A wealth of empirical evidence shows that people display opposite behaviors when deciding whether to rely on an algorithm, even if it is inexpensive to do so and using the algorithm should enhance their own performance. This paper develops a formal theory to explain some of these conflicting facts and submit new testable predictions. Drawing from decision analysis, I invoke two key notions: the ‘value of information’ and the ‘value of control’. The value of information matters to users of algorithms like recommender systems and prediction machines, which essentially provide information. I find that ambiguity aversion or a subjective cost of employing an algorithm will tend to decrease the value of algorithmic information, while repeated exposure to an algorithm might not always increase this value. The value of control matters to users who may delegate decision making to an algorithm. I model how, under partial delegation, imperfect understanding of what the algorithm actually does (so the algorithm is in fact a black box) can cause algorithm aversion. Some possible remedies are formulated and discussed.

中文翻译：

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关于对算法的（非）依赖——决策理论的解释

大量的经验证据表明，人们在决定是否依赖某种算法时会表现出相反的行为，即使这样做的成本并不高，而且使用该算法应该会提高他们自己的表现。本文发展了一种正式的理论来解释其中一些相互矛盾的事实并提出新的可测试的预测。根据决策分析，我引用了两个关键概念：“信息的价值”和“控制的价值”。信息的价值对于推荐系统和预测机等算法的用户很重要，这些算法本质上是提供信息的。我发现歧义厌恶或使用算法的主观成本往往会降低算法信息的价值，而重复接触算法可能并不总是会增加这个价值。控制权的价值对于可以将决策委托给算法的用户来说很重要。我模拟了在部分委托下，对算法实际作用的不完全理解（因此该算法实际上是一个黑匣子）如何导致算法厌恶。制定并讨论了一些可能的补救措施。