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Cass R. Sunstein, "Imperfect Oracle: What AI Can and Cannot Do" (APS Press, 2025)

Sep 29, 2025

In this engaging discussion, Cass R. Sunstein, a Harvard law and public policy scholar, explores the intricacies of AI outlined in his book, Imperfect Oracle. He reveals how AI can help combat human biases and noise that lead to poor decision-making. Sunstein discusses the limits of AI predictions, the importance of quality training data, and how algorithms can aid in reducing discrimination in legal contexts. He also highlights the potential risks of over-reliance on AI and the need for thoughtful regulation to mitigate manipulation.

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INSIGHT

Practical Frame For AI

Cass Sunstein wrote Imperfect Oracle to cut through extreme claims about AI and focus on practical use and limits.
He argues AI can improve judgments but cannot eliminate fundamental limits on prediction.

INSIGHT

Human Heuristics Create Predictable Errors

Humans use heuristics that often produce predictable biases and errors in judgment.
Understanding these biases shows where AI can outperform humans by reducing systematic mistakes.

INSIGHT

Hayek's Limit On Prediction Still Holds

Hayek's argument about dispersed information limits applies to prediction even with powerful tools like AI.
Many outcomes remain unpredictable because we lack access to all relevant, dynamic variables.

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Imperfect Oracle is about the promise and limits of artificial intelligence. The promise is that in important ways AI is better than we are at making judgments. Its limits are evidenced by the fact that AI cannot always make accurate predictions--not today, not tomorrow, and not the day after, either.

Natural intelligence is a marvel, but human beings blunder because we are biased. We are biased in the sense that our judgments tend to go systematically wrong in predictable ways, like a scale that always shows people as heavier than they are, or like an archer who always misses the target to the right. Biases can lead us to buy products that do us no good or to make foolish investments. They can lead us to run unreasonable risks, and to refuse to run reasonable risks. They can shorten our lives. They can make us miserable.

Biases present one kind of problem; noise is another. People are noisy not in the sense that we are loud, though we might be, but in the sense that our judgments show unwanted variability. On Monday, we might make a very different judgment from the judgment we make on Friday. When we are sad, we might make a different judgment from the one we would make when we are happy. Bias and noise can produce exceedingly serious mistakes.

AI promises to avoid both bias and noise. For institutions that want to avoid mistakes it is now a great boon. AI will also help investors who want to make money and consumers who don't want to buy products that they will end up hating. Still, the world is full of surprises, and AI cannot spoil those surprises because some of the most important forms of knowledge involve an appreciation of what we cannot know and why we cannot know it. Written in clear, jargon-free English and grounded in deep understanding, Imperfect Oracle provides a distinctly useful perspective on this complex debate.

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