Professor Richard Hanley faced the dilemma plaguing so many philosophy professors today—how to entice students into the classroom. Based upon his own successful course, Is Data Human presents a thoroughly unique and enjoyable way of introducing students to the basic concepts of philosophy as seen through the lens of Star Trek. From the nature of a person, of minds, and of consciousness, to ethics and morality, to the nature and extent of knowledge and free will, Hanley brings a fresh perspective to the contemporary debates concerning humankind's place in the world.Dare to boldly go where no philosophy professor has gone before—a classroom packed with eager and enthusiastic students.
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.
“One of the most exciting developments from the world of ideas in decades, presented with panache by two frighteningly brilliant, endearingly unpretentious, and endlessly creative young scientists.” – Steven Pinker, author of The Better Angels of Our Nature Our society has gone from writing snippets of information by hand to generating a vast flood of 1s and 0s that record almost every aspect of our lives: who we know, what we do, where we go, what we buy, and who we love. This year, the world will generate 5 zettabytes of data. (That’s a five with twenty-one zeros after it.) Big data is revolutionizing the sciences, transforming the humanities, and renegotiating the boundary between industry and the ivory tower. What is emerging is a new way of understanding our world, our past, and possibly, our future. In Uncharted, Erez Aiden and Jean-Baptiste Michel tell the story of how they tapped into this sea of information to create a new kind of telescope: a tool that, instead of uncovering the motions of distant stars, charts trends in human history across the centuries. By teaming up with Google, they were able to analyze the text of millions of books. The result was a new field of research and a scientific tool, the Google Ngram Viewer, so groundbreaking that its public release made the front page of The New York Times, The Wall Street Journal, and The Boston Globe, and so addictive that Mother Jones called it “the greatest timewaster in the history of the internet.” Using this scope, Aiden and Michel—and millions of users worldwide—are beginning to see answers to a dizzying array of once intractable questions. How quickly does technology spread? Do we talk less about God today? When did people start “having sex” instead of “making love”? At what age do the most famous people become famous? How fast does grammar change? Which writers had their works most effectively censored by the Nazis? When did the spelling “donut” start replacing the venerable “doughnut”? Can we predict the future of human history? Who is better known—Bill Clinton or the rutabaga? All over the world, new scopes are popping up, using big data to quantify the human experience at the grandest scales possible. Yet dangers lurk in this ocean of 1s and 0s—threats to privacy and the specter of ubiquitous government surveillance. Aiden and Michel take readers on a voyage through these uncharted waters.
The authors invited more than 100 journalists worldwide to use photographs, charts and essays to explore the world of big data and its growing influence on our lives and society.
Data-driven personas are a significant advancement in the fields of human-centered informatics and human-computer interaction. Data-driven personas enhance user understanding by combining the empathy inherent with personas with the rationality inherent in analytics using computational methods. Via the employment of these computational methods, the data-driven persona method permits the use of large-scale user data, which is a novel advancement in persona creation. A common approach for increasing stakeholder engagement about audiences, customers, or users, persona creation remained relatively unchanged for several decades. However, the availability of digital user data, data science algorithms, and easy access to analytics platforms provide avenues and opportunities to enhance personas from often sketchy representations of user segments to precise, actionable, interactive decision-making tools—data-driven personas! Using the data-driven approach, the persona profile can serve as an interface to a fully functional analytics system that can present user representation at various levels of information granularity for more task-aligned user insights. We trace the techniques that have enabled the development of data-driven personas and then conceptually frame how one can leverage data-driven personas as tools for both empathizing with and understanding of users. Presenting a conceptual framework consisting of (a) persona benefits, (b) analytics benefits, and (c) decision-making outcomes, we illustrate applying this framework via practical use cases in areas of system design, digital marketing, and content creation to demonstrate the application of data-driven personas in practical applied situations. We then present an overview of a fully functional data-driven persona system as an example of multi-level information aggregation needed for decision making about users. We demonstrate that data-driven personas systems can provide critical, empathetic, and user understanding functionalities for anyone needing such insights.
