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.

But how is the technical structure of AI shaped by social power and inequalities? In this book talk and conversation, Max Kasy will discuss how who AI serves is determined not by the technology itself but by who it is built to serve. How can we shape AI to benefit all? 

Kasy’s new book, The Means of Prediction, reveals how 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. The technology of AI, Kasy insists, 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.

Kasy is joined in conversation by Rob Reich, the McGregor-Girand Professor of Social Ethics of Science and Technology at Stanford. Reich’s scholarship in political theory engages with the work of social scientists and engineers. His newest work is on governance of frontier science and technology. His most recent books are System Error: Where Big Tech Went Wrong and How We Can Reboot (with Mehran Sahami and Jeremy M. Weinstein, HarperCollins 2021) and Digital Technology and Democratic Theory (edited with Lucy Bernholz and Hélène Landemore, University of Chicago Press 2021). Reich has testified before Congress and written widely for the public, including for the New York Times, Washington Post, Wired, Time, The Atlantic, The Guardian, and the Stanford Social Innovation Review. 

Kasy is a professor of economics at Oxford University, where he coordinates the Machine Learning and Economics Group. His research interests include machine learning theory, publication bias, adaptive experimental design, statistical decision theory, identification and causality, and economic inequality. He has received grants from the Alfred P. Sloan foundation, served as an expert for the EU Directorate for Research and Innovation, and received the Young Economist Award from the Economic Association of Austria.