Groups of experts often differ in their decisions: What are the implications for AI and machine learning? A commentary onNoise: A Flaw in Human Judgment, byKahneman, Sibony, and Sunstein (2021)

Abstract

Abstract Machine Learning systems rely heavily on annotated instances. Such annotations are frequently done by human experts, or by tools developed by experts, and so the central message of this book,Noise: A Flaw in Human Judgment(Kahneman, Sibony, and Sunstein 2021) is of considerable importance to AI/Machine Learning community. The core message is that if a number of experts are asked to annotate tasks that involve judgments, these responses will frequently differ. This observation poses a problem for how analysts choose a particular annotated dataset (from the group), or process the set of responses to give a “balanced” response, or whether to reject all the annotated datasets. A further important aspect of this book is the case studies which demonstrate that differences in judgments between fellow experts have been reported in a significant number of disciplines including, business, the law, government, and medicine. Kahneman, Sibony and Sunstein (2021), referred to as KSS subsequently, discuss how Expert Biases can be reduced, but the main focus of this book is a discussion of Noise, that is, differences that often occur between fellow experts, and how Noise can often be reduced. To address the last point KSS have formulated a set of six decision hygiene principles which include the recommendation that complex tasks should be subdivided, and then each subtask should be solved separately. A further principle is that each task should be solved by individual experts before the various judgments are discussed with fellow experts. Effectively, the book being reviewed covers three main topics: First, it reports several motivating studies that show how judgments of fellow experts varied significantly in the pricing of insurance premiums, and in setting the lengths of custodial sentences. These motivating studies very effectively illustrate the central concepts of Judgment, Noise, and Bias; that section also provides definitions of these core concepts and discusses how Noise is often amplified in group meetings. Secondly, the authors provide detailed discussion of further studies, in a variety of domains, which report the levels of disagreement between experts. Thirdly, KSS discusses how to reduce the levels of Noise between experts, as noted above, the authors refer to these as Principles of Noise Hygiene. These three parts are interwoven in a complex way throughout the book; in our view, the best overview of the book is given in the section Review and Conclusions: Taking Noise Seriously (KSS, p. 361).

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