Just about any social need is now met with an opportunity to "connect" through digital means. But this convenience is not free—it is purchased with vast amounts of personal data transferred through shadowy backchannels to corporations using it to generate profit. The Costs of Connection uncovers this process, this "data colonialism," and its designs for controlling our lives—our ways of knowing; our means of production; our political participation. Colonialism might seem like a thing of the past, but this book shows that the historic appropriation of land, bodies, and natural resources is mirrored today in this new era of pervasive datafication. Apps, platforms, and smart objects capture and translate our lives into data, and then extract information that is fed into capitalist enterprises and sold back to us. The authors argue that this development foreshadows the creation of a new social order emerging globally—and it must be challenged. Confronting the alarming degree of surveillance already tolerated, they offer a stirring call to decolonize the internet and emancipate our desire for connection.
As people use self-tracking devices and other digital technologies, they generate increasing quantities of personal information online. These data have many benefits, but they can also be accessed and exploited by third parties. In Data Selves, Deborah Lupton develops a fresh and intriguing perspective on how people make sense of and use their personal data, and what they know about others who use this information. Drawing on feminist new materialism theory and the anthropology of material culture, she acknowledges the importance of paying attention to practices, affects, sensory and other embodied experiences, as well as discourses, imaginaries and ideas in identifying the ways in which people make and enact data, and data make and enact people. Arguing that personal data are more-than-human phenomena, invested with diverse forms of vitalities, Lupton reveals significant implications for data futures, politics and ethics. Using rich examples from popular culture and empirical research, this book illustrates the power of data imaginaries, materializations and affects. Lupton's novel approach to understanding personal data will be of interest to students and scholars in media and cultural studies, sociology, anthropology, surveillance studies, and science and technology studies.
Machine learning applications perform better with human feedback. Keeping the right people in the loop improves the accuracy of models, reduces errors in data, lowers costs, and helps you ship models faster. Human-in-the-loop machine learning lays out methods for humans and machines to work together effectively. You'll find best practices on selecting sample data for human feedback, quality control for human annotations, and designing annotation interfaces. You'll learn to dreate training data for labeling, object detection, and semantic segmentation, sequence labeling, and more. The book starts with the basics and progresses to advanced techniques like transfer learning and self-supervision within annotation workflows.
From old-fashioned bricks-and-mortars to cutting-edge startups, businesses are moving into uncharted territory as they determine how to move from an analog past to a digital future effectively. How can you make sure not to leave human instinct behind? Businesses are leaving behind traditional meetings in favor of virtual ones, transitioning from surveys and studies to analytics and algorithms. The startling and often unacknowledged truth is that?the promise of digital transformation can only be realized when we find a way to balance it with the promise of people.?In the end, it’s the people that matter, and companies must never forget the soul that drives them. In Restoring the Soul of Business, business leader Rishad Tobaccowala?teaches you to: Understand how to unleash the significant benefit that can be realized by combining emotion and data, human and machine, analog and digital. Spot the warning signs of data-blinded companies: cold cultures with little human interaction, poor innovation stemming from discouraged employees who don’t contribute ideas, and poor customer service due to automated, robotic processes. Explore how organizations of various sizes and from different industries have successfully reoriented their thinking on how to fuse technology and humanity. Gain skills to become an expert in connections critical to growth and success, including the connection between being creative and using technology. Everyone working in an organization will find penetrating observations and guidance about how and why establishing the proper balance between human intuition and creativity and data-driven insights can lead to increased revenue, profitability, retention—and even joy—in their careers and business. Restoring the Soul of Business provides practical tools and techniques that every organization can and should implement, and challenges you to move forward with the kind of balance that capitalizes transformation and produces one great success after another.
Our world is being revolutionized by data-driven methods: access to large amounts of data has generated new insights and opened exciting new opportunities in commerce, science, and computing applications. Processing the enormous quantities of data necessary for these advances requires large clusters, making distributed computing paradigms more crucial than ever. MapReduce is a programming model for expressing distributed computations on massive datasets and an execution framework for large-scale data processing on clusters of commodity servers. The programming model provides an easy-to-understand abstraction for designing scalable algorithms, while the execution framework transparently handles many system-level details, ranging from scheduling to synchronization to fault tolerance. This book focuses on MapReduce algorithm design, with an emphasis on text processing algorithms common in natural language processing, information retrieval, and machine learning. We introduce the notion of MapReduce design patterns, which represent general reusable solutions to commonly occurring problems across a variety of problem domains. This book not only intends to help the reader "think in MapReduce", but also discusses limitations of the programming model as well. Table of Contents: Introduction / MapReduce Basics / MapReduce Algorithm Design / Inverted Indexing for Text Retrieval / Graph Algorithms / EM Algorithms for Text Processing / Closing Remarks
