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| bias in data science: Understand, Manage, and Prevent Algorithmic Bias Tobias Baer, 2019-06-07 Are algorithms friend or foe? The human mind is evolutionarily designed to take shortcuts in order to survive. We jump to conclusions because our brains want to keep us safe. A majority of our biases work in our favor, such as when we feel a car speeding in our direction is dangerous and we instantly move, or when we decide not take a bite of food that appears to have gone bad. However, inherent bias negatively affects work environments and the decision-making surrounding our communities. While the creation of algorithms and machine learning attempts to eliminate bias, they are, after all, created by human beings, and thus are susceptible to what we call algorithmic bias. In Understand, Manage, and Prevent Algorithmic Bias, author Tobias Baer helps you understand where algorithmic bias comes from, how to manage it as a business user or regulator, and how data science can prevent bias from entering statistical algorithms. Baer expertly addresses some of the 100+ varieties of natural bias such as confirmation bias, stability bias, pattern-recognition bias, and many others. Algorithmic bias mirrors—and originates in—these human tendencies. Baer dives into topics as diverse as anomaly detection, hybrid model structures, and self-improving machine learning. While most writings on algorithmic bias focus on the dangers, the core of this positive, fun book points toward a path where bias is kept at bay and even eliminated. You’ll come away with managerial techniques to develop unbiased algorithms, the ability to detect bias more quickly, and knowledge to create unbiased data. Understand, Manage, and Prevent Algorithmic Bias is an innovative, timely, and important book that belongs on your shelf. Whether you are a seasoned business executive, a data scientist, or simply an enthusiast, now is a crucial time to be educated about the impact of algorithmic bias on society and take an active role in fighting bias. What You'll Learn Study the many sources of algorithmic bias, including cognitive biases in the real world, biased data, and statistical artifact Understand the risks of algorithmic biases, how to detect them, and managerial techniques to prevent or manage them Appreciate how machine learning both introduces new sources of algorithmic bias and can be a part of a solutionBe familiar with specific statistical techniques a data scientist can use to detect and overcome algorithmic bias Who This Book is For Business executives of companies using algorithms in daily operations; data scientists (from students to seasoned practitioners) developing algorithms; compliance officials concerned about algorithmic bias; politicians, journalists, and philosophers thinking about algorithmic bias in terms of its impact on society and possible regulatory responses; and consumers concerned about how they might be affected by algorithmic bias |
| bias in data science: Handbook of Research on Engineering Innovations and Technology Management in Organizations Gaur, Loveleen, Solanki, Arun, Jain, Vishal, Khazanchi, Deepak, 2020-04-17 As technology weaves itself more tightly into everyday life, socio-economic development has become intricately tied to these ever-evolving innovations. Technology management is now an integral element of sound business practices, and this revolution has opened up many opportunities for global communication. However, such swift change warrants greater research that can foresee and possibly prevent future complications within and between organizations. The Handbook of Research on Engineering Innovations and Technology Management in Organizations is a collection of innovative research that explores global concerns in the applications of technology to business and the explosive growth that resulted. Highlighting a wide range of topics such as cyber security, legal practice, and artificial intelligence, this book is ideally designed for engineers, manufacturers, technology managers, technology developers, IT specialists, productivity consultants, executives, lawyers, programmers, managers, policymakers, academicians, researchers, and students. |
| bias in data science: Big Data and Social Science Ian Foster, Rayid Ghani, Ron S. Jarmin, Frauke Kreuter, Julia Lane, 2016-08-10 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. |
| bias in data science: Introduction to Data Science Rafael A. Irizarry, 2019-11-20 Introduction to Data Science: Data Analysis and Prediction Algorithms with R introduces concepts and skills that can help you tackle real-world data analysis challenges. It covers concepts from probability, statistical inference, linear regression, and machine learning. It also helps you develop skills such as R programming, data wrangling, data visualization, predictive algorithm building, file organization with UNIX/Linux shell, version control with Git and GitHub, and reproducible document preparation. This book is a textbook for a first course in data science. No previous knowledge of R is necessary, although some experience with programming may be helpful. The book is divided into six parts: R, data visualization, statistics with R, data wrangling, machine learning, and productivity tools. Each part has several chapters meant to be presented as one lecture. The author uses motivating case studies that realistically mimic a data scientist’s experience. He starts by asking specific questions and answers these through data analysis so concepts are learned as a means to answering the questions. Examples of the case studies included are: US murder rates by state, self-reported student heights, trends in world health and economics, the impact of vaccines on infectious disease rates, the financial crisis of 2007-2008, election forecasting, building a baseball team, image processing of hand-written digits, and movie recommendation systems. The statistical concepts used to answer the case study questions are only briefly introduced, so complementing with a probability and statistics textbook is highly recommended for in-depth understanding of these concepts. If you read and understand the chapters and complete the exercises, you will be prepared to learn the more advanced concepts and skills needed to become an expert. |
| bias in data science: An Intelligence in Our Image Osonde A. Osoba, William Welser IV, William Welser, 2017-04-05 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. |
| bias in data science: Weapons of Math Destruction Cathy O'Neil, 2016 A former Wall Street quantitative analyst sounds an alarm on mathematical modeling, a pervasive new force in society that threatens to undermine democracy and widen inequality,--NoveList. |
