Noise seems certain to make a mark by calling attention to the problem and providing a tangible guide to reducing it. Despite the authors’ intimidating academic credentials, they take pains to explain, even with welcome redundancy, their various categories of noise, the experiments and formulas that they introduce, as well as their conclusions and solutions ... The authors’ [...] argument, however, is that there is now so much noise that a major hygiene effort is in order across multiple disciplines. In too many arenas, they maintain persuasively, we’ve allowed too much noise at too high a cost ... Noise is about how our most important institutions can make decisions that are more fair, more accurate and more credible. That its prescriptions will not achieve perfect fairness and credibility, while creating pitfalls of their own, is no reason to turn away from this welcome handbook for making life’s lottery a lot more coherent.
Noise digs deep into the details of unwanted variation, including its causes and components, how to measure it, and the interplay between noise and bias ... they provide a well-stocked toolbox to help decision-makers identify and reduce system noise ... long and nuanced. The details and evidence will satisfy rigorous and demanding readers, as will the multiple viewpoints it offers on noise. I was distracted, however, by shifts in writing style at times. Some sentences and sections read like a psychology or statistics textbook, others like a scholarly article, and still others like the Harvard Business Review. But that is a minor complaint. Every academic, policymaker, leader and consultant ought to read this book. It convinced me that we already know how to turn down much of the systemic noise that plagues our organizations and governments.
... vague hand-waving over the serious societal implications of AI is all of a piece with a book that, while it undeniably has a point, and an important one, feels, to be blunt, half-baked. If ever there were a book in search of an editor, it is this one. Noise could have been half the length and it would have been a far better book for it. Instead, weighed down by flabby vignettes complete with imaginary (and terrible) dialogue that add nothing except pointless pages, it is a slog. This is disappointing given the authors’ previous output and it’s tempting to wonder the extent to which this study was a product less of an idea whose time had come than of a publisher’s desire for the next bestseller.
Noise, the book argues (as it must), is everywhere. This is meant to be startling, which is presumably why the authors avoid the standard language, so as not to simply be announcing that one of the two fundamental problems in measurement is everywhere. But the result is that their concept itself is scattered all around the outer rings of the target ... the book reads as a sort of natural history of a slightly alien world, where the boundaries between the surprising and the commonplace keep being mislocated ... Reading it all, one starts to fear that the world’s supply of counter-intuitive discoveries may be running thin—or that the surprising-ideas industry has settled into its own tropes and clichés ... What if the problem isn’t where the shots land on the target, but where the target is in the first place? It is true, as the authors lay out at length, that the American criminal-justice process sentences people to prison in arbitrary and inconsistent ways. But if everyone’s prison terms were reliably ranked according to their crimes, so that no one had a hungry judge throw the book at them, the country would still have the highest incarceration rate in the world ... Maybe the truly counter-intuitive project would be trying to make a better world, rather than optimizing the world we have.
In Noise, academics Daniel Kahneman, Cass Sunstein, and Olivier Sibony synthesize a large existing literature on human and algorithmic decision-making to do exactly that: they provide crisp measurements and examples of error, breaking them down into noise and bias. While bias has dominated headlines, with allegations about racial biases in the criminal justice system—accentuated by our determination to acknowledge and adjust for centuries of racial discrimination—the authors show why noise is typically a much bigger problem ... The authors make a convincing case that pattern noise is pervasive in human judgment, and usually much higher—noisier—than level noise ... Noise also presents psychological reasons for why noise arises. This is useful for considering how we might lower or eliminate it ... This book isn’t just for professionals. It should also change the way individuals evaluate their everyday decision-making and interactions ... Noise will change how we think about human decision-making, and how we decide to accommodate machines. The stakes are large, and the book timely.
... fascinating ... Though the writing can be jargon-heavy, readers will find plenty of insight and useful exercises. The result is dense and complex, but those who stay the course will be rewarded with an intricate examination of decision-making and sound judgment.
