making and learning processes. Thus the book is equally suitable for academics aiming to expand the field and for industrial researchers and practitioners looking for novel ways to check the validity of their assumptions and experiences. Chapter 17 is available open access under a Creative Commons Attribution 4.0 International License via link.springer.com.







Automated Software Testing


Book Description

With the urgent demand for rapid turnaround on new software releases--without compromising quality--the testing element of software development must keep pace, requiring a major shift from slow, labor-intensive testing methods to a faster and more thorough automated testing approach. Automated Software Testing is a comprehensive, step-by-step guide to the most effective tools, techniques, and methods for automated testing. Using numerous case studies of successful industry implementations, this book presents everything you need to know to successfully incorporate automated testing into the development process. In particular, this book focuses on the Automated Test Life Cycle Methodology (ATLM), a structured process for designing and executing testing that parallels the Rapid Application Development methodology commonly used today. Automated Software Testing is designed to lead you through each step of this structured program, from the initial decision to implement automated software testing through test planning, execution, and reporting. Included are test automation and test management guidance for: Acquiring management support Test tool evaluation and selection The automated testing introduction process Test effort and test team sizing Test team composition, recruiting, and management Test planning and preparation Test procedure development guidelines Automation reuse analysis and reuse library Best practices for test automation




Implementing Automated Software Testing


Book Description

“This book fills a huge gap in our knowledge of software testing. It does an excellent job describing how test automation differs from other test activities, and clearly lays out what kind of skills and knowledge are needed to automate tests. The book is essential reading for students of testing and a bible for practitioners.” –Jeff Offutt, Professor of Software Engineering, George Mason University “This new book naturally expands upon its predecessor, Automated Software Testing, and is the perfect reference for software practitioners applying automated software testing to their development efforts. Mandatory reading for software testing professionals!” –Jeff Rashka, PMP, Coauthor of Automated Software Testing and Quality Web Systems Testing accounts for an increasingly large percentage of the time and cost of new software development. Using automated software testing (AST), developers and software testers can optimize the software testing lifecycle and thus reduce cost. As technologies and development grow increasingly complex, AST becomes even more indispensable. This book builds on some of the proven practices and the automated testing lifecycle methodology (ATLM) described in Automated Software Testing and provides a renewed practical, start-to-finish guide to implementing AST successfully. In Implementing Automated Software Testing, three leading experts explain AST in detail, systematically reviewing its components, capabilities, and limitations. Drawing on their experience deploying AST in both defense and commercial industry, they walk you through the entire implementation process–identifying best practices, crucial success factors, and key pitfalls along with solutions for avoiding them. You will learn how to: Make a realistic business case for AST, and use it to drive your initiative Clarify your testing requirements and develop an automation strategy that reflects them Build efficient test environments and choose the right automation tools and techniques for your environment Use proven metrics to continuously track your progress and adjust accordingly Whether you’re a test professional, QA specialist, project manager, or developer, this book can help you bring unprecedented efficiency to testing–and then use AST to improve your entire development lifecycle.




Army Automation Review


Book Description




Handbook of Automated Essay Evaluation


Book Description

This comprehensive, interdisciplinary handbook reviews the latest methods and technologies used in automated essay evaluation (AEE) methods and technologies. Highlights include the latest in the evaluation of performance-based writing assessments and recent advances in the teaching of writing, language testing, cognitive psychology, and computational linguistics. This greatly expanded follow-up to Automated Essay Scoring reflects the numerous advances that have taken place in the field since 2003 including automated essay scoring and diagnostic feedback. Each chapter features a common structure including an introduction and a conclusion. Ideas for diagnostic and evaluative feedback are sprinkled throughout the book. Highlights of the book’s coverage include: The latest research on automated essay evaluation. Descriptions of the major scoring engines including the E-rater®, the Intelligent Essay Assessor, the IntellimetricTM Engine, c-raterTM, and LightSIDE. Applications of the uses of the technology including a large scale system used in West Virginia. A systematic framework for evaluating research and technological results. Descriptions of AEE methods that can be replicated for languages other than English as seen in the example from China. Chapters from key researchers in the field. The book opens with an introduction to AEEs and a review of the "best practices" of teaching writing along with tips on the use of automated analysis in the classroom. Next the book highlights the capabilities and applications of several scoring engines including the E-rater®, the Intelligent Essay Assessor, the IntellimetricTM engine, c-raterTM, and LightSIDE. Here readers will find an actual application of the use of an AEE in West Virginia, psychometric issues related to AEEs such as validity, reliability, and scaling, and the use of automated scoring to detect reader drift, grammatical errors, discourse coherence quality, and the impact of human rating on AEEs. A review of the cognitive foundations underlying methods used in AEE is also provided. The book concludes with a comparison of the various AEE systems and speculation about the future of the field in light of current educational policy. Ideal for educators, professionals, curriculum specialists, and administrators responsible for developing writing programs or distance learning curricula, those who teach using AEE technologies, policy makers, and researchers in education, writing, psychometrics, cognitive psychology, and computational linguistics, this book also serves as a reference for graduate courses on automated essay evaluation taught in education, computer science, language, linguistics, and cognitive psychology.