| bias in data science: Machine Learning Engineering Andriy Burkov, 2020-09-08 The most comprehensive book on the engineering aspects of building reliable AI systems. If you intend to use machine learning to solve business problems at scale, I'm delighted you got your hands on this book. -Cassie Kozyrkov, Chief Decision Scientist at Google Foundational work about the reality of building machine learning models in production. -Karolis Urbonas, Head of Machine Learning and Science at Amazon |
| bias in data science: Fundamentals of Clinical Data Science Pieter Kubben, Michel Dumontier, Andre Dekker, 2018-12-21 This open access book comprehensively covers the fundamentals of clinical data science, focusing on data collection, modelling and clinical applications. Topics covered in the first section on data collection include: data sources, data at scale (big data), data stewardship (FAIR data) and related privacy concerns. Aspects of predictive modelling using techniques such as classification, regression or clustering, and prediction model validation will be covered in the second section. The third section covers aspects of (mobile) clinical decision support systems, operational excellence and value-based healthcare. Fundamentals of Clinical Data Science is an essential resource for healthcare professionals and IT consultants intending to develop and refine their skills in personalized medicine, using solutions based on large datasets from electronic health records or telemonitoring programmes. The book’s promise is “no math, no code”and will explain the topics in a style that is optimized for a healthcare audience. |
| bias in data science: Applying Quantitative Bias Analysis to Epidemiologic Data Timothy L. Lash, Matthew P. Fox, Aliza K. Fink, 2011-04-14 Bias analysis quantifies the influence of systematic error on an epidemiology study’s estimate of association. The fundamental methods of bias analysis in epi- miology have been well described for decades, yet are seldom applied in published presentations of epidemiologic research. More recent advances in bias analysis, such as probabilistic bias analysis, appear even more rarely. We suspect that there are both supply-side and demand-side explanations for the scarcity of bias analysis. On the demand side, journal reviewers and editors seldom request that authors address systematic error aside from listing them as limitations of their particular study. This listing is often accompanied by explanations for why the limitations should not pose much concern. On the supply side, methods for bias analysis receive little attention in most epidemiology curriculums, are often scattered throughout textbooks or absent from them altogether, and cannot be implemented easily using standard statistical computing software. Our objective in this text is to reduce these supply-side barriers, with the hope that demand for quantitative bias analysis will follow. |
| bias in data science: Invisible Women Caroline Criado Perez, 2019-03-12 The landmark, prize-winning, international bestselling examination of how a gender gap in data perpetuates bias and disadvantages women. #1 International Bestseller * Winner of the Financial Times and McKinsey Business Book of the Year Award * Winner of the Royal Society Science Book Prize Data is fundamental to the modern world. From economic development to health care to education and public policy, we rely on numbers to allocate resources and make crucial decisions. But because so much data fails to take into account gender, because it treats men as the default and women as atypical, bias and discrimination are baked into our systems. And women pay tremendous costs for this insidious bias: in time, in money, and often with their lives. Celebrated feminist advocate Caroline Criado Perez investigates this shocking root cause of gender inequality in Invisible Women. Examining the home, the workplace, the public square, the doctor’s office, and more, Criado Perez unearths a dangerous pattern in data and its consequences on women’s lives. Product designers use a “one-size-fits-all” approach to everything from pianos to cell phones to voice recognition software, when in fact this approach is designed to fit men. Cities prioritize men’s needs when designing public transportation, roads, and even snow removal, neglecting to consider women’s safety or unique responsibilities and travel patterns. And in medical research, women have largely been excluded from studies and textbooks, leaving them chronically misunderstood, mistreated, and misdiagnosed. Built on hundreds of studies in the United States, in the United Kingdom, and around the world, and written with energy, wit, and sparkling intelligence, this is a groundbreaking, highly readable exposé that will change the way you look at the world. |
| bias in data science: Human-Centered Data Science Cecilia Aragon, Shion Guha, Marina Kogan, Michael Muller, Gina Neff, 2022-03-01 Best practices for addressing the bias and inequality that may result from the automated collection, analysis, and distribution of large datasets. Human-centered data science is a new interdisciplinary field that draws from human-computer interaction, social science, statistics, and computational techniques. This book, written by founders of the field, introduces best practices for addressing the bias and inequality that may result from the automated collection, analysis, and distribution of very large datasets. It offers a brief and accessible overview of many common statistical and algorithmic data science techniques, explains human-centered approaches to data science problems, and presents practical guidelines and real-world case studies to help readers apply these methods. The authors explain how data scientists’ choices are involved at every stage of the data science workflow—and show how a human-centered approach can enhance each one, by making the process more transparent, asking questions, and considering the social context of the data. They describe how tools from social science might be incorporated into data science practices, discuss different types of collaboration, and consider data storytelling through visualization. The book shows that data science practitioners can build rigorous and ethical algorithms and design projects that use cutting-edge computational tools and address social concerns. |
| bias in data science: Algorithms of Oppression Safiya Umoja Noble, 2018-02-20 Acknowledgments -- Introduction: the power of algorithms -- A society, searching -- Searching for Black girls -- Searching for people and communities -- Searching for protections from search engines -- The future of knowledge in the public -- The future of information culture -- Conclusion: algorithms of oppression -- Epilogue -- Notes -- Bibliography -- Index -- About the author |
