Overcoming Algorithm Aversion: People Will Use Imperfect Algorithms If They Can (Even Slightly) Modify Them
Overcoming Algorithm Aversion: People Will Use Imperfect Algorithms If They Can (Even Slightly) Modify Them
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DOI：
10.1287/mnsc.2016.2643
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发表时间：
2018-03-01
影响因子：
5.4
通讯作者：
Massey, Cade
中科院分区：
文献类型：
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作者：
Dietvorst, Berkeley J.;Simmons, Joseph P.;Massey, Cade
尽管基于证据的算法始终优于人类预测者，但人们在得知它们不完美后往往无法使用它们，这种现象称为算法厌恶。在本文中，我们提出了三项研究，调查如何减少算法厌恶。在激励预测任务中，参与者选择使用自己的预测或专家构建的算法的预测。当参与者可以修改预测时，他们更有可能选择使用不完美的算法，并且因此表现得更好。值得注意的是，即使参与者可以进行的修改受到严格限制，对可修改算法的偏好仍然存在（研究 1-3）。事实上，我们的结果表明，参与者对可修改算法的偏好表明了对预测结果进行一定控制的愿望，而不是对预测结果有更大控制的愿望，因为参与者对可修改算法的偏好对他们能够做出的修改的程度相对不敏感（研究 2）。此外，我们发现，给予参与者修改不完美算法的自由会让他们对预测过程感到更满意，更有可能相信该算法是优越的，并且更有可能选择使用算法进行后续预测（研究3）。这项研究表明，人们可以通过给予人们对不完美算法的预测进行一定程度的控制（即使是轻微的控制）来减少对算法的厌恶。
Although evidence-based algorithms consistently outperform human forecasters, people often fail to use them after learning that they are imperfect, a phenomenon known as algorithm aversion. In this paper, we present three studies investigating how to reduce algorithm aversion. In incentivized forecasting tasks, participants chose between using their own forecasts or those of an algorithm that was built by experts. Participants were considerably more likely to choose to use an imperfect algorithm when they could modify its forecasts, and they performed better as a result. Notably, the preference for modifiable algorithms held even when participants were severely restricted in the modifications they could make (Studies 1-3). In fact, our results suggest that participants' preference for modifiable algorithms was indicative of a desire for some control over the forecasting outcome, and not for a desire for greater control over the forecasting outcome, as participants' preference for modifiable algorithms was relatively insensitive to the magnitude of the modifications they were able to make (Study 2). Additionally, we found that giving participants the freedom to modify an imperfect algorithm made them feel more satisfied with the forecasting process, more likely to believe that the algorithm was superior, and more likely to choose to use an algorithm to make subsequent forecasts (Study 3). This research suggests that one can reduce algorithm aversion by giving people some control-even a slight amount-over an imperfect algorithm's forecast.