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We all want to make better decisions when we're hiring a new employee, choosing a marketing campaign, or allocating funds between different departments. For any job, it is important that you make good judgment. Teams and organizations become successful because of making good decisions, but unfortunately, as humans, we're all flawed. Why good leaders make bad decisions is a common occurence. We're pretty terrible at making predictions. We have a tendency to lean towards preconceived opinions that we have developed over time and end up making tons of mistakes. Even the greatest leaders fall into this predicament, and they also trip up from time to time. How do we eliminate bias decision-making? Why good leaders make bad decisions is caused by the tendency to swing in one direction as a single person or a group. Maybe it is always thinking a project will get done faster than it will or consistently underestimating costs, or hiring only people whose names sound familiar. We overlook another big problem, what we call noise, or random variability in the decisions made by different people, doctors, judges, managers. It's pervasive, and it affects all industries, but noise is also something that once we understand, we can come back. Organizations around the world are working to fix the bias in their decision-making in different ways. Why is it important and how is the noise different? Why good leaders make bad decisions is due to this noise. Well, biases become almost a synonym for error. That is when people see areas of judgment, they attribute them to biases. Suppose you have a number of people making a judgment about an object or a person, and on average, they are exactly right, but they vary. That variability is noise. The reason why good leaders make bad decisions is because of variability that shouldn't exist, its judgments should agree, but they don't. Where there are judgments, there is noise and more of it than you think. We are not just talking about differences between people, actually, sometimes the same person makes a different judgment in a very similar circumstance. You can actually test whether people given the same problem make the same judgment. In some domains, it's impossible to tell because people will recognize it. If you show a judgment of the same defendant, the judge sentenced yesterday; the judge will say, Well, I recognize this person, I sentenced him to five years yesterday, I'm going to sentence him to five years today. However, if you take people who cannot recognize what they have done before, like radiologists who see X-ray images that they've seen some time ago, or even forensic scientists who look at pairs of fingerprints that they have decided some time ago, there will be some variability in their judgment from one moment to the next. There are two kinds of noise; between people and within people. It has been discovered that the biggest source of variation is the different judgment personalities that people have. Let's move to the corporate world. Too much noise is bad for business because it will result in adverse consequences and explains why good leaders make bad decisions. What are some of these real-world consequences? Well, there are several types of consequences. The most apparent one is that they are going to make blunders. If you assume that there is a correct answer, and two people have a different answer, one of them has to be wrong. Frequently businesses think that because on average they are right in their decisions, those mistakes will cancel out. Actually, they don't. If you price one insurance policy too high and the other too low, on average, your pricing may be right, but the one that is priced too high is a customer you might not get, and the one you price too low is a risk that you are not charging the right price. One other key concern for many organizations is the credibility and the fairness of their decisions, especially when assessing the performance of their employees and deciding in absolute terms which employee deserves the best rating. That is an inherently subjective judgment for which you will never know what the Absolute Truth is. It is shocking if your rating and my ratings depend on the luck of the draw because one person will give you a top rating and the next person will give you an average rating that destroys the credibility of the process and, ultimately, the credibility of the organization. Noise is a problem in both predictive decisions like how much an insurance client is going to be worth and also evaluative decisions, determining whether an employee has performed well or not. Another example is an organization that hires people where half of the people that do the hiring favor men and half favor women on average. The organization will be unbiased, but it's going to make a lot of mistakes, it's going to hire the wrong people. That is entirely due to noise. You'll have one department populated entirely by women and another populated entirely by men, which really isn't productive. How do you recognize a noise problem as an individual or organization? This can be done through a noise audit procedure. The noise audit creates exercise and an artificial situation where, for example, in a courthouse, lots of different judges are going to look at a lot of different cases and give separate judgments about them. That gives a clean measure or estimate of how much difference there is that you normally don't see. That might tell you that it is fine, the difference is tolerable and well within what you expected, but much more likely, you are going to find that the noise is in fact much, much larger than you thought and that you need to do something about it. The first thing that will jump to a lot of people's minds is to just eliminate humans from the decision-making process and entirely move to AI. Is that where this is going? No. Although it is an important approach that is being used, and that will be used in more and more fields. More importantly, there are lots of decisions, lots of important decisions that do not lend themselves to that sort of automation, either because it's impractical, or more often because the people accountable for making those decisions do not want to abdicate those responsibilities to machines. For those reasons, we believe that it's important to improve the voice that teaches to educate human judgment to structure human judgment and not to hope to ever abolish it. Okay, so without resorting to algorithms, what are some things that we humans can do to reduce noise in our decisions? When people think in terms of biases, they are very naturally drawn to how we control this particular bias or that bias. This is more like vaccination or mitigation. When you're thinking of noise, you're basically looking for ways of producing a more uniform use of information.

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