| bias in data science: Data Feminism Catherine D'Ignazio, Lauren F. Klein, 2020-03-31 A new way of thinking about data science and data ethics that is informed by the ideas of intersectional feminism. Today, data science is a form of power. It has been used to expose injustice, improve health outcomes, and topple governments. But it has also been used to discriminate, police, and surveil. This potential for good, on the one hand, and harm, on the other, makes it essential to ask: Data science by whom? Data science for whom? Data science with whose interests in mind? The narratives around big data and data science are overwhelmingly white, male, and techno-heroic. In Data Feminism, Catherine D'Ignazio and Lauren Klein present a new way of thinking about data science and data ethics—one that is informed by intersectional feminist thought. Illustrating data feminism in action, D'Ignazio and Klein show how challenges to the male/female binary can help challenge other hierarchical (and empirically wrong) classification systems. They explain how, for example, an understanding of emotion can expand our ideas about effective data visualization, and how the concept of invisible labor can expose the significant human efforts required by our automated systems. And they show why the data never, ever “speak for themselves.” Data Feminism offers strategies for data scientists seeking to learn how feminism can help them work toward justice, and for feminists who want to focus their efforts on the growing field of data science. But Data Feminism is about much more than gender. It is about power, about who has it and who doesn't, and about how those differentials of power can be challenged and changed. |
| bias in data science: The Oxford Handbook of the Science of Science Communication Kathleen Hall Jamieson, Dan M. Kahan, Dietram Scheufele, 2017 On topics from genetic engineering and mad cow disease to vaccination and climate change, this Handbook draws on the insights of 57 leading science of science communication scholars who explore what social scientists know about how citizens come to understand and act on what is known by science. |
| bias in data science: Responsible Data Science Peter C. Bruce, Grant Fleming, 2021-04-13 Explore the most serious prevalent ethical issues in data science with this insightful new resource The increasing popularity of data science has resulted in numerous well-publicized cases of bias, injustice, and discrimination. The widespread deployment of “Black box” algorithms that are difficult or impossible to understand and explain, even for their developers, is a primary source of these unanticipated harms, making modern techniques and methods for manipulating large data sets seem sinister, even dangerous. When put in the hands of authoritarian governments, these algorithms have enabled suppression of political dissent and persecution of minorities. To prevent these harms, data scientists everywhere must come to understand how the algorithms that they build and deploy may harm certain groups or be unfair. Responsible Data Science delivers a comprehensive, practical treatment of how to implement data science solutions in an even-handed and ethical manner that minimizes the risk of undue harm to vulnerable members of society. Both data science practitioners and managers of analytics teams will learn how to: Improve model transparency, even for black box models Diagnose bias and unfairness within models using multiple metrics Audit projects to ensure fairness and minimize the possibility of unintended harm Perfect for data science practitioners, Responsible Data Science will also earn a spot on the bookshelves of technically inclined managers, software developers, and statisticians. |
| bias in data science: Digital Witness Sam Dubberley, Alexa Koenig, Daragh Murray, 2020 This book covers the developing field of open source research and discusses how to use social media, satellite imagery, big data analytics, and user-generated content to strengthen human rights research and investigations. The topics are presented in an accessible format through extensive use of images and data visualization. |
| bias in data science: Foundations of Data Science Avrim Blum, John Hopcroft, Ravindran Kannan, 2020-01-23 This book provides an introduction to the mathematical and algorithmic foundations of data science, including machine learning, high-dimensional geometry, and analysis of large networks. Topics include the counterintuitive nature of data in high dimensions, important linear algebraic techniques such as singular value decomposition, the theory of random walks and Markov chains, the fundamentals of and important algorithms for machine learning, algorithms and analysis for clustering, probabilistic models for large networks, representation learning including topic modelling and non-negative matrix factorization, wavelets and compressed sensing. Important probabilistic techniques are developed including the law of large numbers, tail inequalities, analysis of random projections, generalization guarantees in machine learning, and moment methods for analysis of phase transitions in large random graphs. Additionally, important structural and complexity measures are discussed such as matrix norms and VC-dimension. This book is suitable for both undergraduate and graduate courses in the design and analysis of algorithms for data. |
| bias in data science: Encyclopedia of Data Science and Machine Learning Wang, John, 2023-01-20 Big data and machine learning are driving the Fourth Industrial Revolution. With the age of big data upon us, we risk drowning in a flood of digital data. Big data has now become a critical part of both the business world and daily life, as the synthesis and synergy of machine learning and big data has enormous potential. Big data and machine learning are projected to not only maximize citizen wealth, but also promote societal health. As big data continues to evolve and the demand for professionals in the field increases, access to the most current information about the concepts, issues, trends, and technologies in this interdisciplinary area is needed. The Encyclopedia of Data Science and Machine Learning examines current, state-of-the-art research in the areas of data science, machine learning, data mining, and more. It provides an international forum for experts within these fields to advance the knowledge and practice in all facets of big data and machine learning, emphasizing emerging theories, principals, models, processes, and applications to inspire and circulate innovative findings into research, business, and communities. Covering topics such as benefit management, recommendation system analysis, and global software development, this expansive reference provides a dynamic resource for data scientists, data analysts, computer scientists, technical managers, corporate executives, students and educators of higher education, government officials, researchers, and academicians. |
