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A revealing genealogy of image-recognition techniques and technologies
 

Today’s most advanced neural networks and sophisticated image-analysis methods come from 1950s and ’60s Cold War culture—and many biases and ways of understanding the world from that era persist along with them. Aerial surveillance and reconnaissance shaped all of the technologies that we now refer to as computer vision, including facial recognition. The Birth of Computer Vision uncovers these histories and finds connections between the algorithms, people, and politics at the core of automating perception today.

James E. Dobson reveals how new forms of computerized surveillance systems, high-tech policing, and automated decision-making systems have become entangled, functioning together as a new technological apparatus of social control. Tracing the development of a series of important computer-vision algorithms, he uncovers the ideas, worrisome military origins, and lingering goals reproduced within the code and the products based on it, examining how they became linked to one another and repurposed for domestic and commercial uses. Dobson includes analysis of the Shakey Project, which produced the first semi-autonomous robot, and the impact of student protest in the early 1970s at Stanford University, as well as recovering the computer vision–related aspects of Frank Rosenblatt’s Perceptron as the crucial link between machine learning and computer vision.

Motivated by the ongoing use of these major algorithms and methods, The Birth of Computer Vision chronicles the foundations of computer vision and artificial intelligence, its major transformations, and the questionable legacy of its origins.


Cover alt text: Two overlapping circles in cream and violet, with black background. Top is a printed circuit with camera eye; below a person at a 1977 computer.

A timely investigation of the potential economic effects, both realized and unrealized, of artificial intelligence within the United States healthcare system.

In sweeping conversations about the impact of artificial intelligence on many sectors of the economy, healthcare has received relatively little attention. Yet it seems unlikely that an industry that represents nearly one-fifth of the economy could escape the efficiency and cost-driven disruptions of AI.

The Economics of Artificial Intelligence: Health Care Challenges brings together contributions from health economists, physicians, philosophers, and scholars in law, public health, and machine learning to identify the primary barriers to entry of AI in the healthcare sector. Across original papers and in wide-ranging responses, the contributors analyze barriers of four types: incentives, management, data availability, and regulation. They also suggest that AI has the potential to improve outcomes and lower costs. Understanding both the benefits of and barriers to AI adoption is essential for designing policies that will affect the evolution of the healthcare system.

A thought-provoking examination of how AI might either spur or harm human economic progress.

What happens to an economy when machines can think as well as, or even better than, humans? The Economics of Transformative AI tackles this issue, which is one of the most consequential economic questions of our time. This book brings together sixteen research studies from top economists that look closely at how transformative AI reshapes everything from innovation and market structure to employment, inequality, and human purpose. They explore both opportunities, such as personalized algorithmic assistance, accelerated scientific discovery, and new forms of organization, and profound challenges, including potential labor displacement, rising concentration of power, changes in the information ecosystem, and even possible existential risks to humanity.

The studies in this volume develop economic frameworks for understanding the conditions under which AI might enhance or undermine human flourishing. They offer policymakers, researchers, and business leaders the analytical tools needed to prepare for the potential economic transformations ahead.

Are humans hard-wired to make good decisions about managing their privacy in an increasingly public world? Or are we helpless victims of surveillance through our use of invasive digital media? Exploring the chasm between the tyranny of surveillance and the ideal of privacy, this book traces the origins of personal data collection in digital technologies including artificial intelligence (AI) embedded in social network sites, search engines, mobile apps, the web, and email. The Future of Digital Surveillance argues against a technologically deterministic view—digital technologies by nature do not cause surveillance. Instead, the shaping of surveillance technologies is embedded in a complex set of individual psychology, institutional behaviors, and policy principles.

An eye-opening examination of how power—not technology—will define life with AI.

AI is inescapable, from its mundane uses online to its increasingly consequential decision-making in courtrooms, job interviews, and wars. The ubiquity of AI is so great that it might produce public resignation—a sense that the technology is our shared fate.
 
As economist Maximilian Kasy shows in The Means of Prediction, artificial intelligence, far from being an unstoppable force, is irrevocably shaped by human decisions—choices made to date by the ownership class that steers its development and deployment. Kasy shows that the technology of AI is ultimately not that complex. It is insidious, however, in its capacity to steer results to its owners’ wants and ends. Kasy clearly and accessibly explains the fundamental principles on which AI works, and, in doing so, reveals that the real conflict isn’t between humans and machines, but between those who control the machines and the rest of us.
 
The Means of Prediction offers a powerful vision of the future of AI: a future not shaped by technology, but by the technology’s owners. Amid a deluge of debates about technical details, new possibilities, and social problems, Kasy cuts to the core issue: Who controls AI’s objectives, and how is this control maintained? The answer lies in what he calls “the means of prediction,” or the essential resources required for building AI systems: data, computing power, expertise, and energy. As Kasy shows, in a world already defined by inequality, one of humanity’s most consequential technologies has been and will be steered by those already in power.
 
Against those stakes, Kasy offers an elegant framework both for understanding AI’s capabilities and for designing its public control. He makes a compelling case for democratic control over AI objectives as the answer to mounting concerns about AI's risks and harms. The Means of Prediction is a revelation, both an expert undressing of a technology that has masqueraded as more complicated and a compelling call for public oversight of this transformative technology.
 

An authoritative look at how artificial intelligence both shapes and is shaped by the political and economic forces of the modern world.

As the effects of artificial intelligence are felt across economies and societies, many of its ramifications are still emerging. This volume brings together economists and political scientists to examine how AI intersects with regulation, military power, and political identity—offering analytical frameworks and identifying key open questions for future research.

The contributions address topics such as the allocation of property rights for AI inputs, trade-offs among alternative regulatory regimes, and the role of interest groups in shaping the technology’s trajectory. They explore how AI-related capabilities influence military effectiveness, resource allocation, and bargaining power among nations, and consider AI’s effects on political preferences, from the influence of AI-curated information on polarization to the implications of targeted political advertising and personalized education for national identity formation. The volume highlights key trade-offs that arise in AI’s political economy, and points toward empirical strategies and theoretical models that can advance understanding in this emerging field.

Drawing on diverse disciplinary perspectives, the collection provides a foundation for rigorous inquiry into how AI both shapes and is shaped by political and economic forces.

A timely investigation of what is at stake when AI takes legal decision-making out of human hands.

Artificial intelligence is proliferating in many professions, and the legal field is no exception. In The Rule of Law After Artificial Intelligence, Katie Szilagyi investigates the philosophical and practical implications of using AI in legal spaces, beginning with several fundamental questions: What is the law supposed to do, and from where does it derive its authority? Would law still achieve these aims if automated? How might automation affect the rule of law’s integrity and democratic institutions’ operations?

Blending legal philosophy, applied case studies, and insights from both critical legal scholarship and science and technology studies, Szilagyi argues that law and storytelling are deeply connected. Through creating and contesting the law, we make sense of the information around us and generate narratives about our collective world. These narratives are not static: legal precedent evolves, and legal deliberation on hard cases can help to resolve unclear or unprecedented social issues.

Szilagyi demonstrates that technological innovations make the rule of law vulnerable because large language models and machine learning undermine the visioning function of legal narratives, collapsing exercises of legal interpretation into mere administration. Datafication of law—built on the biased data of our cultural past—threatens longstanding legal ideals, lessens the constraints against abuses of power by private actors, and hamstrings society’s ability to reach a more egalitarian future. Szilagyi argues instead for centering narratives within the law and, in turn, rediscovering the tales the law tells us about who we are.