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Intersectional Automations
Intersectional Automations explores a range of situations where robotics, biotechnological enhancement, artificial intelligence (AI), and algorithmic culture collide with intersectional social justice issues such as race, class, gender, sexuality, ability, and citizenship. As robots, machine learning applications, and human augmentics are artifacts of human culture, they sometimes carry stereotypes, biases, exclusions, and other forms of privilege into their computational logics, platforms, and/or embodiments. The essays in this multidisciplinary collection consider how questions of equity and social justice impact our understanding of these developments, analyzing not only the artifacts themselves, but also the discourses and practices surrounding them, including societal understandings, design choices, law and policy approaches, and their uses and abuses.
Risk Modeling
A wide-ranging overview of the use of machine learning and AI techniques in financial risk management, including practical advice for implementation Risk Modeling: Practical Applications of Artificial Intelligence, Machine Learning, and Deep Learning introduces readers to the use of innovative AI technologies for forecasting and evaluating financial risks. Providing up-to-date coverage of the practical application of current modelling techniques in risk management, this real-world guide also explores new opportunities and challenges associated with implementing machine learning and artificial intelligence (AI) into the risk management process. Authors Terisa Roberts and Stephen Tonna provide...
AI Snake Oil
From two of TIME’s 100 Most Influential People in AI, what you need to know about AI—and how to defend yourself against bogus AI claims and products Confused about AI and worried about what it means for your future and the future of the world? You’re not alone. AI is everywhere—and few things are surrounded by so much hype, misinformation, and misunderstanding. In AI Snake Oil, computer scientists Arvind Narayanan and Sayash Kapoor cut through the confusion to give you an essential understanding of how AI works and why it often doesn’t, where it might be useful or harmful, and when you should suspect that companies are using AI hype to sell AI snake oil—products that don’t work...
The Dark Social
This book explores how people interact online through anonymous communication in encrypted, hidden, or otherwise obscured online spaces. Beyond the Dark Web itself, this book examines how the concept of ‘dark social’ broadens the possibilities for examining notions of darkness and sociality in the age of digitality and datafied life. The authors take into account technical, moral, ethical, and pragmatic responses to ourselves and communities seeking to be/belong in/of/ the dark. Scholarship on the Darknet and Dark Social Spaces tends to focus on the uses of encryption and other privacy-enhancing technologies to engender resistance acts. Such understandings of the dark social are naturall...
Cyber Security, Cryptology, and Machine Learning
This book constitutes the refereed proceedings of the 6th International Symposium on Cyber Security Cryptography and Machine Learning, CSCML 2022, held in Be'er Sheva, Israel, in June - July 2022. The 24 full and 11 short papers presented together with a keynote paper in this volume were carefully reviewed and selected from 53 submissions. They deal with the theory, design, analysis, implementation, or application of cyber security, cryptography and machine learning systems and networks, and conceptually innovative topics in these research areas.
Internet for the People
In Internet for the People, leading tech writer Ben Tarnoff offers an answer. The internet is broken, he argues, because it is owned by private firms and run for profit. Google annihilates your privacy and Facebook amplifies right-wing propaganda because it is profitable to do so. But the internet wasn't always like this-it had to be remade for the purposes of profit maximization, through a years-long process of privatization that turned a small research network into a powerhouse of global capitalism. Tarnoff tells the story of the privatization that made the modern internet, and which set in motion the crises that consume it today. The solution to those crises is straightforward: deprivatiz...
Algorithms for the People
How to put democracy at the heart of AI governance Artificial intelligence and machine learning are reshaping our world. Police forces use them to decide where to send police officers, judges to decide whom to release on bail, welfare agencies to decide which children are at risk of abuse, and Facebook and Google to rank content and distribute ads. In these spheres, and many others, powerful prediction tools are changing how decisions are made, narrowing opportunities for the exercise of judgment, empathy, and creativity. In Algorithms for the People, Josh Simons flips the narrative about how we govern these technologies. Instead of examining the impact of technology on democracy, he explore...
The Networked Leviathan
Governments and consumers expect internet platform companies to regulate their users to prevent fraud, stop misinformation, and avoid violence. Yet, so far, they've failed to do so. The inability of platforms like Facebook, Google, and Amazon to govern their users has led to stolen elections, refused vaccines, counterfeit N95s in a pandemic, and even genocide. Such failures stem from these companies' inability to manage the complexity of their userbases, products, and their own incentives under the eyes of internal and external constituencies. The Networked Leviathan argues that countries should adapt the institutional tools developed in political science to democratize the major platforms. Democratic institutions allow knowledgeable actors to freely share and apply their understanding of the problems they face while leaders more readily recruit third parties to help manage their decision-making capacity. This book is also available Open Access on Cambridge Core. For more information, visit https://networked-leviathan.com.
Differential Privacy
Over the last decade, differential privacy (DP) has emerged as the de facto standard privacy notion for research in privacy-preserving data analysis and publishing. The DP notion offers strong privacy guarantee and has been applied to many data analysis tasks. This Synthesis Lecture is the first of two volumes on differential privacy. This lecture differs from the existing books and surveys on differential privacy in that we take an approach balancing theory and practice. We focus on empirical accuracy performances of algorithms rather than asymptotic accuracy guarantees. At the same time, we try to explain why these algorithms have those empirical accuracy performances. We also take a balan...