| bias in data science: On Being a Data Skeptic Cathy O'Neil, 2013-09-30 Data is here, it's growing, and it's powerful. Author Cathy O'Neil argues that the right approach to data is skeptical, not cynical––it understands that, while powerful, data science tools often fail. Data is nuanced, and a really excellent skeptic puts the term 'science' into 'data science.' The big data revolution shouldn't be dismissed as hype, but current data science tools and models shouldn't be hailed as the end-all-be-all, either. |
| bias in data science: How to Lie with Statistics Darrell Huff, 2010-12-07 If you want to outsmart a crook, learn his tricks—Darrell Huff explains exactly how in the classic How to Lie with Statistics. From distorted graphs and biased samples to misleading averages, there are countless statistical dodges that lend cover to anyone with an ax to grind or a product to sell. With abundant examples and illustrations, Darrell Huff’s lively and engaging primer clarifies the basic principles of statistics and explains how they’re used to present information in honest and not-so-honest ways. Now even more indispensable in our data-driven world than it was when first published, How to Lie with Statistics is the book that generations of readers have relied on to keep from being fooled. |
| bias in data science: Public Policy Analytics Ken Steif, 2021-08-18 Public Policy Analytics: Code & Context for Data Science in Government teaches readers how to address complex public policy problems with data and analytics using reproducible methods in R. Each of the eight chapters provides a detailed case study, showing readers: how to develop exploratory indicators; understand ‘spatial process’ and develop spatial analytics; how to develop ‘useful’ predictive analytics; how to convey these outputs to non-technical decision-makers through the medium of data visualization; and why, ultimately, data science and ‘Planning’ are one and the same. A graduate-level introduction to data science, this book will appeal to researchers and data scientists at the intersection of data analytics and public policy, as well as readers who wish to understand how algorithms will affect the future of government. |
| bias in data science: Proceedings of the 8th ACM Conference on Web Science Wolfgang Nejdl, 2016-05-22 WebSci '16: ACM Web Science Conference May 22, 2016-May 25, 2016 Hannover, Germany. You can view more information about this proceeding and all of ACM�s other published conference proceedings from the ACM Digital Library: http://www.acm.org/dl. |
| bias in data science: Naked Statistics: Stripping the Dread from the Data Charles Wheelan, 2013-01-07 A New York Times bestseller Brilliant, funny…the best math teacher you never had. —San Francisco Chronicle Once considered tedious, the field of statistics is rapidly evolving into a discipline Hal Varian, chief economist at Google, has actually called sexy. From batting averages and political polls to game shows and medical research, the real-world application of statistics continues to grow by leaps and bounds. How can we catch schools that cheat on standardized tests? How does Netflix know which movies you’ll like? What is causing the rising incidence of autism? As best-selling author Charles Wheelan shows us in Naked Statistics, the right data and a few well-chosen statistical tools can help us answer these questions and more. For those who slept through Stats 101, this book is a lifesaver. Wheelan strips away the arcane and technical details and focuses on the underlying intuition that drives statistical analysis. He clarifies key concepts such as inference, correlation, and regression analysis, reveals how biased or careless parties can manipulate or misrepresent data, and shows us how brilliant and creative researchers are exploiting the valuable data from natural experiments to tackle thorny questions. And in Wheelan’s trademark style, there’s not a dull page in sight. You’ll encounter clever Schlitz Beer marketers leveraging basic probability, an International Sausage Festival illuminating the tenets of the central limit theorem, and a head-scratching choice from the famous game show Let’s Make a Deal—and you’ll come away with insights each time. With the wit, accessibility, and sheer fun that turned Naked Economics into a bestseller, Wheelan defies the odds yet again by bringing another essential, formerly unglamorous discipline to life. |
| bias in data science: A Human's Guide to Machine Intelligence Kartik Hosanagar, 2020-03-10 A Wharton professor and tech entrepreneur examines how algorithms and artificial intelligence are starting to run every aspect of our lives, and how we can shape the way they impact us Through the technology embedded in almost every major tech platform and every web-enabled device, algorithms and the artificial intelligence that underlies them make a staggering number of everyday decisions for us, from what products we buy, to where we decide to eat, to how we consume our news, to whom we date, and how we find a job. We've even delegated life-and-death decisions to algorithms--decisions once made by doctors, pilots, and judges. In his new book, Kartik Hosanagar surveys the brave new world of algorithmic decision-making and reveals the potentially dangerous biases they can give rise to as they increasingly run our lives. He makes the compelling case that we need to arm ourselves with a better, deeper, more nuanced understanding of the phenomenon of algorithmic thinking. And he gives us a route in, pointing out that algorithms often think a lot like their creators--that is, like you and me. Hosanagar draws on his experiences designing algorithms professionally--as well as on history, computer science, and psychology--to explore how algorithms work and why they occasionally go rogue, what drives our trust in them, and the many ramifications of algorithmic decision-making. He examines episodes like Microsoft's chatbot Tay, which was designed to converse on social media like a teenage girl, but instead turned sexist and racist; the fatal accidents of self-driving cars; and even our own common, and often frustrating, experiences on services like Netflix and Amazon. A Human's Guide to Machine Intelligence is an entertaining and provocative look at one of the most important developments of our time and a practical user's guide to this first wave of practical artificial intelligence. |
