In Praise of Computation
探讨了算法在预测中优于人类的原因，并通过实验发现人们对算法与人类决策者的偏好大致持平，且部分人了解算法后会改变偏好。
Abstract A great deal of work in behavioral science emphasizes that statistical predictions often outperform clinical predictions. Formulas tend to do better than people do, and algorithms tend to outperform human beings, including experts. One reason is that algorithms do not show inconsistency or “noise”; another reason is that they are often free from cognitive biases. These points have broad implications for risk assessment in domains that include health, safety, and the environment. Still, there is evidence that many people distrust algorithms and would prefer a human decisionmaker. We offer a set of preliminary findings about how a tested population chooses between a human being and an algorithm. In a simple choice between the two across diverse settings, people are about equally divided in their preference. We also find that that a significant number of people are willing to shift in favor of algorithms when they learn something about them, but also that a significant number of people are unmoved by the relevant information. These findings have implications for current findings about “algorithm aversion” and “algorithm appreciation.”