The Means of Prediction – How AI Really Works (and Who Benefits)
By: Maximilian Kasy – University of Oxford
[E]conomist 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.
Algorithmic Harm – Protecting People in the Age of Artificial Intelligence
By: Oren Bar-Gill, Cass R. Sunstein – Harvard University
Will algorithms help people or hurt them? What about artificial intelligence in general? If consumers know what they need to know and do not suffer from behavioral biases, algorithms and AI are likely to be helpful. Consumers will be more likely to get what they want and need. But if consumers lack information, algorithms in particular will be able to convince them to make harmful or foolish choices. And if consumers suffer from behavioral biases, such as unrealistic optimism or a focus on the short term, algorithms will be able to produce serious harms.
In Algorithmic Harm: Protecting People in the Age of Artificial Intelligence, Oren Bar-Gill and Cass Sunstein consider the harms and benefits of AI and algorithms and catalog the different ways in which algorithms are being or may be used in consumer and other markets. The authors identify the market conditions under which these uses injure consumers and consider policy and regulatory responses that could reduce the risks consumers, investors, workers, and voters face now—and in the future. Democracy and self-government are at risk; there is a great deal that can be done to reduce that risk.
Flash Teams – Leading the Future of AI-Enhanced, On-Demand Work
By: Melissa Valentine, Michael S. Bernstein – Stanford University
Gone are the days of static organizational charts and staffing based on the manager’s rolodex and intuition. Now you can recruit any expertise you need from a global online network within minutes: an on-demand, on-the-spot expert at the exact moment that you need their help. You can right-size their involvement, too; some of those experts give a second opinion or a moment of brainstorming, whereas others join as full-fledged team members for a sustained collaborative effort.
This is the future promised by flash teams, a model that The New York Times has already praised for its “revolutionary potential”: a world where experts are available anytime and everywhere, where remote work has become a norm, and where AI is in the loop to guide team decisions. In Flash Teams, award-winning management scholar Melissa Valentine and computer scientist Michael Bernstein chart the opportunities of flash teams and navigate the challenges that teams and managers will face. They distill lessons from their own work assembling and managing flash teams on demand that every manager can learn from so they can successfully use flash teams in their own organizations.
The Economics of Artificial Intelligence
By: Imad A. Moosa – RMIT University, Australia
This prescient book examines the implications of artificial intelligence (AI) for economic theory and policy. Using actual and simulated data to assess the costs and benefits of AI, Imad A. Moosa outlines its potential threats and recommends ways that mankind can deal with its ramifications.
Moosa discusses regulations and corrective policies, illustrating the technological foundations and evolution of AI. Chapters challenge the view that AI is benign, demonstrating the macroeconomic effects of its usage on wages and income distribution. Exploring the geopolitics of AI, Moosa identifies the existential threat it poses to neoliberal capitalism, arguing that more jobs will be lost as a result of AI than will be created.