| bias in data science: Encyclopedia of Organizational Knowledge, Administration, and Technology Khosrow-Pour D.B.A., Mehdi, 2020-09-29 For any organization to be successful, it must operate in such a manner that knowledge and information, human resources, and technology are continually taken into consideration and managed effectively. Business concepts are always present regardless of the field or industry – in education, government, healthcare, not-for-profit, engineering, hospitality/tourism, among others. Maintaining organizational awareness and a strategic frame of mind is critical to meeting goals, gaining competitive advantage, and ultimately ensuring sustainability. The Encyclopedia of Organizational Knowledge, Administration, and Technology is an inaugural five-volume publication that offers 193 completely new and previously unpublished articles authored by leading experts on the latest concepts, issues, challenges, innovations, and opportunities covering all aspects of modern organizations. Moreover, it is comprised of content that highlights major breakthroughs, discoveries, and authoritative research results as they pertain to all aspects of organizational growth and development including methodologies that can help companies thrive and analytical tools that assess an organization’s internal health and performance. Insights are offered in key topics such as organizational structure, strategic leadership, information technology management, and business analytics, among others. The knowledge compiled in this publication is designed for entrepreneurs, managers, executives, investors, economic analysts, computer engineers, software programmers, human resource departments, and other industry professionals seeking to understand the latest tools to emerge from this field and who are looking to incorporate them in their practice. Additionally, academicians, researchers, and students in fields that include but are not limited to business, management science, organizational development, entrepreneurship, sociology, corporate psychology, computer science, and information technology will benefit from the research compiled within this publication. |
| bias in data science: Noise Daniel Kahneman, Olivier Sibony, Cass R. Sunstein, 2021-05-18 From the Nobel Prize-winning author of Thinking, Fast and Slow and the coauthor of Nudge, a revolutionary exploration of why people make bad judgments and how to make better ones—a tour de force” (New York Times). Imagine that two doctors in the same city give different diagnoses to identical patients—or that two judges in the same courthouse give markedly different sentences to people who have committed the same crime. Suppose that different interviewers at the same firm make different decisions about indistinguishable job applicants—or that when a company is handling customer complaints, the resolution depends on who happens to answer the phone. Now imagine that the same doctor, the same judge, the same interviewer, or the same customer service agent makes different decisions depending on whether it is morning or afternoon, or Monday rather than Wednesday. These are examples of noise: variability in judgments that should be identical. In Noise, Daniel Kahneman, Olivier Sibony, and Cass R. Sunstein show the detrimental effects of noise in many fields, including medicine, law, economic forecasting, forensic science, bail, child protection, strategy, performance reviews, and personnel selection. Wherever there is judgment, there is noise. Yet, most of the time, individuals and organizations alike are unaware of it. They neglect noise. With a few simple remedies, people can reduce both noise and bias, and so make far better decisions. Packed with original ideas, and offering the same kinds of research-based insights that made Thinking, Fast and Slow and Nudge groundbreaking New York Times bestsellers, Noise explains how and why humans are so susceptible to noise in judgment—and what we can do about it. |
| bias in data science: Machine Learning Design Patterns Valliappa Lakshmanan, Sara Robinson, Michael Munn, 2020-10-15 The design patterns in this book capture best practices and solutions to recurring problems in machine learning. The authors, three Google engineers, catalog proven methods to help data scientists tackle common problems throughout the ML process. These design patterns codify the experience of hundreds of experts into straightforward, approachable advice. In this book, you will find detailed explanations of 30 patterns for data and problem representation, operationalization, repeatability, reproducibility, flexibility, explainability, and fairness. Each pattern includes a description of the problem, a variety of potential solutions, and recommendations for choosing the best technique for your situation. You'll learn how to: Identify and mitigate common challenges when training, evaluating, and deploying ML models Represent data for different ML model types, including embeddings, feature crosses, and more Choose the right model type for specific problems Build a robust training loop that uses checkpoints, distribution strategy, and hyperparameter tuning Deploy scalable ML systems that you can retrain and update to reflect new data Interpret model predictions for stakeholders and ensure models are treating users fairly |
| bias in data science: Cognitive Biases in Visualizations Geoffrey Ellis, 2018-09-27 This book brings together the latest research in this new and exciting area of visualization, looking at classifying and modelling cognitive biases, together with user studies which reveal their undesirable impact on human judgement, and demonstrating how visual analytic techniques can provide effective support for mitigating key biases. A comprehensive coverage of this very relevant topic is provided though this collection of extended papers from the successful DECISIVe workshop at IEEE VIS, together with an introduction to cognitive biases and an invited chapter from a leading expert in intelligence analysis. Cognitive Biases in Visualizations will be of interest to a wide audience from those studying cognitive biases to visualization designers and practitioners. It offers a choice of research frameworks, help with the design of user studies, and proposals for the effective measurement of biases. The impact of human visualization literacy, competence and human cognition on cognitive biases are also examined, as well as the notion of system-induced biases. The well referenced chapters provide an excellent starting point for gaining an awareness of the detrimental effect that some cognitive biases can have on users’ decision-making. Human behavior is complex and we are only just starting to unravel the processes involved and investigate ways in which the computer can assist, however the final section supports the prospect that visual analytics, in particular, can counter some of the more common cognitive errors, which have been proven to be so costly. |
| bias in data science: A Hands-On Introduction to Data Science Chirag Shah, 2020-04-02 An introductory textbook offering a low barrier entry to data science; the hands-on approach will appeal to students from a range of disciplines. |
| bias in data science: Bias in Science and Communication Matthew Brian Welsh, 2018 This book is intended as an introduction to a wide variety of biases affecting human cognition, with a specific focus on how they affect scientists and the communication of science. The role of this book is to lay out how these common biases affect the specific types of judgements, decisions and communications made by scientists. |
