"The authors found that, while seeing a model make relatively small mistakes consistently decreased the participants’ confidence in the model, seeing a human make relatively large mistakes did not consistently decrease their confidence in the human.

In general, people are more likely to abandon an algorithm than a human judge for making the same mistake. We tolerate human errors at a higher rate compared to machine errors.

Scientific evidence on the superiority of algorithms and models to make forecasts in many aspects of life is plentiful and it goes back a long way (Meehl, 1954; Dawes, 1979; Silver, 2012)). However, people prefer humans over algorithms.

With our biases, we are precluding superior approaches in tackling many tasks, not just investing. This is a problem for society."

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