Media scholar ( and Internet Enthusiast ) David Shenk examines the troubling effects of information proliferation on our bodies, our brains, our relationships, and our culture, then offers strikingly down-to-earth insights for coping with the deluge. With a skillful mixture of personal essay, firsthand reportage, and sharp analysis, Shenk illustrates the central paradox of our time: as our world gets more complex, our responses to it become increasingly simplistic. He draws convincing links between data smog and stress distraction, indecision, cultural fragmentation, social vulgarity, and more. But there's hope for a saner, more meaningful future, as Shenk offers a wealth of novel prescriptions—both personal and societal—for dispelling data smog.

The author awoke from years of computer worship to the less welcome side-effects of information mania. Replacing his faith in the virtue of technology is a feeling that the raison d'etre of the industry is to hook the culture on the manic velocity of data and lose interest in slower deliberation.

A media scholar examines the effects of data overload on our lives, our relationships, and our culture, and offers strikingly down-to-earth insights for coping with our information infatuation.

Algorithms are everywhere, organizing the near-limitless data that exists in our world. Drawing on our every search, like, click, and purchase, algorithms determine the news we get, the ads we see, the information accessible to us, and even who our friends are. These complex configurations not only form knowledge and social relationships in the digital and physical world but also determine who we are and who we can be. Algorithms use our data to assign our gender, race, sexuality, and citizenship status. In this era of ubiquitous surveillance, contemporary data collection entails more than gathering information about us. Entities like Google, Facebook, and the NSA also decide what that information means, constructing our worlds and the identities we inhabit in the process. We have little control over who we algorithmically are. Through a series of entertaining and engaging examples, John Cheney-Lippold draws on the social constructions of identity to advance a new understanding of our algorithmic identities. We Are Data will educate and inspire readers who want to wrest back some freedom in our increasingly surveilled and algorithmically constructed world.

This book constitutes the refereed proceedings of the 4th International Conference on Recent Developments in Science, Engineering and Technology, REDSET 2017, held in Gurgaon, India, in October 2017. The 66 revised full papers presented were carefully reviewed and selected from 329 submissions. The papers are organized in topical sections on big data analysis, data centric programming, next generation computing, social and web analytics, security in data science analytics.

Data Democracy: At the Nexus of Artificial Intelligence, Software Development, and Knowledge Engineering provides a manifesto to data democracy. After reading the chapters of this book, you are informed and suitably warned! You are already part of the data republic, and you (and all of us) need to ensure that our data fall in the right hands. Everything you click, buy, swipe, try, sell, drive, or fly is a data point. But who owns the data? At this point, not you! You do not even have access to most of it. The next best empire of our planet is one who owns and controls the world’s best dataset. If you consume or create data, if you are a citizen of the data republic (willingly or grudgingly), and if you are interested in making a decision or finding the truth through data-driven analysis, this book is for you. A group of experts, academics, data science researchers, and industry practitioners gathered to write this manifesto about data democracy. The future of the data republic, life within a data democracy, and our digital freedoms An in-depth analysis of open science, open data, open source software, and their future challenges A comprehensive review of data democracy's implications within domains such as: healthcare, space exploration, earth sciences, business, and psychology The democratization of Artificial Intelligence (AI), and data issues such as: Bias, imbalance, context, and knowledge extraction A systematic review of AI methods applied to software engineering problems