| bias in data science: The Ethical Algorithm Michael Kearns, Aaron Roth, 2020 Algorithms have made our lives more efficient and entertaining--but not without a significant cost. Can we design a better future, one in which societial gains brought about by technology are balanced with the rights of citizens? The Ethical Algorithm offers a set of principled solutions based on the emerging and exciting science of socially aware algorithm design. |
| bias in data science: Data Science for Librarians Yunfei Du, Hammad Rauf Khan, 2020-03-26 More data, more problems -- A new strand of librarianship -- Data creation and collection -- Data for the academic librarian -- Research data services and the library ecosystem -- Data sources -- Data curation (archiving/preservation) -- Data storage, management, and retrieval -- Data analysis and visualization -- Data ethics and policies -- Data for public libraries and special libraries -- Conclusion: library, information, and data science. |
| bias in data science: The Great Mental Models, Volume 1 Shane Parrish, Rhiannon Beaubien, 2024-10-15 Discover the essential thinking tools you’ve been missing with The Great Mental Models series by Shane Parrish, New York Times bestselling author and the mind behind the acclaimed Farnam Street blog and “The Knowledge Project” podcast. This first book in the series is your guide to learning the crucial thinking tools nobody ever taught you. Time and time again, great thinkers such as Charlie Munger and Warren Buffett have credited their success to mental models–representations of how something works that can scale onto other fields. Mastering a small number of mental models enables you to rapidly grasp new information, identify patterns others miss, and avoid the common mistakes that hold people back. The Great Mental Models: Volume 1, General Thinking Concepts shows you how making a few tiny changes in the way you think can deliver big results. Drawing on examples from history, business, art, and science, this book details nine of the most versatile, all-purpose mental models you can use right away to improve your decision making and productivity. This book will teach you how to: Avoid blind spots when looking at problems. Find non-obvious solutions. Anticipate and achieve desired outcomes. Play to your strengths, avoid your weaknesses, … and more. The Great Mental Models series demystifies once elusive concepts and illuminates rich knowledge that traditional education overlooks. This series is the most comprehensive and accessible guide on using mental models to better understand our world, solve problems, and gain an advantage. |
| bias in data science: The Demon-Haunted World Carl Sagan, 2011-07-06 A prescient warning of a future we now inhabit, where fake news stories and Internet conspiracy theories play to a disaffected American populace “A glorious book . . . A spirited defense of science . . . From the first page to the last, this book is a manifesto for clear thought.”—Los Angeles Times How can we make intelligent decisions about our increasingly technology-driven lives if we don’t understand the difference between the myths of pseudoscience and the testable hypotheses of science? Pulitzer Prize-winning author and distinguished astronomer Carl Sagan argues that scientific thinking is critical not only to the pursuit of truth but to the very well-being of our democratic institutions. Casting a wide net through history and culture, Sagan examines and authoritatively debunks such celebrated fallacies of the past as witchcraft, faith healing, demons, and UFOs. And yet, disturbingly, in today's so-called information age, pseudoscience is burgeoning with stories of alien abduction, channeling past lives, and communal hallucinations commanding growing attention and respect. As Sagan demonstrates with lucid eloquence, the siren song of unreason is not just a cultural wrong turn but a dangerous plunge into darkness that threatens our most basic freedoms. Praise for The Demon-Haunted World “Powerful . . . A stirring defense of informed rationality. . . Rich in surprising information and beautiful writing.”—The Washington Post Book World “Compelling.”—USA Today “A clear vision of what good science means and why it makes a difference. . . . A testimonial to the power of science and a warning of the dangers of unrestrained credulity.”—The Sciences “Passionate.”—San Francisco Examiner-Chronicle |
| bias in data science: Discriminating Data Wendy Hui Kyong Chun, 2021-11-02 How big data and machine learning encode discrimination and create agitated clusters of comforting rage. In Discriminating Data, Wendy Hui Kyong Chun reveals how polarization is a goal—not an error—within big data and machine learning. These methods, she argues, encode segregation, eugenics, and identity politics through their default assumptions and conditions. Correlation, which grounds big data’s predictive potential, stems from twentieth-century eugenic attempts to “breed” a better future. Recommender systems foster angry clusters of sameness through homophily. Users are “trained” to become authentically predictable via a politics and technology of recognition. Machine learning and data analytics thus seek to disrupt the future by making disruption impossible. Chun, who has a background in systems design engineering as well as media studies and cultural theory, explains that although machine learning algorithms may not officially include race as a category, they embed whiteness as a default. Facial recognition technology, for example, relies on the faces of Hollywood celebrities and university undergraduates—groups not famous for their diversity. Homophily emerged as a concept to describe white U.S. resident attitudes to living in biracial yet segregated public housing. Predictive policing technology deploys models trained on studies of predominantly underserved neighborhoods. Trained on selected and often discriminatory or dirty data, these algorithms are only validated if they mirror this data. How can we release ourselves from the vice-like grip of discriminatory data? Chun calls for alternative algorithms, defaults, and interdisciplinary coalitions in order to desegregate networks and foster a more democratic big data. |
| bias in data science: Cochrane Handbook for Systematic Reviews of Interventions Julian P. T. Higgins, Sally Green, 2008-11-24 Healthcare providers, consumers, researchers and policy makers are inundated with unmanageable amounts of information, including evidence from healthcare research. It has become impossible for all to have the time and resources to find, appraise and interpret this evidence and incorporate it into healthcare decisions. Cochrane Reviews respond to this challenge by identifying, appraising and synthesizing research-based evidence and presenting it in a standardized format, published in The Cochrane Library (www.thecochranelibrary.com). The Cochrane Handbook for Systematic Reviews of Interventions contains methodological guidance for the preparation and maintenance of Cochrane intervention reviews. Written in a clear and accessible format, it is the essential manual for all those preparing, maintaining and reading Cochrane reviews. Many of the principles and methods described here are appropriate for systematic reviews applied to other types of research and to systematic reviews of interventions undertaken by others. It is hoped therefore that this book will be invaluable to all those who want to understand the role of systematic reviews, critically appraise published reviews or perform reviews themselves. |
