NOISE: How to overcome the high, hidden cost of inconsistent decision making

By Daniel Kahneman, Andrew M. Rosenfield, Linnea Gandhi, Tom Blaser. 2016

184 citations recorded by the source corpus.

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Occam's razor is insufficient to infer the preferences of irrational agents (2017, background): “Though noise is important [Kahneman et al., 2016], many biases, such as anchoring bias, overconﬁdence, planning fallacies, and so on, affect humans in a highly systematic way; see Kahneman and Egan [2011] for many examples.”
Artificial intelligence in cardiology (2017, background): “In a recent article by Daniel Kahneman (who was awarded the Nobel Memorial Prize in Economic Sciences in 2002) et al. [1], most errors in judgment and decision-making have been attributed mainly to two phenomena, i. e. bias, such as for example stereotyping minorities (social bias) and noise, which…”
Towards a Standard for Identifying and Managing Bias in Artificial Intelligence (2022, background): “Delegating decision-making to algorithms is appealing because ML systems produce more consistent decisions compared to humans [149].”
Algorithmic management in a work context (2021, background): “Focusing primarily on the technology involved in algorithmic management, some perceive algorithms as “technocratic and dispassionate” decision makers that can provide more objective and consistent decisions than humans (Kahneman et al., 2016; Kleinberg et al., 2018).”