![]() ![]() Nicolas Reitzaum/HEC Paris Olivier Sibonyįixing a bias is like curing a disease: You know what the disease is, you know what the symptoms are, and you’re pushing in the opposite direction. We can actually point our finger at it and say: “I was not hired because of the gender bias in the person who was evaluating me.” These biases do explain many errors in HR decisions. Can you explain it?īias is a great explanation for errors, it’s a great culprit. You make a clear distinction in your book between bias and noise. It’s a matter of credibility, it’s a matter of fairness, and it’s a matter of accuracy in your decisions. If you’re making noisy performance evaluations, you are not rewarding the better people and sending the right signals to the underperforming ones. If you’re making “noisy” hiring decisions, you are not hiring the best people. ![]() And because it’s a lottery, it creates a lot of errors. You don’t want your decisions to be the result of a lottery, and essentially “noise” is a lottery. ![]() Organizations should care about that, because they often rely on one person to make the decision-and if they rely on more than one person, they usually don’t have the right measures in place to make sure that they take advantage of that potential diversity. ![]()
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