| bias in data science: All About Data Science Devi Prasad, 2023-11-30 Embark on a transformative journey into the world of data science with our groundbreaking book that demystifies the complexities of this dynamic field. Whether you're a novice eager to explore the foundations or a seasoned professional seeking advanced insights, 'Data Science Unveiled' is your comprehensive guide. Dive into the essentials of machine learning, unravel the power of predictive analytics, and master the art of data visualization. With hands-on examples and real-world applications, this book equips you with the skills to navigate the data landscape confidently. Uncover the secrets behind successful data-driven decision-making and propel your career forward. Join us on this enlightening exploration, where data is not just a tool but a key to unlocking a future shaped by insights. |
| bias in data science: Commentary on the Third Geneva Convention , 2021-09-09 The application and interpretation of the four Geneva Conventions of 1949 and their two Additional Protocols of 1977 have developed significantly in the seventy years since the International Committee of the Red Cross (ICRC) first published its Commentaries on these important humanitarian treaties. To promote a better understanding of, and respect for, this body of law, the ICRC commissioned a comprehensive update of its original Commentaries, of which this is the third volume. The Third Convention, relative to the treatment of prisoners of war and their protections, takes into account developments in the law and practice in the past seven decades to provide up-to-date interpretations of the Convention. The new Commentary has been reviewed by humanitarian law practitioners and academics from around the world. This new Commentary will be an essential tool for anyone involved with international humanitarian law. |
| bias in data science: Information Science and Applications (ICISA) 2016 Kuinam J. Kim, Nikolai Joukov, 2016-02-15 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. |
| bias in data science: Big Data Processing with Apache Spark Srini Penchikala, 2018-03-13 Apache Spark is a popular open-source big-data processing framework thatÕs built around speed, ease of use, and unified distributed computing architecture. Not only it supports developing applications in different languages like Java, Scala, Python, and R, itÕs also hundred times faster in memory and ten times faster even when running on disk compared to traditional data processing frameworks. Whether you are currently working on a big data project or interested in learning more about topics like machine learning, streaming data processing, and graph data analytics, this book is for you. You can learn about Apache Spark and develop Spark programs for various use cases in big data analytics using the code examples provided. This book covers all the libraries in Spark ecosystem: Spark Core, Spark SQL, Spark Streaming, Spark ML, and Spark GraphX. |
How to Reduce Bias in the Life Cycle of a Data Science Project
Specifically, after acknowledging potential structural biases, we identify and examine four key stages where bias is likely to emerge: (1) bias in the representation of data and the labeling …
bias in the data science Understanding and managing lifecycle
As a group, go through the steps of the data science life cycle, and see if you can identify where unethical or biased decisions were made. How could they have
BIASES IN DATA SCIENCE LIFECYCLE - arXiv.org
In this work we described different sources of biases in each stage of data science, provided some examples and gave references to the best practices.
FIELD GUIDE to Address Bias in Datasets Inspired by the …
threat of bias held in the offered data. Every data science practitioner should be well versed in the threat of data bias as well as armed with practical methods to identify bias. This field guide …
Thinking Clearly A Data Scientist’s Guide to Understanding …
cognitive biases in data science, consider implementing the following strategies: Be aware of your own biases: Recognize that you, too, are susceptible to cognitive biases, and actively work to …
Dealing with Bias and Fairness in AI/ML/Data Science Systems
1. Think about overall fairness and equity when building Data Science/ML systems 2. Go from social goals to fairness goals to ML fairness metrics 3. Audit bias and fairness of a decision …
FAIR2: A framework for addressing discrimination bias in …
- for identifying and addressing discrimination bias in social data science. We illustrate how FAIR2 enriches data science with experiential knowledge, clarifies assumptions about discrimination …
BIAS, TRANSPARENCY AND EXPLICABILITY. - UPC Universitat …
Explores the analysis of bias through its categoriza-tion in data, algorithms, and socio-technical factors, and examines its implications on structural and social issues from a perspective of …
Where Does Bias Hide? - National Association of Insurance …
Bias embedded in data elements due social policies and practices. There are over 180 cognitive biases than have been cataloged. Some that are most relevant for machine learning are: The …
Bias in Data-driven AI Systems - An Introductory Survey
In this paper, we survey recent technical approaches on bias and fairness in AI-based decision-making systems, we discuss their legal ground2as well as open challenges and directions …
Mitigation of Data Bias Through Fair Feature Selection Methods
During this thesis, we have developed, through several contributions, approaches and methods that make it possible to identify, correct biases and improve fairness in decision- making …
Representation Bias in Data: A Survey on Identification and …
bias in structured data based on factors such as objectives and capabilities. Following our taxonomy’s guidelines, we investigate each work’s details, explain its novelty, and discuss its …
Data, Power and Bias in Artificial Intelligence - Harvard …
discourse associated with each seems disparate. This paper reviews ongoing work to en-sure data justice, fairness and bias mitigation in AI systems from different domains exploring the …
Communicating Uncertainty and Cataloging Bias in Spatial …
