Biases, according to the APA, are defined as partiality or an inclination or predisposition for or against something. Real-world experience and data collected by audit committees have shown how biases can stand in the way of necessary transformation, change, implementation, and execution — affecting the efficacy of leadership teams and their organizations.
But what if there was a way to reduce bias by reducing the “noise”?
In a recent interview, Daniel Kahneman, emeritus professor of psychology and public affairs at Princeton University, and Olivier Sibony, professor of strategy and business policy at HEC Paris, and co-authors of the book Noise: A Flaw in Human Judgement spoke with McKinsey researchers about the impact of system noise on organizational function and decision-making. The co-authors encountered “noise” in their own individual studies, and chose to write a book about people’s contention with high variability in inputs and cognitive processing that can affect their singular and collective judgments.
System noise is an unwanted variability with a system of judgments. Performance reviews can be a real-world business example of being particularly “noisy.”
“An organization will reduce bias as it reduces noise. One of the origins of bias is that people tend to jump to conclusions, and they reach those conclusions early, based on very little information,” says Kahneman. “They find information that confirms their existing opinions, and they look for information in a selective way. If you implement a decision-hygiene procedure, you break that pattern and prompt people to view the problem as separate subproblems that can be looked at factually, without intuition, or with a minimum of intuitive output and input. If decision hygiene procedures are followed, you have less room for biases in the independent judgment.”
Decision hygiene acts as a noise-preventative through specific procedures for reducing noise. For example, if you’re working on solving a problem, independently gather people’s judgments before aggregating them. Take competence into account, since some people will be better at judgments than others. Sardony also suggests using AI or algorithms to replace human judgment. “They will eliminate the noise. There will be no mood, no temperature, no difference between your judgments and my judgments. The machine will churn out the same judgments so long as the algorithm doesn’t change.”
If leaders want to enable organizational transformation, committing to reducing noise and therefore, bias, is a powerful step. Reducing both will lead to substantive progress in both digital and social initiatives.
All opinions & expressions are solely those of the author and not those of any other individual, institution or business.