First, we introduce a framework for teaching uncertainty, error, and bias in SDS that emphasizes three core competencies - (A) understanding uncertainty, error, and bias, (B) identifying …
The Bias-Variance Tradeoff: How Data Science Can Inform ... - ed
In this article, I show that the bias-variance tradeoff can be formally generalized to help explain the nature of these debates in the education literature. I first introduce the bias-variance tradeoff …
What is the purpose of and who is the intended Series
There are two general conditions that lead to data bias: 1) the dataset is not representative of the underlying population for which the prediction or algorithm will be used, and/or 2) the method …
Managing bias and unfairness in data for decision support: a …
We identify relevant research gaps and show which data management activities could be repurposed to handle biases and which ones might reinforce such biases. In the second part, …
arXiv:2009.09795v2 [cs.CY] 27 Oct 2020
Data bias is a systematic distortion in the data that compromises its representativeness. It is directly related to sampling that con rms whether the sample is representative of the larger …
Ethical considerations in data science: Balancing privacy and …
Examining federated learning's ethical implications exposed heightened concerns about algorithmic biases and transparency challenges, highlighting the urgency of addressing …
Addressing bias in big data and AI for health care: A call for …
We start with an overview of known sources and examples of bias in the medical field. We then focus on data bias, and outline the main open challenges that need to be addressed from an …
Mitigating Bias and Advocating for Data Sovereignty: The …
rithmic bias, data sovereignty, and regulatory compliance. This study explores the role of metadata and paradata as mecha-nisms for embedding ethical oversight into AI development. …
How to Reduce Bias in the Life Cycle of a Data Science …
Specifically, after acknowledging potential structural biases, we identify and examine four key stages where bias is likely to emerge: (1) bias in the representation of data and the labeling …
bias in the data science Understanding and managing lifecycle
As a group, go through the steps of the data science life cycle, and see if you can identify where unethical or biased decisions were made. How could they have
BIASES IN DATA SCIENCE LIFECYCLE - arXiv.org
In this work we described different sources of biases in each stage of data science, provided some examples and gave references to the best practices.
FIELD GUIDE to Address Bias in Datasets Inspired by the …
threat of bias held in the offered data. Every data science practitioner should be well versed in the threat of data bias as well as armed with practical methods to identify bias. This field guide …
Thinking Clearly A Data Scientist’s Guide to Understanding …
cognitive biases in data science, consider implementing the following strategies: Be aware of your own biases: Recognize that you, too, are susceptible to cognitive biases, and actively work to …
Dealing with Bias and Fairness in AI/ML/Data Science Systems
1. Think about overall fairness and equity when building Data Science/ML systems 2. Go from social goals to fairness goals to ML fairness metrics 3. Audit bias and fairness of a decision …
FAIR2: A framework for addressing discrimination bias in …
- for identifying and addressing discrimination bias in social data science. We illustrate how FAIR2 enriches data science with experiential knowledge, clarifies assumptions about discrimination …
BIAS, TRANSPARENCY AND EXPLICABILITY. - UPC …
Explores the analysis of bias through its categoriza-tion in data, algorithms, and socio-technical factors, and examines its implications on structural and social issues from a perspective of …
Where Does Bias Hide? - National Association of Insurance …
Bias embedded in data elements due social policies and practices. There are over 180 cognitive biases than have been cataloged. Some that are most relevant for machine learning are: The …
Bias in Data-driven AI Systems - An Introductory Survey
In this paper, we survey recent technical approaches on bias and fairness in AI-based decision-making systems, we discuss their legal ground2as well as open challenges and directions …
Mitigation of Data Bias Through Fair Feature Selection Methods
During this thesis, we have developed, through several contributions, approaches and methods that make it possible to identify, correct biases and improve fairness in decision- making …
Representation Bias in Data: A Survey on Identification and …
bias in structured data based on factors such as objectives and capabilities. Following our taxonomy’s guidelines, we investigate each work’s details, explain its novelty, and discuss its …
Data, Power and Bias in Artificial Intelligence - Harvard …
discourse associated with each seems disparate. This paper reviews ongoing work to en-sure data justice, fairness and bias mitigation in AI systems from different domains exploring the …
Communicating Uncertainty and Cataloging Bias in Spatial …
First, we introduce a framework for teaching uncertainty, error, and bias in SDS that emphasizes three core competencies - (A) understanding uncertainty, error, and bias, (B) identifying …
The Bias-Variance Tradeoff: How Data Science Can Inform …
In this article, I show that the bias-variance tradeoff can be formally generalized to help explain the nature of these debates in the education literature. I first introduce the bias-variance tradeoff …
What is the purpose of and who is the intended Series
There are two general conditions that lead to data bias: 1) the dataset is not representative of the underlying population for which the prediction or algorithm will be used, and/or 2) the method …
Managing bias and unfairness in data for decision support: a …
We identify relevant research gaps and show which data management activities could be repurposed to handle biases and which ones might reinforce such biases. In the second part, …
arXiv:2009.09795v2 [cs.CY] 27 Oct 2020
Data bias is a systematic distortion in the data that compromises its representativeness. It is directly related to sampling that con rms whether the sample is representative of the larger …
Ethical considerations in data science: Balancing privacy and …
Examining federated learning's ethical implications exposed heightened concerns about algorithmic biases and transparency challenges, highlighting the urgency of addressing …
Addressing bias in big data and AI for health care: A call for …
We start with an overview of known sources and examples of bias in the medical field. We then focus on data bias, and outline the main open challenges that need to be addressed from an …
Mitigating Bias and Advocating for Data Sovereignty: The …
rithmic bias, data sovereignty, and regulatory compliance. This study explores the role of metadata and paradata as mecha-nisms for embedding ethical oversight into AI development. …
